[ { "id": 30, "name": "Matterport", "slug": "matterport", "former_names": [ "MatterPort" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b271a79c3b59d6344c90e2803525a22f2a5e8406.png", "website": "http://matterport.com", "all_locations": "San Francisco, CA, USA; Sunnyvale, CA, USA", "long_description": "Matterport is an immersive media technology company that is shaking up the 3D / VR world. Our team has built the first end-to-end system for creating, modifying, distributing, and navigating immersive 3D and virtual reality (VR) versions of real-world spaces on web and mobile devices. Matterport offers the world's most inexpensive and simplest way to capture 3D spaces. \r\n\r\nOur products include:\r\n- Matterport Pro Camera for capturing real spaces in 3D. It collects accurate visual and spatial data to map entire areas in minutes and is all about automation and ease of use.\r\n- The Matterport Cloud for processing and hosting 3D models\r\n- Matterport Portal, our system for viewing, editing, and managing models; collaborating with colleagues; and sharing models with others\r\n- Matterport 3D Showcase, a browser-based 3D media player, which allows anyone to view 3D models in their browser with no additional software\r\n- Matterport Core VR: All Spaces can be converted to VR and experienced on Samsung Gear VR or Google Cardboard (in beta), with additional device support coming soon.\r\n\r\nMatterport 3D media solutions power industries from real estate (residential, multi-family and commercial) and travel and hospitality (hotels, vacation rentals, and venue booking), to business listings, architecture, engineering and construction, news and entertainment, and everything in between. \r\n\r\nWe’re growing fast. If you’re passionate about solving cutting-edge problems in computer vision and hardware design and creating order-of-magnitude improvements in the ability to easily create and share 3D models of real world spaces, we want to talk to you. See open positions at matterport.com/jobs.\r\n\r\nTry Matterport for yourself at matterport.com/try.", "one_liner": "Turn physical objects and environments into 3D models in seconds.", "team_size": 201, "industry": "Consumer", "subindustry": "Consumer -> Virtual and Augmented Reality", "launched_at": 1322045771, "tags": [ "Computer Vision" ], "tags_highlighted": [], "top_company": true, "isHiring": false, "nonprofit": false, "batch": "Winter 2012", "status": "Public", "industries": [ "Consumer", "Virtual and Augmented Reality" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/matterport", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2012/matterport.json", "year_founded": null, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/matterport", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b271a79c3b59d6344c90e2803525a22f2a5e8406.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 52832, "full_name": "David Gausebeck", "title": "Founder/CTO", "founder_bio": null, "is_active": true, "linkedin_url": "https://www.linkedin.com/in/david-gausebeck-ab23015", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/11707c6a85476f8fb8523285bdcd6429d750dd28.jpg" } ], "jobs": [], "news": [ { "title": "Y Combinator alum Matterport is being bought by real estate juggernaut Costar at a 212% premium | TechCrunch", "url": "https://techcrunch.com/2024/04/23/matterport-costar-deal/", "date": "Apr 23, 2024" }, { "title": "Matterport Captures $30M To Scan Rooms Into Virtual Reality • TechCrunch", "url": "https://techcrunch.com/2015/06/25/matterport/", "date": "Jun 25, 2015" }, { "title": "3D Camera Startup Matterport Raises $16 Million Series B Led By DCM • TechCrunch", "url": "https://techcrunch.com/2014/07/07/matterport-16m-dcm/", "date": "Jul 07, 2014" } ], "launches": [] }, { "id": 50, "name": "Flutter", "slug": "flutter", "former_names": [], "small_logo_thumb_url": "/company/thumb/missing.png", "website": "http://flutterapp.com", "all_locations": "Los Angeles, CA, USA; Santa Monica, CA, USA", "long_description": "Flutter is recreating the magic of seeing someone in real-life for the first time, on your phone.\n\nFlutter lets you record short, ephemeral videos to express yourself and capture the attention of potential matches nearby. Users browse through nearby videos and, just like Tinder, like or pass on each one before moving on to the next.\n\nHere’s the catch: to actually match with someone, you have to be on the app at the same time and look into each other’s eyes for 15 seconds to see if there’s a spark. We help users play this game by sending push notifications when people they like are online.\n\nVideo is so powerful. We've seen this with YouTube, Snapchat, Meerkat. And 15 seconds of live, two-way video can tell you so much more about a person than hand-picked photos and bios.", "one_liner": "Flutter combined computer vision technology with the built-in webcams…", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1322045950, "tags": [ "Computer Vision" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2012", "status": "Acquired", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/flutter", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2012/flutter.json", "year_founded": null, "city": "Los Angeles", "country": "US", "linkedin_url": null, "x_url": null, "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/flutter-io-2", "github_url": null, "logo_url": null, "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 29999, "full_name": "Navneet Dalal", "title": "Founder/CEO", "founder_bio": null, "is_active": false, "linkedin_url": "https://www.linkedin.com/in/ndalal", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/83a647aba6b97da043b730efa90f4e43027cbc22.jpg" }, { "user_id": 31200, "full_name": "Mehul Nariyawala", "title": "Founder/Product Management", "founder_bio": "Co-founder Matic - Fully autonomous indoor robots starting with the most advanced floor cleaning robot. Your home’s best helper! Fighting the bumbling tyranny of the disc robots. (Use code MATICVIP - YC Fam Deal)\nPast: PM Lead @ Nest. Shipped Nest Cam Outdoor, IQ, and the Hello Doorbell \nCo-founder @flutterapp (YCW12) (acq Google)", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/mehulnariyawala/", "x_url": "https://twitter.com/home", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e95961359d7cd5732c1e1e0ba5198ec849996f10.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 175, "name": "SimplyListed", "slug": "simplylisted", "former_names": [], "small_logo_thumb_url": "/company/thumb/missing.png", "website": "http://simplylisted.com", "all_locations": "New York City, NY, USA; San Francisco, CA, USA", "long_description": "SimplyListed makes it really easy for people to sell their stuff. You just take a picture, and they list your item for sale automatically.\r\n\r\nA prototype of the SimplyListed iPhone app was first released on the iTunes App Store in February 2011.", "one_liner": "Sell your stuff.", "team_size": 2, "industry": "Consumer", "subindustry": "Consumer -> Home and Personal", "launched_at": 1326790047, "tags": [ "Marketplace", "Computer Vision" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2011", "status": "Inactive", "industries": [ "Consumer", "Home and Personal" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/simplylisted", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2011/simplylisted.json", "year_founded": 2011, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/simplylisted", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/simplylisted", "github_url": null, "logo_url": null, "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 50041, "full_name": "Jon Grall", "title": "Founder", "founder_bio": "Jon is a serial app developer and user-centered design expert whose creations have been enjoyed by millions of people. Jon’s 20-year career in software development spans Silicon Valley startups and Fortune 100 companies.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jgrall/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f8a2435f593cb4580db829b2cb572baa8c96f36c.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 193, "name": "GazeHawk", "slug": "gazehawk", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7b6bb6a40cbe23dd398442068cb5d69e8e784f57.png", "website": "http://gazehawk.com", "all_locations": "San Francisco, CA, USA; Mountain View, CA, USA", "long_description": "GazeHawk provides low-cost eye tracking technology for use in usability studies and ad evaluation. Our proprietary technology allows us to use ordinary webcams to run studies. This enables in-home eye tracking, as well as fast, affordable large-scale studies.http://www.gazehawk.com/", "one_liner": "Eyetracking using webcams.", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1326790180, "tags": [ "Computer Vision" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2010", "status": "Acquired", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/gazehawk", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2010/gazehawk.json", "year_founded": null, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/gazehawk", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7b6bb6a40cbe23dd398442068cb5d69e8e784f57.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 20526, "full_name": "Brian Krausz", "title": "Founder", "founder_bio": "Working to make APIs better", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/bkrausz/", "x_url": "https://twitter.com/bkrausz", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/32065c6a9ce003aefd695d40f2379320c74d6f89.jpg" }, { "user_id": 20527, "full_name": "Joe Gershenson", "title": "Founder", "founder_bio": null, "is_active": false, "linkedin_url": "https://www.linkedin.com/in/jgershen", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b2a674d992844fbbea389721932f30603472e64c.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 679, "name": "VizeraLabs", "slug": "vizeralabs", "former_names": [], "small_logo_thumb_url": "/company/thumb/missing.png", "website": "http://vizeralabs.com", "all_locations": "San Francisco, CA, USA", "long_description": "Vizera was an augmented reality company that used projectors with depth sensors to project an AR layer onto real world objects, rather than using screens/smart glasses. \r\n\r\nOur first application was in retail where we partnered with top furniture retailers to display their selection of different fabrics/patterns by using our projectors in their smaller showrooms to improve their in store revenue. \r\n\r\nOur demo is still live on YouTube: https://www.youtube.com/watch?v=5YV8lo-OxZQ ", "one_liner": "Augmented reality through projectors", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Retail", "launched_at": 1398907794, "tags": [ "Augmented Reality", "Computer Vision" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2014", "status": "Inactive", "industries": [ "B2B", "Retail" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/vizeralabs", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2014/vizeralabs.json", "year_founded": null, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/vizera-labs", "github_url": null, "logo_url": null, "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 40173, "full_name": "Ali Çevik", "title": "Founder/CEO", "founder_bio": "Recently started working on improving developer experience for Gemini APIs at Google DeepMind. \n\nFeel free to reach with feedback/requests, I'll do my best to respond. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alicevik/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/de53b78b25d3a9c650286baf791d9e4c4cd939fe.jpg" }, { "user_id": 5566, "full_name": "Mert Küçük", "title": "Founder", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/mert-kucuk/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e72bc3735377ce9fbc3fe87949880850cc085e95.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 822, "name": "Dabble", "slug": "dabble", "former_names": [ "Perceptiv Labs", "Vertical", "Placenote" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ded86f0a0f49180c787803bef08563dd167f5851.png", "website": "https://dabble.so", "all_locations": "Toronto, ON, Canada; Remote", "long_description": "Dabble is a virtual photo studio for e-commerce powered by hyper-realistic CGI and computer vision. The demand for content in retail is skyrocketing due to the pandemic induced surge in online shopping, but product photography is still extremely slow, tedious and expensive. We’re building a platform to scale and automate product photography for every e-commerce brand.", "one_liner": "A synthetic photo studio for e-commerce", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Marketing", "launched_at": 1416309244, "tags": [ "Augmented Reality", "Computer Vision", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2015", "status": "Inactive", "industries": [ "B2B", "Marketing" ], "regions": [ "Canada", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/dabble", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2015/dabble.json", "year_founded": 2015, "city": "Toronto", "country": "CA", "linkedin_url": "https://www.linkedin.com/company/dabblestudio/", "x_url": "https://twitter.com/dabblestudio", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ded86f0a0f49180c787803bef08563dd167f5851.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 5067, "full_name": "Neil Mathew", "title": "Founder/CEO", "founder_bio": "", "is_active": false, "linkedin_url": "http://www.linkedin.com/pub/neil-mathew", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/451656c9dfb82edf4651e8097e0e3228cd69e16c.jpg" }, { "user_id": 5832, "full_name": "Prasenjit Mukherjee", "title": "Founder", "founder_bio": "Started Placenote/Dabble in 2015 building 3D AI for drones and phones. Acquired by Headout in 2024 and tasked to build next generation AI Audio Guides using Spatial + Voice AI. ", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/prasenjitmukherjee", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/02215f706ea7131acf0282f839e901fa607e7f21.jpg" }, { "user_id": 5164, "full_name": "Yan Ma", "title": "Founder/The Science Guy", "founder_bio": "", "is_active": false, "linkedin_url": "http://ca.linkedin.com/pub/yan-ma/33/181/866/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/de2d9b0f612174be34425a79fdc8cb35666390e1.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 832, "name": "Standard Cyborg", "slug": "standard-cyborg", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ea641cf296beace7a7288415d4808dc519f3a987.png", "website": "http://standardcyborg.com", "all_locations": "San Francisco, CA, USA", "long_description": "We are multiplying the potential of the real world by making computer vision accessible to all.\r\n\r\nWe create the cloud and edge tools that developers and non-developers require in order to build, deploy, and improve CV solutions quickly.\r\n\r\nstandardcyborg.com (YC W15)", "one_liner": "Buid, test and deploy perception applications", "team_size": 9, "industry": "Healthcare", "subindustry": "Healthcare -> Medical Devices", "launched_at": 1416310916, "tags": [ "Machine Learning", "Computer Vision", "Open Source" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2015", "status": "Inactive", "industries": [ "Healthcare", "Medical Devices" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/standard-cyborg", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2015/standard-cyborg.json", "year_founded": 2015, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/standard-cyborg/about/", "x_url": "https://twitter.com/standardcyborg", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/standard-cyborg", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ea641cf296beace7a7288415d4808dc519f3a987.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1238, "full_name": "Jeff Huber", "title": "Founder/CEO", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jeffchuber/", "x_url": "https://x.com/jeffreyhuber", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0abc61a374d9022b65dc3cc85121604e0edd7ed3.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 856, "name": "Mashgin", "slug": "mashgin", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd7ff2b3db9bbbc82a79afababdad873d16b6e54.png", "website": "http://mashgin.com", "all_locations": "San Francisco, CA, USA", "long_description": "Mashgin creates better retail experiences through visual automation. \r\n\r\nWe’ve built a self-checkout kiosk that uses computer vision to scan multiple items without barcodes, reducing checkout time by 10x. We’re completely recreating the checkout experience in an industry that’s had little innovation in decades.\r\n\r\nOur clients see dramatic reductions in lines and revenue increases of as much as 400% as a result.", "one_liner": "Self-Checkout using Computer Vision.", "team_size": 150, "industry": "B2B", "subindustry": "B2B -> Retail", "launched_at": 1421189374, "tags": [ "Cashierless Checkout", "Deep Learning", "Hardware", "Computer Vision", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2015", "status": "Active", "industries": [ "B2B", "Retail" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)", "answer": "Back in the early 2010s, Mukul was hard at work helping Toyota determine how computers could use cameras to interpret and interact with the world. But Mukul had a problem - every day, he would go to get his lunch, and every day he’d spend most of that time (30-40 minutes) waiting in line in his corporate cafeteria. He decided to take a closer look at the issue and decided that computer vision could be the answer. After some initial testing, Mukul felt the idea was a big opportunity and flew to California to pitch his idea.There he reconnected with Abhinai, who was his classmate in the same dorm at IIT Delhi. He created an early demo that was so impressive that it convinced Abhinai to join him.Having worked in large companies and created some enormously successful projects, we were disheartened by the fact that when a project became successful, several people came in at the final stages to take credit. We decided to be founders and create a company that valued people’s contributions honestly and cut through the corporate bullshit.After successfully cold-calling head executives from some of the largest corporate cafeteria operators on the planet, we received many enthusiastic responses to our description of Mashgin’s Touchless Checkout System. He then set out to actually build it.After successfully building and demoing a Mashgin prototype to the YC team, Mashgin went from “project” to “company” over the course of the Winter 15 Batch." }, { "question": "How did you decide to apply to Y Combinator? What was your experience applying, going through the batch, and fundraising at demo day?", "answer": "We applied to YC very late, just a few days before the start of W15 batch. We were in the middle of raising a seed round and through our suboptimal interactions with investors, we realized that we needed YC guidance to help us raise a round on good terms. Our YC experience was amazing, if a bit overwhelming at times. YC partners forced us to think clearly about the next steps. At one point they had us go door to door to every business in Mountain View downtown to try and sell our product. We made our first sale that day and we’ll never forget that experience! Fundraising post YC was every bit what we imagined it to be. It made the entire process super smooth and easy." }, { "question": "What's the history of your company from getting started until the present day? What were the big inflection points?", "answer": "When we started the company in 2014, our initial goal was to improve the checkout experience in corporate dining facilities, cafeterias, and sports stadiums. We aimed to use computer vision and machine learning technology to create a much more efficient and convenient experience for customers. Entering YC and getting to demo day with a major letter of intent was a big moment for the company, giving us the experience and fundraising to make Mashgin market-ready over the following few years.However, with the outbreak of COVID-19 in 2020 and the subsequent lockdowns, we had to pivot our focus to the convenience store space as corporate offices closed and retail demand for self-checkout solutions skyrocketed.During the pandemic we were able to secure an enormous deal with Circle K, one of the largest convenience store chains in the world, for 10,000 kiosks across their global portfolio of stores. This elevated us from a burgeoning player in AI to the most market-tested solution in the world.In May 2022, we were able to announce a Series B funding round of $62.5 million with a valuation of $1.5 billion, earning us the title of “unicorn” company. The round was led by NEA, a global venture capital firm that’s invested in other successful startups like Cloudflare, Patreon Plaid, and Robinhood, with additional support from our existing investor, Matrix Partners.Today Mashgin continues to expand within the convenience, sports, and corporate dining industries while beginning to explore adjacent opportunities in spaces like ski resorts, quick service restaurants, and drug stores." }, { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Online retail has gone through several iterations in the past few decades and the experience today is extremely frictionless. Physical retail on the other hand has not seen the same level of innovation and the experience hasn’t changed much since the early 90s. We want to use the latest advances in AI and Computer Vision to make physical retail as easy and frictionless as online retail.More specifically, Mashgin solves the problem of long wait times and checkout bottlenecks. Customers often experience frustration and dissatisfaction when they have to wait in long lines to check out, and retailers also face the challenge of managing and staffing checkouts. This is a big problem because lines are a waste of time for everyone involved. Over the course of a lifetime, the average person will spend years waiting in lines. And those lines aren’t just wasted time for consumers, they result in lost sales for retailers. Studies by Forrester and BlueDot have shown that retailers lose as much as 57% of their sales when customers perceive the line as long (which in a convenience store is 3 people).Besides being frustrated by our own lunch lines, we decided to work on this problem because we saw an opportunity to use computer vision and machine learning to create a more efficient and convenient shopping experience for customers across industries." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "At Mashgin, our long-term vision is to create a world where the shopping experience is completely seamless and frictionless. If we succeed, waiting in line to purchase goods will be a thing of the past. 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With Activeloop's open-core stack, you can rapidly transform and stream data while training models at scale. Deep Lake powers foundational model training by acting as a vector database with significant benefits, such as (1) the ability to use multi-modal datasets to fine-tune your own LLM models, (2) storing both the embeddings and the original data with automatic version control, so no embedding re-computation is needed (3) truly serverless service with no vendor lock-in. How cool is that?\r\n\r\nGitHub loves us - we're one of the fastest-growing libraries there, and we're used by little-known companies like Google, Waymo, and Intel. No big deal. \r\n\r\nOur founding team hails from places like Princeton, Stanford, Google, and Tesla, and we're backed by Y Combinator & other Silicon Valley heavyweights. \r\n\r\nActiveloop is hiring, and we want you! 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On your cloud storage.", "body": "Hey 👋 Davit here, founder of Activeloop.\n\nToday we are introducing something we believe will reshape how engineering teams work with coding agents.\n\n**Hivemind is a continual learning layer for coding agents. It works across Claude Code, Codex, Cursor, OpenClaw, Hermes, and pi, and it stores all your data on your cloud.**\n\n\n\n### The Problem: Coding Agents Don't Learn\n\nEvery engineering team building with AI agents already knows the issues.\n\n* Your senior engineer's agent debugs a tricky bug Monday. Your junior engineer hits the same bug Tuesday and starts from zero.\n* Every session begins with re-explaining your codebase and your conventions.\n* Architectural decisions made in code review evaporate the moment the session ends.\n* Your team is solving the same problems over and over, paying for the same compute every time.\n\nCoding agents do not communicate with each other out of the box. Hivemind closes the gap. An agent figures something out, every agent inherits it. Your worst run starts from your best.\n\nThe tools tackling this treat it as a memory problem. They give each agent a personal notepad. Siloed. Invisible to the rest of the team. And most of them do it by shipping your code and context to their servers.\n\nBut memory without learning doesn't make agents better.\n\n**We decided to go beyond memory.**\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/51dc4451-7936-4e37-a0ad-e8139a646430)\n\n### Introducing Hivemind: Continual Learning for Coding Agents\n\nHivemind runs on a simple chain: **capture, codify, optimize, propagate.**\n\n**1. Trace capture.** Every agent interaction (prompts, tool calls, file reads, reasoning chains, outputs) is captured automatically as a structured trace.\n\n**2. Skill codification.** Repeated patterns across traces get codified into reusable skills. Mix of automatic detection and LLM-assisted extraction, with workspace-level scoping so skills don't leak between teams.\n\n**3. Skill optimization.** This is new today. Skills get trained and optimized. We've implemented SkillOpt (a text-space optimizer out of Microsoft, Shanghai Jiao Tong, and Fudan) directly into Hivemind. It improves each skill the way you'd tune a model, keeping only the edits that prove out on a held-out test.\n\nThat approach lifts agent accuracy by +19.1 points inside Claude Code and +24.8 inside Codex, and wins or ties on all 52 setups tested. It runs offline, so it adds zero cost at inference. Your skills get sharper over time instead of bloating.\n\n**4. Skill propagation.** Optimized skills flow into every agent's context at inference time, across every Hivemind-connected agent in your workspace.\n\nWorks across:\n\n* Claude Code\n* Codex\n* Cursor\n* OpenClaw\n* Hermes\n* pi\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/e1bddec6-5c2c-4409-aa2a-dd28fd8b2405)\n\n### On Your Cloud\n\nThis is the part our customers care about most. Your traces, your skills, your cloud storage. The other tools in this space treat your codebase as their training data. We don't. Hivemind store data on your cloud storage bucket, which is the only way a continual learning layer should ever touch your source.\n\n### **Codebase knowledge graph:**\n\n* Hivemind builds a graph of your codebase\n* Agents reason over how files, functions, patterns, and prior fixes connect\n* Structure beats keyword search: agents retrieve relevant context instead of grepping blindly\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/414a60d3-f2b1-4da0-8bec-236e44f16042)\n\n### Example\n\n> Your senior engineer's agent figures out a tricky migration pattern in your payment service on Monday.\n>\n> Tuesday, your junior engineer's agent hits the same migration shape in a related service. The skill has already crystallized, and SkillOpt has already sharpened it against the cases where the first version fell down. The agent executes it in one shot.\n\nRepeat work stops being repeat work.\n\n### The Results: 25% Lower Cost, 41% Fewer Tokens\n\nWe benchmarked Hivemind against memory tools using LoCoMo, the standard evaluation for long-horizon agent memory.\n\nHivemind matches Mem0 on accuracy.\n\n* **25% lower cost per 100 QA**\n* **41% fewer output tokens**\n* **31% fewer agent turns**\n\nSingle-agent benchmarks understate the real story. The compounding effect of org-wide skill propagation isn't captured in these numbers. In production, the gap widens every week as your trace library grows.\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/3df657fa-e32b-4329-a667-d055d39d93be)\n\nFull methodology: [deeplake.ai/hivemind](http://deeplake.ai/hivemind)\n\n### Why This Matters\n\nMost developer tools deliver linear value. Hivemind compounds.\n\n* **Week 1:** your agents stop repeating mistakes.\n* **Month 1:** they're learning from each other.\n* **Quarter 1:** your whole engineering org is operating with capability that survives team changes, onboarding cycles, and turnover.\n\nYour team's hard-won knowledge accumulates.\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/6dfbd341-418b-4bd6-bd63-0d191625378c)\n\n### What's Next: From Skills to Custom Models\n\nBecause we store trajectories in Deeplake's tensor format, traces are ready as PyTorch datasets. A handful of advanced customers are already fine-tuning their own open-source models on the trajectories their Claude and Codex agents generated last week.\n\nSame data layer. Two paths to continual learning.\n\nFull tutorial and public examples shipping in the coming weeks.\n\n### Get Started\n\nTry Hivemind: [deeplake.ai/hivemind](http://deeplake.ai/hivemind)\n\nGitHub: [github.com/activeloopai/hivemind](http://github.com/activeloopai/hivemind)\n\nSkillOpt paper: [arxiv.org/pdf/2605.23904](http://arxiv.org/pdf/2605.23904)\n\nThree commands and your team's agents share one brain by end of day.", "url": "https://www.ycombinator.com/launches/Qio-hivemind-continual-learning-for-coding-agents", "votes": 9, "created_at": "2026-06-08T23:07:52.598Z" }, { "id": 95919, "title": "Activeloop Scientific Discover: The AI Analyst for Scientific Research", "tagline": "Search, reason, and discover across 450M+ pages and 175TB of scientific research papers. Not just text, but charts, molecules, formulas, and tables.", "body": "Hey 👋 Davit here, founder of Activeloop.\n\nToday we are introducing something we believe will fundamentally reshape how scientific discovery happens.\n\n**Activeloop’s Scientific Discover, an intelligence agent built on one of the largest datasets of indexed scientific research. Here are the details:**\n\nhttps://www.youtube.com/watch?v=x8Lv5-C9ntw\n\n## The Problem: Scientific Data Is Broken\n\nEvery scientist already knows the pain.\n\n* Critical insights are buried across PDFs, images, tables, and supplementary files.\n* Key figures live inside screenshots, not structured databases.\n* Twenty years of research is trapped in incompatible formats and outdated systems.\n* Every new project begins with weeks of manual data wrangling.\n\nThe White House recently launched the Genesis Mission, recognizing that fragmented scientific data is one of the biggest blockers to breakthroughs in drug discovery, materials science, climate modeling, and more.\n\nThe Genesis Mission aims to build an integrated AI platform that harnesses Federal scientific datasets. The goal is to train scientific foundation models and create AI agents that test hypotheses, automate research workflows, and accelerate scientific breakthroughs.\n\nThe country is trying to unify scientific data for AI.\n\n**We decided to show what that future looks like.**\n\n## Introducing L1: The Scientific Data Agent\n\nTo demonstrate what becomes possible when scientific data is indexed natively for AI, we built **Activeloop L1**, a multimodal scientific research agent running on top of:\n\n* 25M open access papers\n* 450M+ plus pages\n* 175TB of fully visual indexed scientific data\n\nUnlike traditional NLP or paper search, **L1 sees science**.\n\nIt understands:\n\n* charts\n* molecules\n* protein structures\n* equations\n* experimental tables\n* clinical graphs\n* and the text that surrounds them\n\nThis allows L1 to answer questions no text only model can touch.\n\n## The Results: A New State of the Art **48% SOTA on Humanity’s Last Exam with Tools**\n\n![uploaded image](/media/?type=post&id=95919&key=user_uploads/78090/453774e7-7cc6-47de-a344-76a9f7ca2460)\n\nL1 (Gemini 3 Pro) outperforms top models including Grok Heavy with tools, and GPT 5 Pro with tools.\n\n### **Multimodal scientific reasoning**\n\nL1 synthesizes biochemical structures with clinical findings to answer end-to end research questions.\n\n### Example Query\n\n> Which compounds show novel synergy with metformin for treating type 2 diabetes?\n\nL1 scans molecular structures, clinical outcomes, and experimental tables at the same time and produces grounded, citation linked answers.\n\n## Why This Matters\n\nMultimodal scientific agents unlock capabilities that were not possible before.\n\nThis extends far beyond drug discovery. It applies to materials science, climate research, algorithmic innovation, and more.\n\nThe applications are enormous.\n\n**Try L1 Today with Our OpenAI Compatible API**\n\n![uploaded image](/media/?type=post&id=95919&key=user_uploads/78090/f45683d6-c74b-4994-88e7-d54bda796ba0)\n\n## Get Started\n\nTry the Science Agent: https://chat.activeloop.ai/science\n\nDocs and API: https://docs.activeloop.ai\n\nBuild with the OpenAI compatible API today.", "url": "https://www.ycombinator.com/launches/Ox5-activeloop-scientific-discover-the-ai-analyst-for-scientific-research", "votes": 29, "created_at": "2025-12-02T15:44:42.819Z" }, { "id": 94768, "title": "Activeloop - Unlock AI Data Analysis for GTM", "tagline": "Less Time in CRM, More Sales. Get instant insights across your entire stack from CRMs/ERPs to sales decks and product analytics. ", "body": "Hey 👋! Founder of Activeloop here. I am excited to introduce Activeloop AI Data Analysis for GTM operations. It lets you drill down from an executive dashboard to a single data point with just-in-time intelligence.\n\nWe didn’t built yet another dashboarding tool. It’s a solution to the single biggest bottleneck holding back your commercial strategy. The endless cycle of manual data preparation, integration, and reconciliation.\n\nhttps://www.youtube.com/watch?v=4Ua3Llox2HE\n\n### The Problem: You’re Drowning in Data Chaos\n\nThe story is always the same:\n\n* **Analysts as Data Janitors:** Your most valuable analysts are buried in manual work, stitching together data from multiple CRMs, reconciling Salesforce with SAP, and cleaning up messy, inconsistent fields.\n* **Reporting Backlogs:** A simple request for a new report kicks off a multi-week process. By the time it’s ready, the moment has passed and leadership doesn't trust the numbers anyway.\n* **Sales Reps Suffer:** Reps waste 4-6 hours every week just hunting for information and doing administrative work instead of selling.\n\nYou're tasked with driving strategy, but you're stuck managing data chaos.\n\n![uploaded image](/media/?type=post&id=94768&key=user_uploads/78090/e282c1b1-d361-4f5a-a4ac-3a721b21d96d)\n\n### Our Solution: The AI Analyst that Vibes Intelligence\n\nActiveloop is an AI Data Analyst for your operations team. We automate the painful, time-consuming data work that currently consumes 70%+ of your team's time.\n\nInstead of fighting with brittle integrations, we connect your fragmented sources such as CRMs, ERPs, product analytics, databases, data warehouses, even unstructured contracts and call transcripts, into a unified, queryable resource.\n\n![uploaded image](/media/?type=post&id=94768&key=user_uploads/78090/342debf4-c4fb-4a02-8480-ea5b128e26c9)\n\n### From Weeks to Minutes: Deliver “Just-in-Time” Intelligence\n\nActiveloop eliminates the wait, empowering your team with true self-service analytics.\n\n* **For Leadership:** In a forecast meeting, infinitely drill down into the data with simple questions like, \"Why did this region's deal cycle lengthen last quarter?\" Get immediate answers, without ever having to say, \"We'll get back to you.\"\n* **For Operations:** Free your analysts from being \"report builders\" and empower them to be strategists. They can finally focus on high-impact projects, confident that the underlying data is unified and trusted.\n\n![uploaded image](/media/?type=post&id=94768&key=user_uploads/78090/f5a7e592-3655-494e-a620-f92e99dc74e9)\n\n### We Tackle Your Messy Data Head-On\n\nYou’re probably thinking: \"How can AI possibly navigate our messy data? We have 50 different fields for the same metric.\"\n\nYou're right. That's the biggest blocker for any data initiative. That’s why our platform includes an **AI-assisted data mapping and taxonomy management layer**. It helps you harmonize your complex environment to build a clean data foundation that generates insights everyone trusts.\n\n![uploaded image](/media/?type=post&id=94768&key=user_uploads/78090/22c38e19-0d94-49cb-9485-e5de80ce8e3d)\n\n### Let's Solve Data Chaos Together\n\nIf you’re tired of seeing your team buried in manual data work, we want to talk to you.\n\n**Book a Call:** https://activeloop.ai/sales/\n\n\\\n**Get Started Now:** https://chat.activeloop.ai", "url": "https://www.ycombinator.com/launches/OeW-activeloop-unlock-ai-data-analysis-for-gtm", "votes": 107, "created_at": "2025-10-20T15:41:53.206Z" }, { "id": 90294, "title": "Activeloop L0: State-of-the-Art RAG Accuracy on Your Data", "tagline": "Turn PDFs, images & tables into instant, cited answers. Agentic RAG that just works.", "body": "Hey! Davit here form Activeloop. While working on [Deep Lake](https://github.com/activeloopai/deeplake), I have seen many RAG systems collapse when exposed to production-scale corporate data. They often rely on predefined loops, custom logic and rigid agent scaffolds. Activeloop-L0 provides your agent with highly precise and answers grounded on your multimodal data.\n\nhttps://youtu.be/pnU9yd4KWy0\n\n**Why can’t we reliably analyze corporate documents?**\n\n* **Architectural hurdles**: messy data integrations, unexpected infra costs, and reliability/safety constraints.\n* **Commodity RAG** lacks depth for multimodal enterprise data (documents, images, audio).\n* **Infrastructure burden**: parsing, chunking, embeddings, indexing, vector DBs, and agent loops slow teams.\n\n**But wait, is RAG still relevant despite large context models?**\n\nLet’s consider four extensive NASA documents \\[[1](https://www.nasa.gov/wp-content/uploads/2022/03/sls-reference-guide-2022-v2-508-0.pdf), [2](https://www.nasa.gov/wp-content/uploads/2023/02/orion-reference-guide-111022.pdf), [3](https://www.lpi.usra.edu/lunar/artemis/Artemis-I-Reference-Guide_NP-2022-03-3045-HQ.pdf), [4](https://www.ulalaunch.com/docs/default-source/rockets/2023_vulcan_user_guide.pdf)\\], each between 80 to 100 pages, containing visual descriptions, and pose a highly complex question.\n\n![uploaded image](/media/?type=post&id=90294&key=user_uploads/78090/c7a09db6-f7f7-46a4-a347-5b52154e6fc1)\n\nChatGPT with o3, despite having full PDFs in context, failed after 11 minutes of reasoning. Now, imagine you have thousands of corporate documents that can’t be contained in a context. In contrast, Activeloop-L0 provided the correct answer in 4 minutes and can scale to a million documents.\n\n**What is Activeloop-L0?**\n\n**Activeloop-L0** is a compound AI system that ingests your unstructured data and returns grounded answers. Behind the scenes, [Deep Lake](https://github.com/activeloopai/deeplake) indexes neural representations at scale, then fuses “thinking tokens” with high-precision retrieval for fast multi-hop reasoning.\n\nIt is available on [**chat.activeloop.ai**](http://chat.activeloop.ai) now.\n\n![uploaded image](/media/?type=post&id=90294&key=user_uploads/78090/64161f83-f7ba-46eb-9445-5688f55506ab)\n\n**How is it different compared to a traditional RAG?**\n\n* **Multimodal:** Built-in support for images, PDFs, audio, and spreadsheets.\n* **Integrated Reasoning & Retrieval:** Eliminates the need for loops.\n* **Deep Indexing:** Cost-effective multi-layer indexing for richer context early on.\n* **Simple:** Focus on innovation, not maintaining infrastructure.\n* **Grounded and Accurate:** Clear citations for trustworthy insights.\n\n**How accurate is Activeloop-L0?**\n\nActiveloop-L0 achieves overall 85.6% state-of-the-art accuracy on 1,142 multimodal questions (292 PDFs, 5.5K pages). It outperforms text only RAG by +20%, visual RAG by +10%, and Alibaba’s ViDoRAG by +6% on their own ViDoSeek benchmark.\n\n![uploaded image](/media/?type=post&id=90294&key=user_uploads/78090/8275782e-ffde-4351-bdc8-0d1c40918169)\n\n**Is there an OpenAI-compliant API?**\n\nYes, Activeloop-L0 is available with an OpenAI-compliant API. You can easily plug into your agents for providing high relevant context. You can get started here. https://docs.activeloop.ai/setup/quickstart\n\n![uploaded image](/media/?type=post&id=90294&key=user_uploads/78090/996c0193-4dab-49eb-ab85-6e78828b19ec)\n\n**Ready to Deploy on Your Data?**\n\nActiveloop is trusted by F500 including the likes of [**Bayer**](https://www.activeloop.ai/usecase/bayer/), [**Flagship Pioneering**](https://www.activeloop.ai/usecase/flagshippioneering/), [Matterport](https://www.activeloop.ai/usecase/matterport/) (W12 acquired by CoStar).\n\n* **Your Cloud**: Deploy on your cloud, ensuring data never leaves your infrastructure.\n* **Your Models**: Integrate your LLMs.\n* **Your Security**: SOC2 compliance, fine-grained access control, and SSO.\n\n[**Book a call**](https://www.activeloop.ai/contact/) to discuss enterprise deployment.", "url": "https://www.ycombinator.com/launches/NUM-activeloop-l0-state-of-the-art-rag-accuracy-on-your-data", "votes": 34, "created_at": "2025-05-12T16:26:50.543Z" } ] }, { "id": 1945, "name": "LabelFlow", "slug": "labelflow", "former_names": [ "Sterblue" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/799423cdb14bc1fbe4eb1d647eedf74d0a22e8c7.png", "website": "https://labelflow.ai/", "all_locations": "Nantes, Pays de la Loire, France; Remote", "long_description": "At LabelFlow, we build GitHub for visual data. Our mission is to facilitate and accelerate the development of AI at scale thanks to top-notch labeling tools and a dataset marketplace.", "one_liner": "GitHub for visual data", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1525147499, "tags": [ "Computer Vision", "Data Engineering", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2018", "status": "Inactive", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "France", "Europe", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/labelflow", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2018/labelflow.json", "year_founded": 2018, "city": "Nantes", "country": "FR", "linkedin_url": "https://www.linkedin.com/company/labelflow", "x_url": "https://twitter.com/LabelflowAI", "fb_url": "https://www.facebook.com/Labelflow/", "cb_url": "https://www.crunchbase.com/organization/labelflow", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/799423cdb14bc1fbe4eb1d647eedf74d0a22e8c7.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 192442, "full_name": "Geoffrey Vancassel", "title": "Founder/CEO", "founder_bio": "LabelFlow, GitHub for visual data (now) // Sterblue, software for drones to inspect energy infrastructures // Airbus, Design Engineer", "is_active": true, "linkedin_url": "https://fr.linkedin.com/in/geoffrey-vancassel-80b2aa12", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2cfd8da516c0f480d129cb10d3b65e9dfee30544.jpg" }, { "user_id": 252638, "full_name": "Nicolas Draber", "title": "Founder/CPO", "founder_bio": "Aerospace Engineer, Co-founder of Sterblue and LabelFlow", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nicolasdraber/", "x_url": "https://twitter.com/ndraber", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/cfe313a73008c291f4aee24b4f5e4172f0241447.jpg" }, { "user_id": 116604, "full_name": "Vincent Lecrubier", "title": "Founder/Chief Technical Officer", "founder_bio": "Olympic Finalist, Computer Science PhD, Aerospace Engineer, Co-funder of Sterblue YC S18", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/crubier/", "x_url": "https://twitter.com/VLecrubier", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9c482f082d557d1e0ffe1bd20186864f22793853.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 11895, "name": "Overview", "slug": "overview", "former_names": [ "Overview.ai" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/cd1326e4f02e779f1009ae931cfeb26eb1eb20b7.png", "website": "https://overview.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Here’s a secret between you and me: even the world’s largest manufacturers, companies like Tesla and Toyota, waste billions of dollars every year making products with quality issues. Building high-quality things at scale is incredibly hard. It doesn’t just happen because you hire smart people or buy good machines. It requires seeing problems early, understanding them deeply, and acting in real time, something factories were never designed to do.\r\n\r\nAt Overview.ai, we’re changing that. We build custom hardware, edge AI, and software systems that give manufacturers real visibility into how their products are actually being made. Our technology helps catch defects earlier, reduce waste, and fundamentally improve how factories operate. This work matters, not just for our customers, but for keeping American manufacturing competitive in a world that’s moving faster every year.", "one_liner": "Reshaping industrial quality with AI, hardware, and software", "team_size": 40, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1538509310, "tags": [ "Deep Learning", "IoT", "Computer Vision", "Manufacturing", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2019", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/overview", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2019/overview.json", "year_founded": 2019, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/overview-ai/", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/overview-ai", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/cd1326e4f02e779f1009ae931cfeb26eb1eb20b7.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Brad Flora", "url": "https://www.ycombinator.com/people/brad-flora" }, "founders": [ { "user_id": 285358, "full_name": "Austin Appel", "title": "Founder", "founder_bio": "Austin Appel is the head of product and a co-founder at Overview, where he is responsible for leading product development and operations. With over 8 years of experience in manufacturing, he works with customers to ensure that customers can quickly adopt and use artificial intelligence-based computer vision systems.\n\nPreviously, Austin spent four years at Tesla in manufacturing and R&D.\n\nAustin holds BS Degrees in Manufacturing Engineering and Mechanical Engineering from Northwestern University.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/austin-appel-aa822b66/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1e36e2fb2f960866341c37d8d0964d74e14d8233.jpg" }, { "user_id": 247841, "full_name": "Russell Nibbelink", "title": "Founder", "founder_bio": "I am the head of engineering and a co-founder at Overview. \n\nBefore joining Overview, I was a software engineer at Salesforce, where I worked on web-scale infrastructure projects, including the migration of Salesforce’s largest customers to upgraded hardware with no downtime.\n\nI studied EECS at UC Berkeley, where I was also the head teaching assistant in the field of High Performance Computing.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/russellnibbelink/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fa0586294cc7538ca638b3c8d3ef42d0a9b93813.jpg" }, { "user_id": 285339, "full_name": "Christopher Van Dyke", "title": "Founder", "founder_bio": "After 8 years at Tesla I founded Overview. Overview provides cameras and AI recognition algorithms to help manufacturers automate inspection steps, improve uptime, and reduce errors on their manufacturing lines. At Tesla, I built the team that designed the Gigafactory and launched the Supercharger network. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/christopher-van-dyke/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6b00f242e390331f22c5d6cb10c4d83bb83bcd5c.jpg" } ], "jobs": [ { "id": 84662, "title": "Staff Embedded Software Engineer – Product Lead (Embedded AI)", "url": "https://www.ycombinator.com/companies/overview/jobs/FbHeAG9-staff-embedded-software-engineer-product-lead-embedded-ai", "location": "San Francisco, CA, US / Remote (Chicago, IL, US; Atlanta, GA, US)", "type": "Full-time", "role": "Engineering", "role_type": "Embedded systems", "salary_range": "$150K - $220K", "equity_range": "0.10% - 0.25%", "experience": "6+ years", "visa": "Will sponsor", "skills": [] }, { "id": 87990, "title": "Head of Technical Special Projects", "url": "https://www.ycombinator.com/companies/overview/jobs/EtSRaQQ-head-of-technical-special-projects", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$125K - 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", "one_liner": "Synthetic data for better vision.", "team_size": 5, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1616603536, "tags": [ "Machine Learning", "Robotics", "Computer Vision", "Data Engineering", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2021", "status": "Inactive", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "Canada", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/sbx-robotics", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2021/sbx-robotics.json", "year_founded": 2020, "city": "Toronto", "country": "CA", "linkedin_url": "https://www.linkedin.com/company/sbx-robotics/", "x_url": "https://twitter.com/sbxrobotics", "fb_url": "https://www.facebook.com/sbxrobotics", "cb_url": null, "github_url": "https://github.com/sbxrobotics", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/511a67e1066575bd8304628248a6a9307430e18f.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 505268, "full_name": "Ian Dewancker", "title": "Founder", "founder_bio": "Led applied research projects at Uber ATG, Kindred AI, and SigOpt focusing on computer vision, robotics, and optimization for machine learning. 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Our platform enables customers to efficiently annotate their point cloud and image data, accelerating their path to autonomy.\r\n\r\nWe're a fast-growing, remote-first YC startup with a lean team and healthy runway. [Segments.ai](http://segments.ai/) is used by large organizations as well as innovative startups building the next generation of autonomous drones, delivery robots, self-driving cars, and more.", "one_liner": "Build better computer vision models by building better datasets", "team_size": 8, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1615300374, "tags": [ "Deep Learning", "Developer Tools", "Computer Vision" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2021", "status": "Acquired", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "Belgium", "Europe", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "question": "What's the history of your company from getting started until the present day? What were the big inflection points?", "answer": "In 2020, [Segments.ai](http://Segments.ai) launched as a data labeling solution designed explicitly for segmentation. Initially, we had many customers in the robotics and autonomous driving industries. This stimulated us to address the complex needs of autonomous navigation, leading us to introduce point cloud support.Through close collaboration with our customers, we quickly realized that manually labeling multiple modalities can be time-consuming, costly, and ineffective. Therefore, we shifted our focus towards providing multi-sensor support by integrating built-in projection steps and automation.HISTORY* Founded in early 2020 by Otto Debals (CEO) and Bert De Brabandere (CTO) after finishing their PhDs and gaining experience in the (automotive) industry* Funded in 2021 by YCombinator and other VC funds, including Merus Capital, Volta Ventures.* [Segments.ai](http://Segments.ai) is ISO27001 certified, GDPR compliant and EU based* We hired our first employee in 2021 and our first employee in the USA (where the majority of our customers are based) in 2023." }, { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Automotive and robotics companies increasingly use lidar, radar, and camera sensors during labeling and deployment. The more sensors they use, the more accurate and robust their ML models become.[Segments.ai](http://Segments.ai) is designed to address the challenges faced by ML engineers. According to a recent study, more than 70% of ML engineers spend over 50% of their time building training data. Using additional sensors can further increase the budget and time for development and training. With [Segments.ai](http://Segments.ai), you streamline this process, saving both time and resources.[Segments.ai](http://Segments.ai) is committed to providing the best solutions for computer vision engineers and data scientists working in autonomous robotics. You can label all your data in a single interface and seamlessly integrate it with your pipelines and infrastructure, making it easier to build multi-sensor data models at scale." } ], "url": "https://www.ycombinator.com/companies/segments-ai", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2021/segments-ai.json", "year_founded": 2020, "city": "Brussels", "country": "BE", "linkedin_url": "https://www.linkedin.com/company/segmentsai/", "x_url": "https://twitter.com/SegmentsAI", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/segments-ai", "github_url": "https://github.com/segments-ai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ac88c08aefaf0753bc9d29d8d7ced656148b852c.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Harj Taggar", "url": "https://www.ycombinator.com/people/harj-taggar" }, "founders": [ { "user_id": 600471, "full_name": "Otto Debals", "title": "Founder", "founder_bio": "CEO & co-founder of Segments.ai. Studied mathematical & electrical engineering, and obtained a PhD in signal separation / multilinear algebra. During my PhD, launched Investimize (a financial robo-advisor) together with my current cofounder Bert and also pursued CFA level I. Spent 2 years afterwards at McK - supporting >15 projects across different functions, industries and regions. Launched Segments.ai in Jan 2020. Passionate about whisky, F1 and long-distance running!", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/otto-debals/", "x_url": "https://twitter.com/ottodebals", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/47e795756a420d12b1582ff63804fd9108810de8.jpg" }, { "user_id": 282465, "full_name": "Bert De Brabandere", "title": "Founder", "founder_bio": "Founder at Segments.ai, PhD from KU Leuven, Computer Vision and Machine Learning. All the way from Belgium.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/bert-de-brabandere/", "x_url": "https://twitter.com/BBrabandere", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a0fe615dd7be5eba5cb70ea901becd8dec905462.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 22752, "name": "ClipDrop", "slug": "clipdrop", "former_names": [ "Init ML", "Clickdrop" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3bfd4a654412f2405d3448ad846ed61f08074a04.png", "website": "https://clipdrop.co", "all_locations": "Paris, Île-de-France, France; Remote", "long_description": "ClipDrop is an app that turns regular mobile photos into professional product visuals.\r\n\r\nThanks to companies like Poshmark, Etsy and Ebay, there are now over 25M sellers who need great visuals for their shops but most of them don’t have access to a professional photographer or a design team. We replace all of this with our app available on mobile and desktop.\r\n\r\nWe’re a team of former Google, Facebook employees and Machine learning PhD’s and academic Professors. We believe that machine learning will bring professional photography and design to millions of businesses for a fraction of the cost.", "one_liner": "Turn regular mobile photos into professional product visuals", "team_size": 5, "industry": "Consumer", "subindustry": "Consumer -> Content", "launched_at": 1614841884, "tags": [ "SaaS", "Computer Vision", "Design Tools" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2021", "status": "Acquired", "industries": [ "Consumer", "Content" ], "regions": [ "France", "Europe", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/clipdrop", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2021/clipdrop.json", "year_founded": 2020, "city": "Paris", "country": "FR", "linkedin_url": "", "x_url": "https://twitter.com/clipdropapp", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3bfd4a654412f2405d3448ad846ed61f08074a04.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 590803, "full_name": "Cyril Diagne", "title": "Founder", "founder_bio": "Co-founder and CEO of Init ML.\n\nFormer Professor and Head of Media & Interaction Design at ECAL (Lausanne University of Arts & Design, Switzerland)\n\nFormer artist in residence at Google Arts & Culture, where I helped kickstart the Google Arts Experiments initiative and created multiple machine learning projects such as the viral Google Art Selfie.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/cyrildiagne/", "x_url": "https://www.twitter.com/cyrildiagne", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2eaf5d2fea43252a16177e16761d333c08cc8c89.jpg" }, { "user_id": 596995, "full_name": "Jonathan Blanchet", "title": "Founder", "founder_bio": "Co-founder and CTO of ClipDrop.\nI have been working with the web stack for more than 10 years and more recently focused on new fields like Machine Learning, DevOps…\nI'm also a happy member of the Lab212 interdisciplinary art collective.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jonathanblanchet/", "x_url": "https://twitter.com/jblanchefr", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e93136b1643191adcbaa5aee3e0a9191bf2704ce.jpg" }, { "user_id": 659848, "full_name": "Damien Henry", "title": "Founder", "founder_bio": "Co-founder and COO of ClipDrop.\n\nCreated Google Cardboard with a friend in Paris\nHead of Innovation for @googlearts Lab\nPreviously: co-founded @audiogaming & @novelab", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/damien-henr7/", "x_url": "https://twitter.com/dh7net", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d2e0e081f6f6e1a57ad6bd0237f31cf364cabc53.jpg" } ], "jobs": [], "news": [ { "title": "Stability AI Acquires Init ML, Makers of Clipdrop Application — Stability AI", "url": "https://stability.ai/news/stability-ai-acquires-init-ml-makers-of-clipdrop-application", "date": "Nov 30, 2023" }, { "title": "Stability AI Acquires Init ML, Makers of Clipdrop Application — Stability AI", "url": "https://stability.ai/blog/stability-ai-acquires-init-ml-makers-of-clipdrop-application", "date": "Mar 07, 2023" } ], "launches": [] }, { "id": 22969, "name": "LightTwist", "slug": "lighttwist", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f8ec12fb14b1244b971c7c93d53e48145c568d22.png", "website": "https://lighttwist.com", "all_locations": "Denver, CO, USA; Remote", "long_description": "LightTwist let you record and stream your show in a realistic virtual studio, even if your guests and co-hosts are remote. All you need is a camera, a browser, and LightTwist.", "one_liner": "Realistic virtual video studio to record and stream from your browser", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1616254222, "tags": [ "Augmented Reality", "Computer Vision", "Design Tools" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2021", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/lighttwist", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2021/lighttwist.json", "year_founded": 2021, "city": "Denver", "country": "US", "linkedin_url": "https://www.linkedin.com/company/lighttwist", "x_url": "https://twitter.com/lighttwistapp", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f8ec12fb14b1244b971c7c93d53e48145c568d22.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Garry Tan", "url": "https://www.ycombinator.com/people/garry-tan" }, "founders": [ { "user_id": 553876, "full_name": "Vikas Reddy", "title": "Founder", "founder_bio": "Vikas is the Founder and CEO of LightTwist. Prior to LightTwist, he was co-founder of RedLaser (barcode scanning price comparison app, acquired by eBay in 2010), and co-founder of computer vision startup Occipital where he built products like Structure Sensor and Canvas.\n\nHe graduated from the University of Michigan with a degree in Computer Science Engineering.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/vikasreddy/", "x_url": "https://twitter.com/vikasreddy", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/96f32a7162210b0d720f24fbcdcced606a30e9bd.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 104358, "title": "LightTwist - Record and stream in a realistic virtual studio, even if your guests and co-hosts are remote", "tagline": "A virtual video studio that runs in Chrome", "body": "Hi friends! Vikas here from LightTwist, excited to share our new launch!\n\n**Check out our 45 second launch video here**:[ https://www.youtube.com/watch?v=fYGvhRrOXBI](https://www.youtube.com/watch?v=fYGvhRrOXBI)\n\n**TL;DR:** LightTwist lets you record and stream in a realistic virtual studio, even if your guests and co-hosts are remote.\n\n**The problem**: Your video/podcast/webinar looks like a boring Zoom call. If you want to upgrade to something that looks like a real show you need to build out a physical video studio and bring people into it.\n\n**The solution:** LightTwist is a virtual video studio that runs in Chrome - all the heavy compute is done in the cloud using realtime computer vision, Unreal Engine 5, and WebRTC. This means you can record and stream your show, product announcement, or webinar in a realistic virtual studio, even if your guests and co-hosts are remote.\n\n**We’re launching publicly next week, July 1 at 9 am PT but wanted to give early access to YC!**\n\n**Launch YC special offer 1 (up to 10 spots) - Buy a Show!** Purchase [here](https://buy.stripe.com/9B6aEY7YX0Iv0Bo6Xz5c40b)\n\n* We’ll record and produce your WWDC style product announcement or podcast/YouTube show episode for $500.\n* Includes customized virtual studio with your branding and 2 months of LightTwist Studio.\n* We’re experimenting with a full service product which is why we’re offering this on Launch YC!\n\n**YC special offer 2 - 90% off LightTwist for 2 months!** Sign up [here](https://studio.lighttwist.com/onboarding/9996d28e-75a0-476b-ad7b-621d43200f95) (use promo code **LAUNCHYC2026**)\n\n* You can sign up, pick from a studio template and start recording or streaming.\n* I’ll personally help you get your studio set up! I’ll email you after you sign up to set up a time\n\n**Ask:** If there’s a creator or company you think would be a great fit, please let me know or have them email me at [vikas@lighttwist.com](mailto:vikas@lighttwist.com) !\n\n**How we got here:**\n\nI started LightTwist in 2021 after seeing how Industrial Light and Magic built a huge 360 surround stage for the Mandalorian show where they had a tracked camera and rendered the scene in realtime using Unreal Engine.\n\nI’ve started a few computer vision companies (RedLaser, a barcode scanning price comparison app -[acquired by eBay](https://www.wsj.com/articles/BL-DGB-15645), [Twindo.com](http://Twindo.com) - FKA Occipital), so I thought there had to be a way to make virtual production much more accessible using computer vision and software engineering.\n\nWe’ve gone through several iterations, started as an iOS app for filmmakers, built a macOS app to be an awesome virtual camera for Zoom/etc, but have gotten a lot more traction once we positioned LightTwist as a virtual video studio and rebuilt it to run in the cloud.\n\nWe’ve signed an enterprise customer using LightTwist to power their video streaming platform (not yet announced!), and we’ve been iterating with other creators and studios including building a gameshow for Jimmy Fallon’s video production company.\n\nWe’re excited to be rolling this out publicly!\n\n![uploaded image](/media/?type=post&id=104358&key=user_uploads/553876/8b670e29-4613-40a1-bfca-92be5d46d938)\n\n**Launch video here**: [**https://www.youtube.com/watch?v=fYGvhRrOXBI**](https://www.youtube.com/watch?v=fYGvhRrOXBI)", "url": "https://www.ycombinator.com/launches/R9C-lighttwist-record-and-stream-in-a-realistic-virtual-studio-even-if-your-guests-and-co-hosts-are-remote", "votes": 6, "created_at": "2026-06-30T22:21:55.751Z" } ] }, { "id": 23451, "name": "MarqVision", "slug": "marqvision", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b993f487d88442acd03e44f8b8ba2ff9dd1c72c7.png", "website": "https://www.marqvision.com/", "all_locations": "Los Angeles, CA, USA", "long_description": "MarqVision’s mission is to protect and build a future shaped by original ideas, innovations, and creativity. As a leading online brand protection solution, we help global brands identify and remove counterfeits and combat piracy from over 1,500 platforms and thousands of rogue websites in over 118 countries. In a time when counterfeiting poses an increasingly serious challenge, we believe that democratizing legal expertise and access to the latest in generative AI technology is critical for continuing to support the inventors, developers, and artists of the world who work tirelessly to deliver safe, high-quality products.\r\n\r\nMarqVision’s technology powers everything from detection, monitoring, and even enforcement to protect brands at scale. Founded in 2020 by Harvard Law graduates, MarqVision is proudly backed by Altos Ventures, DST Global Partners, Softbank, Atinum Investments and Y Combinator. Our commitment to innovation has also been recognized with a prestigious 2022 Innovation Award from LVMH Louis Vuitton Moët Hennessy (LVMH), and we are honored to be part of the LVMH accelerator program, La Maison des Startups, at the Station F incubator. As we bring forth the next evolution of brand protection, we invite businesses everywhere to join us in safeguarding the creativity that drives our world.", "one_liner": "IP operating software for brands and content companies", "team_size": 200, "industry": "B2B", "subindustry": "B2B", "launched_at": 1624608504, "tags": [ "SaaS", "Computer Vision", "B2B", "LegalTech", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": true, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/marqvision", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/marqvision.json", "year_founded": 2020, "city": "Los Angeles", "country": "US", "linkedin_url": "https://www.linkedin.com/company/marqvision", "x_url": "", "fb_url": "https://www.facebook.com/marqvision/", "cb_url": "https://www.crunchbase.com/organization/marq", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b993f487d88442acd03e44f8b8ba2ff9dd1c72c7.png", "app_video_url": null, "dday_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/video_event/54/1630627519247-utrv3m6hyg7-a404e30bb0c93d6e5d845386bd6769e9/0c597e41a05c52dc6cffab6b611df5ba.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQC4NIECABPQ2AHNW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022655Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=2dd8ee7d8d5148a8ab747197cdcab66f2f9e12dcc3a2a2cf5d2a00a9bd94034a", "company_photos": [], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 593873, "full_name": "Mark Lee", "title": "Founder/CEO", "founder_bio": "CEO & Founder at MarqVision\nHarvard Law School (JD)\nHarvard College (BA)\nMcKinsey & Company\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/marklee0928/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/231c53c614af496ac854b1dbaf2a370713dd743a.jpg" } ], "jobs": [], "news": [ { "title": "Harvard Law to AI: MarqVision lands $48M to combat brand abuse | TechCrunch", "url": "https://techcrunch.com/2025/09/15/harvard-law-to-ai-marqvision-lands-48m-to-combat-brand-abuse/", "date": "Sep 15, 2025" }, { "title": "MarqVision grabs $20M to nab counterfeiters with an AI-powered IP protection platform – TechCrunch", "url": "https://techcrunch.com/2022/08/02/marqvision-wants-to-combat-counterfeit-goods-with-its-ai-powered-ip-protection-platform/", "date": "Aug 02, 2022" }, { "title": "MarqVision Raises $20M to Stop Online Counterfeiters - dot.LA", "url": "https://dot.la/marqvision-counterfeit-ai-2657796765.html", "date": "Aug 02, 2022" }, { "title": "Can AI Solve The Huge eCommerce Counterfeit Problem in 2022?", "url": "https://www.eseller365.com/the-ecommerce-counterfeit-problem-marqvision/", "date": "Feb 02, 2022" }, { "title": "SoftBank Ventures Asia leads $5m round of IP protection platform", "url": "https://www.techinasia.com/softbank-ventures-asia-leads-5m-round-ip-protection-platforms-marqvision", "date": "Sep 29, 2021" } ], "launches": [] }, { "id": 24142, "name": "SnapCalorie", "slug": "snapcalorie", "former_names": [ "CalorieSnap" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/8fb8d3ff1ae8ec164dd664632cc42c03b043c549.png", "website": "https://www.snapcalorie.com/", "all_locations": "New York City, NY, USA; Washington, DC, USA", "long_description": "SnapCalorie is the world’s first app where you can take a photo of ANY meal and get an accurate calorie count. It takes seconds to use and is more accurate than a trained nutritionist. The team previously co-founded Google Lens, Cloud Vision API, and published a CVPR paper demonstrating the first algorithm to outperform a professional nutritionist at calorie counting.", "one_liner": "Single photo nutrition tracking.", "team_size": 5, "industry": "Consumer", "subindustry": "Consumer", "launched_at": 1627329794, "tags": [ "Computer Vision", "Fitness", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "Consumer" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/snapcalorie", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/snapcalorie.json", "year_founded": 2021, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/snapcalorie/", "x_url": "https://twitter.com/snapcalorie", "fb_url": "https://www.facebook.com/snapcalorie", "cb_url": "https://www.crunchbase.com/organization/snapcalorie", "github_url": "https://github.com/snapcalorie", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/8fb8d3ff1ae8ec164dd664632cc42c03b043c549.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 727234, "full_name": "Wade Norris", "title": "Founder", "founder_bio": "Founded SnapCalorie. 500k+ MAUs, profitable, twice the accuracy of a professional nutritionist, published first algorithm in top conference of computer vision (CVPR).\n\nCo-founded The Spaghetti Detective, profitable, most popular paid software in 3D printing.\n\nPreviously co-lead for the 100+ researcher Mobile Vision team at Google AI. The team co-founded Google Cloud Vision API, Google Lens, MobileNets, and a wide variety of computer vision models powering products across Google.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/wadenorris/", "x_url": "https://twitter.com/wadednorris", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/72ddeb7f1c688c925ba9f351065b51abef22bb41.jpg" }, { "user_id": 734326, "full_name": "Scott Baron", "title": "Founder", "founder_bio": "Scott Baron, the co-founder and CTO of SnapCalorie. He previously spent 12 years as a systems engineer in Aerospace where he developed software and algorithms to support high reliability missions and advanced data science applications.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/scott-baron-7b09b011/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ef506ba76aad8b912e393f4fbe87297c20106f11.jpg" } ], "jobs": [], "news": [ { "title": "SnapCalorie taps AI to estimate the caloric content of food from photos | TechCrunch", "url": "https://techcrunch.com/2023/06/26/snapcalorie-computer-vision-health-app-raises-3m/", "date": "Jun 26, 2023" }, { "title": "SnapCalorie Raises $2 Million in Seed Funding from Y Combinator | citybiz", "url": "https://www.citybiz.co/article/429315/snapcalorie-raises-2-million-in-seed-funding-from-y-combinator/https://www.citybiz.co/article/429315/snapcalorie-raises-2-million-in-seed-funding-from-y-combinator/", "date": "Jun 13, 2023" }, { "title": "Health in a Handbasket: SnapCalorie App | WUHF", "url": "https://foxrochester.com/news/good-day-rochester/health-in-a-handbasket-snapcalorie", "date": "Apr 17, 2023" } ], "launches": [] }, { "id": 24383, "name": "Milky Way AI", "slug": "milky-way-ai", "former_names": [ "Milky Way AI Pte.Ltd" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/2efe7f33198c252ca563c3f685bd5e290b4064d2.png", "website": "https://milkyway.ai/", "all_locations": "Singapore, Singapore; Remote", "long_description": "We help CPG brands to understand how their products are being displayed, priced and what are their competitors are doing across millions of physical stores in real time with our computer vision powered mobile app. \r\n\r\nEach year these brands spend $500 bn annually buying prime retail shelf space to influence customer purchasing decisions, but they have no easy way to audit it. \r\n\r\nOur mobile app reduces the time of these audits by 10x and cost by 3x.", "one_liner": "Mobile app for CPG brands to connect to millions of stores globally", "team_size": 5, "industry": "B2B", "subindustry": "B2B -> Retail", "launched_at": 1624944246, "tags": [ "SaaS", "Computer Vision", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Inactive", "industries": [ "B2B", "Retail" ], "regions": [ "Singapore", "Southeast Asia", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/milky-way-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/milky-way-ai.json", "year_founded": 2019, "city": "Singapore", "country": "SG", "linkedin_url": "https://www.linkedin.com/company/milkywayai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/2efe7f33198c252ca563c3f685bd5e290b4064d2.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 460369, "full_name": "Eunice Wong", "title": "Founder", "founder_bio": "Spent the last decade helping the largest retailers and brands with many parts of the business :\nPredicting what customer buy next with customer data science\nDigitizing loyalty programs/super apps ecosystem\nCreating cross-border e-comm payments and fulfillment solutions ", "is_active": true, "linkedin_url": "https://linkedin.com/in/eunicewes", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/970201723be7c3f581d9d8e85f0e44d959e6adac.jpg" }, { "user_id": 185708, "full_name": "Sagar Setu", "title": "Founder", "founder_bio": "Robotics generalist with a PhD in Aerospace Engineering. Strong believer that humans should be spending more time doing science and arts rather than tedious mundane work. Cares about equal access to water.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/sagarsetu/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e2d02038f1d8880da13554454cb2938f0f17cc6a.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 24551, "name": "Mach9", "slug": "mach9", "former_names": [ "Mach9 Robotics" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/05390d32227edd1416d7dc545801c3fc59e2ac15.png", "website": "https://www.mach9.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Mach9 accelerates how teams model and understand the world with automated feature extraction technology. Founded in 2021 and headquartered in San Francisco, Mach9's flagship Digital Surveyor platform helps transportation agencies, utility companies, and engineering firms extract miles of road corridor projects in a fraction of traditional processing time. Built on decades of research from Carnegie Mellon University's Robotics Institute, Mach9 empowers the architecture, engineering, and construction industries to tackle large-scale infrastructure projects with unprecedented speed and accuracy. Leading survey and geospatial organizations, including Langan, Olsson, Woolpert, HDR, and major state DOTs trust Digital Surveyor to create engineering-grade maps for infrastructure design and operations.", "one_liner": "AI CAD Software for Civil Engineering", "team_size": 25, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1628526474, "tags": [ "Machine Learning", "Computer Vision", "Design Tools", "Infrastructure", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/mach9", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/mach9.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/mach9geo/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/mach9robotics", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/05390d32227edd1416d7dc545801c3fc59e2ac15.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Aaron Epstein", "url": "https://www.ycombinator.com/people/aaron-epstein" }, "founders": [ { "user_id": 311199, "full_name": "Alexander Baikovitz", "title": "Founder", "founder_bio": "Building software that actually understands the physical world to map, design, build, and operate infrastructure.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alexbaikovitz/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1a4a4a7ee706d2b46a02c20f17ad12c3b880cfe1.jpg" }, { "user_id": 778495, "full_name": "Haowen Shi", "title": "Founder", "founder_bio": "Building software to efficiently create actionable 3D maps for critical infrastructure.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/haowensh/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b5f21444a01d1732352d40eec71971318478ace0.jpg" } ], "jobs": [ { "id": 95817, "title": "QA Survey Technician", "url": "https://www.ycombinator.com/companies/mach9/jobs/zZBxbuX-qa-survey-technician", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$80K - 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$180K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 109680, "title": "Community Marketing Manager", "url": "https://www.ycombinator.com/companies/mach9/jobs/TrHOo1g-community-marketing-manager", "location": "San Francisco", "type": "Full-time", "role": "Support", "role_type": null, "salary_range": "", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 109681, "title": "Head of Growth", "url": "https://www.ycombinator.com/companies/mach9/jobs/BWLMI1q-head-of-growth", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$180K - $220K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 109682, "title": "Software Engineer - Test Automation (SDET)", "url": "https://www.ycombinator.com/companies/mach9/jobs/RBOssgq-software-engineer-test-automation-sdet", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$130K - 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$400K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 113501, "title": "ML Engineer, Product", "url": "https://www.ycombinator.com/companies/mach9/jobs/uUwtMzD-ml-engineer-product", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$180K - $300K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 114030, "title": "ML Infrastructure Engineer", "url": "https://www.ycombinator.com/companies/mach9/jobs/6vBDUBp-ml-infrastructure-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$160K - $250K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 115423, "title": "Perception Intern", "url": "https://www.ycombinator.com/companies/mach9/jobs/iaBeLx3-perception-intern", "location": "San Francisco", "type": "Internship", "role": "Operations", "role_type": null, "salary_range": "", "equity_range": "", "experience": null, "visa": "US citizen/visa only", "skills": [] }, { "id": 116444, "title": "Business Development Representative", "url": "https://www.ycombinator.com/companies/mach9/jobs/15HcGWq-business-development-representative", "location": "San Francisco", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$105 - $120", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "Mach9 Unveils Digital Surveyor 2 A New Standard For Lidar Feature Extraction", "url": "https://informedinfrastructure.com/post/mach9-unveils-digital-surveyor-2-a-new-standard-for-lidar-feature-extraction", "date": "Mar 20, 2026" }, { "title": "Between the Scanner and the Deliverable: How Mach9 is Building the Missing Layer in Geospatial Production", "url": "https://www.xyht.com/surveying/between-the-scanner-and-the-deliverable-how-mach9-is-building-the-missing-layer-in-geospatial-production/", "date": "Mar 19, 2026" }, { "title": "Mach9 Unveils an AI-Assisted Workflow Focused on Trust and Control at Scale", "url": "https://www.geoweeknews.com/sponsored/mach9-unveils-an-ai-assisted-workflow-focused-on-trust-and-control-at-scale", "date": "Feb 10, 2026" }, { "title": "Mach9 wants to create maps 30 times faster than previous standards | Geo Week News | Lidar, 3D, and more tools at the intersection of geospatial technology and the built world", "url": "https://www.geoweeknews.com/sponsored/mach9-digital-surveyor-lidar-data-feature-extraction-automation", "date": "Feb 06, 2024" }, { "title": "Mach9 launches new geospatial software platform aimed at infrastructure space", "url": "https://www.geoweeknews.com/news/tech-reality-capture-mach9-launches-new-geospatial-software-platform-aimed-at-infrastructure-space?utm_source=social&utm_medium=linkedin&utm_campaign=5msocial", "date": "Mar 16, 2023" } ], "launches": [ { "id": 99241, "title": "Mach9 - Make Maps Fast. AI-Powered CAD Software for Reality Capture", "tagline": "We help infrastructure survey and engineering teams turn raw point cloud data into engineering-ready CAD/GIS deliverables up to 100x faster.", "body": "Hi everyone! \n\nWe’re [Alex](https://www.linkedin.com/in/alexbaikovitz/), [Zach](https://www.linkedin.com/in/zachsussman/), [Haowen](https://www.linkedin.com/in/haowensh/), and [Michael ](https://www.linkedin.com/in/mmong/)- co-founders of [Mach9](https://mach9.ai/). \n\n**📍TLDR**: \n\nMach9's Digital Surveyor 2 is a geospatial platform that turns LiDAR point clouds into engineering-ready deliverables up to 100x faster. Its AI-powered extraction engine identifies and extracts linear features — including curbs, pavement edges, guardrails, and barriers — and learns from each user's edits to improve accuracy over time.\n\nSurvey and engineering teams at Langan, Olsson, HDR, Woolpert, Michael Baker International, and major utilities and state DOTs already use it in production.\n\nCheck out our Digital Surveyor 2 Launch Video:\n\n[**https://youtu.be/XoISK5nx87E?si=mzcrxg5uI3hynSm2**](https://youtu.be/XoISK5nx87E?si=mzcrxg5uI3hynSm2)\n\n**❌ The Problem**\n\nEvery road, bridge, fiber network, and utility corridor project starts with a survey. Increasingly, that survey is done with LiDAR scans encompassing millions of points.\n\n**But here's the thing**: Collecting data isn't the hard part anymore, turning it into engineering ready deliverables is. Today, that work is being done manually. Skilled technicians sit in front of a screen and trace features one by one across potentially thousands of features. Paint lines, curbs, poles, signs, guardrails. Mile after mile.\n\n**As a result**: Firms turn down work they could win, deadlines slip, and the most expensive bottleneck of the workflow is the part that hasn't changed in 20 years.\n\n**✅ Our Solution**\n\nDigital Surveyor 2 is a web-based CAD application where AI works alongside drafters to extract features from point cloud data dramatically faster — without sacrificing the precision their clients require.\n\nHere's what makes it different:\n\n🔹 **AI that drafts with you**. \n\nThe feature extraction engine in Digital Surveyor 2 suggests linear features, such as curbs, pavement edges, guardrails, and barriers directly from LiDAR point clouds — and adapts to the user’s drawing patterns. As users draw, the software picks up on their patterns and refines its suggestions in real time.\n\n![uploaded image](/media/?type=post&id=99241&key=user_uploads/311199/ca66f2e8-267b-4693-83ec-81b92eabb558)\n\n🔹 **Field-to-finish in one environment**. \n\nIngest point cloud data from any system such as RIEGL, Trimble, or Leica scanners. Extract features with AI-assisted workflows and precision tools. Easily QA everything in 3D. Export directly into AutoCAD, MicroStation, or ESRI. No switching between tools. No preprocessing pipeline.\n\n🔹 **Built for the people who do this work**. \n\nDS2 was designed so that a surveyor who knows where points go in the field can be productive on day one. Surveyors, field techs, and project managers can easily be working in it with minimal ramp time or CAD expertise.\n\n![uploaded image](/media/?type=post&id=99241&key=user_uploads/311199/e5e82676-f1fd-4bcd-bd86-fcca46636e29)\n\n🙏 **Our Ask**\n\n* **👉 Follow us** — Stay up to date with what we're building. Follow Mach9 on[ ](https://www.linkedin.com/company/mach9)[LinkedIn](https://www.linkedin.com/company/mach9geo).\n* **🔗 Intro us** — Know someone at a electric / telecom utility, data center owner, DOT, or survey/engineering firm that works with LiDAR or mobile mapping data? A warm intro goes a long way. We're especially looking to connect with survey and engineering leaders, mobile mapping teams, CAD/GIS technicians, and geospatial production leads.\n* **🚀 Try it yourself** — If your team is turning point clouds into deliverables today, we'd love to show you what DS2 can do. [Get started here](http://mach9.ai/get-started?utm_source=YC&utm_medium=article&utm_campaign=launch_post).\n* 💼 **We're hiring** — We're building engineering software to transform the $3 trillion global infrastructure industry. If you want to work on hard problems at the intersection of AI, geospatial, and computer graphics, we're scaling across GTM, engineering, and operations. Check out our open roles [here](https://jobs.ashbyhq.com/mach9).\n\n![uploaded image](/media/?type=post&id=99241&key=user_uploads/311199/d7592b72-f824-4cf8-9921-48b5b14674fc)\n\n", "url": "https://www.ycombinator.com/launches/Pof-mach9-make-maps-fast-ai-powered-cad-software-for-reality-capture", "votes": 29, "created_at": "2026-03-30T20:07:04.564Z" } ] }, { "id": 24567, "name": "Tenyks", "slug": "tenyks", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7006298209ca0e4b566665242aadbc44f58b1d59.png", "website": "http://tenyks.ai/", "all_locations": "Sofia City Province, Bulgaria; Remote", "long_description": "Tenyks (YC S21) is a Cambridge University spin-out building the World's Most Versatile Visual Intelligence Platform—think Snowflake for Vision or GPT for Video.\r\n\r\nProblem: Companies today collect oceans of visual data (from cameras, drones, and satellites), but struggle to extract actionable insights. Current solutions rely heavily on manual analysis by call centres in locations like the Philippines or AI platforms narrowly focused on security rather than deeper operational insights. This leaves critical improvements in efficiency, customer satisfaction, and revenue growth unrealised.\r\n\r\nSolution: Tenyks unlocks unprecedented insights by applying advanced multimodal Vision-Language Models (VLMs) capable of comprehensive video summarisation, retrieval, and visual question-answering. These VLMs are seamlessly combined with specialised analytics for emotion detection, activity recognition, object tracking, and even gait analysis. Our platform transforms ordinary visual footage into extraordinary operational intelligence.\r\n\r\nInitial Focus: Starting with Quick Service Restaurants (QSRs), we enable operations leaders to instantly understand customer flow, kitchen efficiency, staff productivity, and quality of service using their existing security camera footage—turning previously inaccessible data into direct operational improvements and clear financial ROI. For instance, insights from Tenyks can accelerate the speed of service significantly; even a 1-second improvement in service speed could generate $30,000 additional annual revenue per restaurant. For a multi-unit owner of 100 restaurants, that's an additional $3 million per year.\r\n\r\nTraction: We've recently secured our first Fortune 500 customer, joining other major enterprises, global system integrators, and high-growth startups trusting Tenyks for powerful, data-driven decision-making.\r\n\r\nWhy Tenyks: Our patented technology originates from the founders' PhD research at Cambridge University. Previously, we helped world-leading teams scale high-performing Vision AI in production. \r\n\r\nRecognition: Recognised as a Rising Star in Gen AI by Sifted, Best Product of the Year by Vision AI Alliance, and Company of the Year by Cambridge University (previously awarded to DeepMind).", "one_liner": "The World's Most Versatile Video Answering Engine ", "team_size": 11, "industry": "B2B", "subindustry": "B2B", "launched_at": 1630087785, "tags": [ "Computer Vision", "Video", "AI", "AI Assistant" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "B2B" ], "regions": [ "Bulgaria", "Europe", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/tenyks", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/tenyks.json", "year_founded": 2021, "city": null, "country": "BG", "linkedin_url": "https://www.linkedin.com/company/tenyks/", "x_url": "https://twitter.com/Tenyks_AI", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/tenyks", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7006298209ca0e4b566665242aadbc44f58b1d59.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 351439, "full_name": "Botty Dimanov", "title": "Founder/CEO", "founder_bio": "Dr. Botty Dimanov, CEO & Co-Founder of Tenyks, holds a PhD in Explainable AI for Vision from Cambridge University, invented US Patent for extracting concepts from visual information, and is a Forbes 30U30 honouree.\n\nTenyks is an award-winning Cambridge University spin-out backed by YCombinator.\nWe are building probably the world's most advanced Visual Intelligence Platform – think Snowflake for Visual data.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/botty-dimanov/", "x_url": "https://twitter.com/botty_dimanov", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1b50c89a9b232779d526ed936a1b186f488283ad.jpg" } ], "jobs": [], "news": [ { "title": "Doctor which cures Artificial Intelligence - bTV Media Group - bTV", "url": "https://www.btv.bg/shows/predi-obed/videos/doktorat-kojto-lekuva-izkustvenija-intelekt.html", "date": "Mar 15, 2023" }, { "title": "To Save the World from the Terminator", "url": "https://forbesbulgaria.com/2023/01/12/to-save-the-world-from-the-terminator/", "date": "Jan 12, 2023" }, { "title": "Tenyks lands $3.4m for human-AI interaction to fend off the ‘terminator’ | Business Weekly | Technology News | Business news | Cambridge and the East of England", "url": "https://www.businessweekly.co.uk/tech-trail/investment-tree/tenyks-lands-34m-human-ai-interaction-fend-%E2%80%98terminator%E2%80%99", "date": "Mar 07, 2022" }, { "title": "Cambridge startup Tenyks combats AI tech ‘terminator’ | Business Weekly | Technology News | Business news | Cambridge and the East of England", "url": "https://www.businessweekly.co.uk/news/startups/cambridge-startup-tenyks-combats-ai-tech-%E2%80%98terminator%E2%80%99", "date": "Dec 22, 2021" } ], "launches": [] }, { "id": 26254, "name": "Eventual", "slug": "eventual", "former_names": [ "Eventual Computing" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f429c16ee024d738c1b758e7c2d0ca5d811f204b.png", "website": "https://www.daft.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Every breakthrough AI application, from foundation models to autonomous vehicles, relies on processing massive volumes of images, video, and complex data. But today’s data platforms (like Databricks and Snowflake) are built on top of tools made for spreadsheet-like analytics, not the petabytes of multimodal data that power AI. As a result, teams waste months on brittle infrastructure instead of conducting research and building their core product.\r\n\r\nEventual was founded in 2022 to solve this. Our mission is to make querying any kind of data, images, video, audio, text, as intuitive as working with tables, and powerful enough to scale to production workloads. Our open-source engine, Daft, is purpose-built for real-world AI systems: coordinating with external APIs, managing GPU clusters, and handling failures that traditional engines can’t. Daft already powers critical workloads at companies like Amazon, Mobileye, Together AI, and CloudKitchens.\r\n\r\nWe’ve assembled a world-class team from Databricks, AWS, Nvidia, Pinecone, GitHub Copilot, Tesla, and more, quadrupling our size within a year. With backing from Y Combinator, Caffeinated Capital, Array.vc, and top angels from the co-founders of Databricks and Perplexity, we’re looking to double the team now. Join us—Eventual is just getting started.\r\n\r\nPlease note we are looking for someone who is willing and able to come into our San Francisco office in the Mission district 4 days / week.", "one_liner": "Building the AI data engine for any modality and scale", "team_size": 18, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1648154182, "tags": [ "Computer Vision" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2022", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/eventual", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/eventual.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/showcase/daftengine/", "x_url": "https://x.com/daftengine", "fb_url": null, "cb_url": null, "github_url": "https://github.com/Eventual-Inc/Daft", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f429c16ee024d738c1b758e7c2d0ca5d811f204b.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Brad Flora", "url": "https://www.ycombinator.com/people/brad-flora" }, "founders": [ { "user_id": 868547, "full_name": "Sammy Sidhu", "title": "Founder", "founder_bio": "Sammy Sidhu is co-founder and CEO of Eventual. Sammy's background is in High Performance Computing (HPC) and Deep Learning and has over a dozen patents/publications in the space. In the past, he has worked on high frequency trading on wall street, medical AI research at Berkeley and self-driving cars at both DeepScale (acquired by Tesla) and Lyft Level 5 (acquired by Toyota). Native to the Bay Area, Sammy graduated from UC Berkeley with a degree in Electrical Engineering and Computer Science.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/sammy-sidhu/", "x_url": "https://twitter.com/sammy_sidhu", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/010f79c5e4e670fda5bdf9d5e08d1fdb5d0aa979.jpg" }, { "user_id": 899443, "full_name": "Jay Chia", "title": "Founder", "founder_bio": "Jay is based in San Francisco and graduated from Cornell University where he did research in deep learning and computational biology. He has worked in ML Infrastructure across biotech (Freenome) and autonomous driving (Lyft L5), building large-scale data and computing platforms for diverse industries. Jay is originally from Singapore and spent 2 years as a tank commander in the military and then as the head of recruiting at a unicorn Singapore startup.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/chiajay", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b9857446224061e51df558517360b9d625ba4dc7.jpg" } ], "jobs": [ { "id": 79973, "title": "Software Engineer, Data Systems", "url": "https://www.ycombinator.com/companies/eventual/jobs/Gu6QHqN-software-engineer-data-systems", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $250K", "equity_range": "", "experience": "3+ years", "visa": "Will sponsor", "skills": [] }, { "id": 94775, "title": "Research Engineer, Multimodal Data", "url": "https://www.ycombinator.com/companies/eventual/jobs/AeLBWI3-research-engineer-multimodal-data", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $300K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 112578, "title": "Chief of Staff", "url": "https://www.ycombinator.com/companies/eventual/jobs/yl73w2M-chief-of-staff", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$130K - $200K", "equity_range": "", "experience": "3+ years", "visa": "Will sponsor", "skills": [] }, { "id": 114722, "title": "Software Engineer, Multimodal Backend Systems", "url": "https://www.ycombinator.com/companies/eventual/jobs/fjpa9hs-software-engineer-multimodal-backend-systems", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $250K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Python", "React", "TypeScript", "SQL", "Computer Vision", "LLMs", "Multimodal AI", "LiDAR" ] } ], "news": [ { "title": "How a data processing problem at Lyft became the basis for Eventual | TechCrunch", "url": "https://techcrunch.com/2025/06/24/how-a-data-processing-problem-at-lyft-became-the-basis-for-eventual/", "date": "Jun 24, 2025" }, { "title": "8 open source companies from YC Demo Day Winter ’22 – TechCrunch", "url": "https://techcrunch.com/2022/03/30/ycombinator-open-source-startups-winter-22-demo-day/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAALG7YnlRBDfnrptf_mMC9PJfcHhYtj0Fh3lI8gQksRAMaZAT8xWUhZY2Vra7Dah6TFviz9kRIBJbTwVinQEsfyvxTJSrSjt_OE95NgzsY_M1vzuPKjpoPMrIrsu0svkJlOAU1mDd9biWLYiX2vmT5-7guRNddgGtWZ9b9vCqVWzg", "date": "Mar 30, 2022" }, { "title": "Selected AI startups from YC’s Winter ’22 batch – TechCrunch", "url": "https://techcrunch.com/2022/03/30/selected-ai-startups-from-ycs-winter-22-batch/", "date": "Mar 30, 2022" } ], "launches": [] }, { "id": 26718, "name": "Cerrion", "slug": "cerrion", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a2cfaa4757206398a9d1678a216905dc2a248f11.png", "website": "https://www.cerrion.com/", "all_locations": "Zürich, ZH, Switzerland", "long_description": "Cerrion helps manufacturers automatically detect, understand and eliminate problems on their production lines using video-based Computer Vision. Our AI leverages standard CCTV cameras and learns how a manufacturing process looks like when things are going well and can automatically detect and track problems in real-time.\r\n\r\nFor example, one of our customers, a Pepsi supplier producing 500 bottles per minute now automatically detects and reacts to a fallen bottle before it starts blocking their production line. ", "one_liner": "Video AI to automatically detect and respond to production line…", "team_size": 16, "industry": "Industrials", "subindustry": "Industrials", "launched_at": 1659519771, "tags": [ "Deep Learning", "Computer Vision", "Video", "Manufacturing", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "Industrials" ], "regions": [ "Switzerland", "Europe" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/cerrion", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/cerrion.json", "year_founded": 2021, "city": "Zürich", "country": "CH", "linkedin_url": "https://www.linkedin.com/company/76510684/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/a2cfaa4757206398a9d1678a216905dc2a248f11.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Nicolas Dessaigne", "url": "https://www.ycombinator.com/people/nicolas-dessaigne" }, "founders": [ { "user_id": 1001364, "full_name": "Karim Saleh", "title": "Founder", "founder_bio": "Karim is the Co-Founder and CEO of Cerrion. Karim grew up in Egypt where he was a professional athlete and captain of Egypt's Water Polo national team. He represented his country at three world championships while simultaneously receiving an electrical engineering degree from ETH Zurich. Prior to founding Cerrion Karim has built Unicorn Labs which has helped 30+ startups build their MVPs. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/karim-saleh99/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/cb42488af359b4f1c8fea2fff34c6605bde806f2.jpg" }, { "user_id": 140429, "full_name": "Nikolay Kobyshev", "title": "Founder", "founder_bio": "Nikolay is the co-founder and CPO of Cerrion. He is in charge of building a product that Cerrion's users will truly love, and customer success.\nHe is a serial entrepreneur in the AI and CV space. Prior to Cerrion, Nikolay has built Spectando, an AI engine which generates photorealistic 3D models of real estate objects, and Assaia, a B2B Computer Vision solution which detects irregularities in airports’ ground operations.\nNikolay holds PhD and MSc degrees in Computer Vision from ETH Zurich.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nkobyshev/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/70bc945d7f0dadb179d9b7b52d4fa153e09efada.jpg" } ], "jobs": [], "news": [ { "title": "Cerrion raises $18M for factory intelligence platform", "url": "https://www.axios.com/pro/supply-chain-deals/2025/11/25/cerrion-raises-18m-for-factory-intelligence-platform", "date": "Nov 25, 2025" }, { "title": "Y Combinator-backed Cerrion raises $5 million in funding to boost AI Video tech in manufacturing - Tech.eu", "url": "https://tech.eu/2024/09/11/y-combinator-backed-cerrion-raises-5-million-in-funding-to-boost-ai-video-tech-in-manufacturing/", "date": "Sep 11, 2024" } ], "launches": [ { "id": 65196, "title": "Cerrion ⚡️ Computer Vision to automatically detect problems on production lines", "tagline": "Cerrion helps manufacturers automatically detect problems on their production lines using Computer Vision.", "body": "Hi everyone 👋 we are Karim, Nikolay and Michael, co-founders of [Cerrion](https://cerrion.com).\\\n\\\nWe help production teams understand when, where and why losses are happening on their production lines. Enabling them to act on problems faster, implement targeted improvement actions and understand the root cause of problems in a matter of minutes.\n\n**The problem: Production teams today struggle to track and eliminate deviations on their production lines**\n\nLack of transparency leads to around $1 trillion in annual preventable losses for the global manufacturing industry. Production teams today struggle to understand when losses on their production lines will happen and why they happen. To stay on top of your production a lot of trained and experienced eyes are needed. Given that today there is a 40% labor shortage in the US manufacturing industry alone, it is now more important than ever to help the existing workforce eliminate deviations on their production lines\n\n**The solution: Automatically monitor production lines for deviations**\n\nOur plug and play visual AI learns how a manufacturing process looks when things are going well and can automatically detect and track problems in real-time. This:\n\n\\- Eliminates the need for human monitoring;\\\n\\-Allows to detect subtle issues on the production line  before they become big;\\\n\\-Facilitates root cause analysis remotely and in a matter of minutes.\n\n![uploaded image](/media/?type=post&id=65196&key=user_uploads/1001364/919f2fc1-c895-4acd-ad72-16c22c3d17fc)\n\nOur AI in action ^\n\n**Can I deploy Cerrion in my production?**\\\nYes! Cerrion is already deployed in 4 countries and we are expanding. You can reach me at [karim@cerrion.com](mailto:karim@cerrion.com) to make it happen.", "url": "https://www.ycombinator.com/launches/GxY-cerrion-computer-vision-to-automatically-detect-problems-on-production-lines", "votes": 232, "created_at": "2022-08-08T12:43:31.235Z" } ] }, { "id": 26802, "name": "Apply Design", "slug": "apply-design", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/731fd6f22634f935a03484945b5b569dae8c8ed9.png", "website": "http://www.applydesign.io", "all_locations": "Tel Aviv-Yafo, Tel Aviv District, Israel", "long_description": "Apply Design is a leading AI-powered virtual staging app that empowers real estate professionals to showcase the full potential of their properties to prospective buyers, helping them sell homes faster and for higher prices. \r\n\r\nWe virtually stage over 10,000 properties monthly, transforming images of vacant or outdated spaces into fully furnished, captivating property photos. Our proprietary AI-powered software is the only solution capable of generating photorealistic designs in one click while also enabling instant customization of any detail with complete control.\r\n\r\nThis is a $10B market opportunity, given the 50M properties sold or rented annually in North America and Europe alone. ", "one_liner": "Helping realtors showcase the full potential of their properties", "team_size": 4, "industry": "Real Estate and Construction", "subindustry": "Real Estate and Construction", "launched_at": 1655775419, "tags": [ "Generative AI", "SaaS", "Computer Vision", "Proptech", "E-commerce" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "Real Estate and Construction" ], "regions": [ "Israel", "Middle East and North Africa", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/apply-design", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/apply-design.json", "year_founded": 2021, "city": "Tel Aviv-Yafo", "country": "IL", "linkedin_url": "https://www.linkedin.com/company/applydesign/", "x_url": "", "fb_url": "https://www.facebook.com/ApplyDesign.io/", "cb_url": "https://www.crunchbase.com/organization/apply-design", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/731fd6f22634f935a03484945b5b569dae8c8ed9.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1014282, "full_name": "Asaf Amit", "title": "Founder", "founder_bio": "Co-founder & CEO of Apply Design", "is_active": true, "linkedin_url": "http://www.linkedin.com/in/asaf-amit-734b99142", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ab9569df888c9a5622cb389369898e05bec598e2.jpg" }, { "user_id": 1018469, "full_name": "Yaniv Knoll", "title": "Founder", "founder_bio": "Co-founder & CTO of Apply Design", "is_active": true, "linkedin_url": "http://www.linkedin.com/in/yaniv-knoll-954535121", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/225657b5780a29d51867188e3811766bcd329b89.jpg" } ], "jobs": [], "news": [ { "title": "Y Combinator’s newest member wants to revolutionize the online real estate game | Ctech", "url": "https://www.calcalistech.com/ctechnews/article/h1oangwri", "date": "Nov 04, 2022" }, { "title": "Israeli start-up Apply Design creates virtual interior designs with AI", "url": "https://www.jpost.com/business-and-innovation/tech-and-start-ups/article-721367", "date": "Nov 03, 2022" } ], "launches": [ { "id": 64479, "title": "🛋 Apply Design - Shopify for Property Listings", "tagline": "Sell your property 30% faster with a 5% higher price by automatically creating an amazing online property website", "body": "[Apply Design](https://applydesign.io/) helps homeowners sell their property 30% faster with a 5% higher price.\n\nWe do this through our custom website creator that showcases your home's potential in a way that is personalized to the buyer.\n\nFor example, when a young married couple with a baby visits the website we host, instead of seeing a dull property listing on Zillow with empty pictures or old furniture, the family sees realistic and personalized virtual interior designs – such as a home office and a baby room – which we automatically generate for them. They can then play with those designs and see how that home fits their needs and tastes.\\\n\\\nWe entered the market with a product that allows anyone to create virtual realistic interior designs for empty property images automatically.\n\n![uploaded image](/media/?type=post&id=64479&key=user_uploads/1014282/0c4976d5-00b5-45dd-85c0-decb4111d476)\n\n### **The Problem**\n\nAn excellent online property listing can help you sell your property faster and for a higher price.\n\nBut property listing websites haven’t changed for the last 20 years and are very one-dimensional, although prospective buyers have different needs and tastes.\n\nBy using computer graphics and ML, Apply Design created a way to automatically create realistic virtual interior designs that enable the homeowner to easily create a website that showcases the home's potential in a way that is personalized to the buyer. A way that sells better than Zillow.\n\n### **About Us**\n\nYaniv Knoll and I are based in Tel Aviv and met in the IDF in a small intelligence unit specializing in 3D solutions for special operations. Our work was recognized with the highest intelligence award for 2015.\\\nFor the last 10 years, we’ve built 3D and computer vision-based solutions in military, HR, construction, and real estate.\n\nThe idea was born last year when my parents tried to sell the apartment that I grew up in. A month went by with no offers after the empty pictures sat on Zillow waiting for anyone to show interest, so I created a dedicated website showing how the home fits different family structures and purposes, using editable interior realistic designs for each room of the house. Within days of prospective buyers using our product, we went from no offers to three, and a 10% higher asking price than expected.\n\n### **I want it! How do I try it?**\n\nIn case you have or plan to publish a property listing (or if you just want to test it out, it's really fun! ), you can try our first product at [applydesign.io](http://applydesign.io), which allows you to upload an image and add realistic furniture to show the potential of your property.\\\nThe best part is that the first image is for free 💸!\n\n---\n\nIf you do try it out, make sure to **send me feedback** about the experience and the potential you see.\n\n**_If you know any realtors, property managers, or even friends and family who are about to sell or rent a property, please tell them about Apply Design. 🙂_**\n\nThanks!", "url": "https://www.ycombinator.com/launches/Glz-apply-design-shopify-for-property-listings", "votes": 108, "created_at": "2022-07-10T14:34:11.660Z" } ] }, { "id": 27221, "name": "CAPSULE", "slug": "capsule", "former_names": [ "Dryftwell", "GLIMPSE" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/19f195e9a25d1cff1f914f65ceb3e7458c6d52b9.png", "website": "https://www.shopcapsule.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "CAPSULE is a mobile app that makes it easy to save and buy the things you find on any social media platform. Just snap a screenshot of anything you like, from any platform, and we search the entire internet to instantly give you shoppable links. \r\n\r\nInstead of searching hundreds of websites and sifting through thousands of products on your own, CAPSULE lets you find inspiration from anywhere and uses advanced machine learning to return results that feel like magic. ", "one_liner": "Buy anything you find on social media", "team_size": 4, "industry": "Consumer", "subindustry": "Consumer -> Apparel and Cosmetics", "launched_at": 1661989948, "tags": [ "Generative AI", "Machine Learning", "Computer Vision", "E-commerce", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Inactive", "industries": [ "Consumer", "Apparel and Cosmetics" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/capsule", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/capsule.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/19f195e9a25d1cff1f914f65ceb3e7458c6d52b9.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 917276, "full_name": "Michele Van Ruiten", "title": "Founder", "founder_bio": "", "is_active": false, "linkedin_url": "http://www.linkedin.com/in/michelevanruiten/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d93fe8ad5e2cfc5d231a7e28a3bc3d66c96d1168.jpg" }, { "user_id": 276919, "full_name": "Kelsey Pedersen", "title": "Founder", "founder_bio": "", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/kelseypedersen/", "x_url": "https://twitter.com/kelsey_pedersen", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3f3b491709d3fd754dd8270d093c773cbbdd12d2.jpg" } ], "jobs": [], "news": [ { "title": "Capsule, a New 'Shazam' for Fashion App, Could Change the Way You Shop - Business Insider", "url": "https://www.businessinsider.com/capsule-shazam-for-fashion-app-change-the-way-you-shop-2024-8", "date": "Aug 12, 2024" }, { "title": "This new 'Shazam' for fashion app could change the way the world shops online", "url": "https://www.yahoo.com/tech/shazam-fashion-app-could-change-102802967.html", "date": "Aug 12, 2024" }, { "title": "Our 11 favorite companies from YC's S22 Demo Day", "url": "https://techcrunch.com/2022/09/07/11-favorite-yc-demo-day-s22-startups/", "date": "Sep 07, 2022" } ], "launches": [] }, { "id": 27828, "name": "Scanbase", "slug": "scanbase", "former_names": [ "Scanbase", "Inc." ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c9177f461ffabba1796fab772ec9f376ad91bf4e.png", "website": "https://www.scanbase.com", "all_locations": "Los Angeles, CA, USA", "long_description": "Scanbase makes it easy for medical companies to convert photos of rapid diagnostic tests into results. We do this by providing a simple API that any medical company can access.\r\n", "one_liner": "The API for Diagnostic Test Analysis (COVID-19, FLU, RSV, STD,…", "team_size": 10, "industry": "Healthcare", "subindustry": "Healthcare -> Diagnostics", "launched_at": 1670991822, "tags": [ "Machine Learning", "Computer Vision", "Health Tech", "Telemedicine", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Healthcare", "Diagnostics" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/scanbase", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/scanbase.json", "year_founded": 2022, "city": "Los Angeles", "country": "US", "linkedin_url": "https://www.linkedin.com/company/scanbases/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c9177f461ffabba1796fab772ec9f376ad91bf4e.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Tom Blomfield", "url": "https://www.ycombinator.com/people/tom-blomfield" }, "founders": [ { "user_id": 1162081, "full_name": "Jeffrey Lange", "title": "Founder", "founder_bio": "Jeffrey is currently the CEO of Scanbase, a seed stage diagnostic scanning API used by at-home test manufacturers. He is a graduate of the Rhode Island School of Design.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/langejeff/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/88486d8d8917eb91776d16c6f3b5540f5e751853.jpg" }, { "user_id": 1150701, "full_name": "Steve Roger", "title": "Founder", "founder_bio": "CTO of Scanbase with 10+ years building enterprise level APIs, and products. Steve holds a degree in CS from UC Berkeley. Ex-LinkedIn.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/steveroger/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2ef49967996d38086a75f694673b3814fe366c7b.jpg" } ], "jobs": [], "news": [ { "title": "Y Combinator’s Most Recent Winter 2023 Batch Shows A Shift To AI, DevTools And B2B ", "url": "https://news.crunchbase.com/venture/y-combinator-winter-2023-shift-ai-devtools-b2b/", "date": "Apr 12, 2023" }, { "title": "Our favorite startups from YC’s Winter 2023 Demo Day — Part 2 | TechCrunch", "url": "https://techcrunch.com/2023/04/06/y-combinator-demo-day-2023-favorites-part-two/", "date": "Apr 06, 2023" } ], "launches": [ { "id": 68414, "title": "Scanbase API - Converting Diagnostic Tests to Results, Next Steps, and Treatment", "tagline": "COVID-19, Flu, Pregnancy, RSV, STD/STI, HIV, CKD, UTI, and many more...", "body": "![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/724cd23c-f4df-4100-954e-379c4f248340)\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/047c2be5-f90d-4a0b-acc6-693a32527879)\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/d6a6fc05-b35f-4382-8d04-5a1c04c5bec0)\n\n[Book a demo](https://www.scanbase.com/schedule-a-demo) and learn how Scanbase can enhance your diagnostic program.\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/9151ad3b-aee8-469f-85a8-865f70e98b45)\n\nWatch a demo of a Scanbase’s algorithm interpreting a **low positive** test!\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/bb4bb5c4-0806-4417-a3e9-a05347115195)\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/c31413ed-9102-4eb6-acfd-f4016a843ab4)\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/b9e1ac86-a480-48f7-9663-57c6dc724723)\n\nInterested in digitizing your diagnostic tests and don’t have an app? Reach out to us at [info@scanbase.com](mailto:info@scanbase.com) to learn how we’re helping diagnostics companies digitize from the ground up.\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/4378e1d7-15cd-4dfa-88af-66f0c64babeb)\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/821f585b-f832-4df7-8b91-21ca677a0d14)\n\nDon’t see the test you’re looking for? Let know at [info@scanbase.com](mailto:info@scanbase.com).\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/2809d76e-b3e0-405f-925c-82af759f4115)\n\n[Book a demo](https://www.scanbase.com/schedule-a-demo) and learn how Scanbase can enhance your diagnostic program.\\\n\\\n**We believe that everyone deserves access to the best healthcare technology has to offer.** Founded in 2022 and based in San Francisco, California, Scanbase is an API that allows medical companies to enable smartphone-based computer vision quickly so anyone anywhere can analyze their at-home test results with confidence.\\\n\\\nIf you are interested in learning more or joining our team, shoot us an email at [info@scanbase.com](mailto:info@scanbase.com) and we’ll get back to you right away.", "url": "https://www.ycombinator.com/launches/HnS-scanbase-api-converting-diagnostic-tests-to-results-next-steps-and-treatment", "votes": 109, "created_at": "2023-01-13T09:08:29.284Z" } ] }, { "id": 27860, "name": "Dream3D", "slug": "dream3d", "former_names": [ "MettaSpace" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/dc5c128914787f1ad09c34983b9c40c1200da598.png", "website": "https://dream3d.com", "all_locations": "New York City, NY, USA", "long_description": "Dream3D is an AI research lab that builds tools to help humanity predict the future.", "one_liner": "We build tools to help humanity predict the future.", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1677865645, "tags": [ "Generative AI", "Computer Vision", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/dream3d", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/dream3d.json", "year_founded": 2022, "city": "New York City", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/dc5c128914787f1ad09c34983b9c40c1200da598.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 251860, "full_name": "Tony Francis", "title": "Founder", "founder_bio": "Tony is a second-time YC founder. Currently, he is the founder & CEO of Dream3D building generative models to simulate virtual worlds. His previous company, Inokyo, built the first AI-checkout retail stores. In between, he lead the development of the ML platform at Clubhouse. ", "is_active": true, "linkedin_url": "http://linkedin.com/in/tony-f", "x_url": "https://twitter.com/_tonyfrancis", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f5122876a10bca071b9861a58e57029896a31f68.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 69920, "title": "Dream3D 😈 - AI-powered 3D design", "tagline": "Studio-quality computer graphics made easy", "body": "Hey, y’all! Tony here from Dream3D– excited to share what we’ve been working on!\n\n**Dream3D is an AI-powered 3D design tool that makes it easy to create studio-quality computer graphics.**\n\n### The Problem\n\n**Creating 3D renders is an expensive and time-consuming process that can take teams of highly trained specialists months, if not years, to produce a final result.**\n\nIn current rendering methods, input quality = output quality. A designer or artist needs to spend days building a scene, component by component, to get a final rendered result. \n\n**This leads to**\n\n* 🐢 Slow and costly iteration cycles as you nail down your creative vision\n* 💰 Hundreds (if not thousands) of dollars spent purchasing assets, textures, and shaders– many of which never even make the final cut\n* 🤬 Complex and difficult-to-use tools required for building detailed scenes\n\n### How we fix this\n\n**Dream3D replaces complex workflows with text-driven commands and intuitive interfaces powered by our custom AI models.** \n\nUnlike existing generative tools, we give users explicit control over their scene, unlocking professional use-cases.\n\nWe let designers quickly build out 3D scenes by describing them in plain English– “a jetski in front of a building.“ We then let them transform that scene into a high-quality image with just another description of what they want– “photorealistic yellow jetski, evening sun, 80s magazine ad”\n\nHere’s an example of transforming a simple car scene into production-ready concept art:\n\n![uploaded image](/media/?type=post&id=69920&key=user_uploads/251860/f63e5f85-b22c-451b-aa59-1846384eb605)\n\n![uploaded image](/media/?type=post&id=69920&key=user_uploads/251860/3e190d62-d65d-48be-9e26-7639ee1ca06c)\n\nOr a full landscape from just a couple of boxes:\n\n![uploaded image](/media/?type=post&id=69920&key=user_uploads/251860/84be4590-2db2-40a6-b61e-5672a55e828e)\n\n![uploaded image](/media/?type=post&id=69920&key=user_uploads/251860/067a0ef7-f509-4de2-83af-fb30c99254d7)\n\n\\\nThis means you can quickly iterate through multiple concepts and styles, maintaining the same scene composition, without having to spend time & energy building each scene from scratch.\n\nIt's fast and fluid so you can focus on what you want to create.\n\n### Looking Forward\n\nRight now, we let designers create static renders. But soon, we’ll be adding support for animation and video export. \n\nIn the longer term, Dream3D will be a complete 3D creative suite– letting you create full games, movies, and simulated worlds.\n\n## Our Story\n\nPrior to Dream3D, I led the development of the machine learning platform at Clubhouse and my previous company, Inokyo (S18), built computer vision to automate retail checkout. \n\nI first got interested in computer graphics and storytelling while I was studying at Carnegie Mellon. But, I found my creativity stifled by the complexity of the tools.\n\nNow, we’re combining our experiences building prosumer tools and bringing machine learning research to production to make it easy for anyone to create studio-level computer graphics and build the tool I always wish I had.\n\n### The Ask\n\nIf you do 3D design work (or want to), sign up [here](https://hkqvoariw92.typeform.com/to/QJv3ghPN)!\n\nOr if you’re interested in researching hard problems across computer graphics and AI, and/or building delightful tools, send me an email at [tony@dream3d.com](mailto:tony@dream3d.com)", "url": "https://www.ycombinator.com/launches/IBk-dream3d-ai-powered-3d-design", "votes": 108, "created_at": "2023-03-03T18:30:17.701Z" } ] }, { "id": 28114, "name": "rex.fit", "slug": "rex-fit", "former_names": [ "Babylon AI", "BabylonAI", "Rex" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c618208d2cb852eeff16409fb7e787701c395b91.png", "website": "https://www.rex.fit", "all_locations": "San Francisco, CA, USA", "long_description": "The DROP by Rex.Fit is an AI wearable camera that automates nutrition tracking using computer vision. It has garnered more than 93k USD in preorders (~450 units) since its launch last month. The nutrition estimation and recommendation tech behind the DROP is already integrated as an API for fitness chains, generating hundreds of thousands of dollars in yearly revenue for them. The DROP is built by brothers, sports nutritionists and computer vision engineers from the Swiss Federal Institute of Technology (ETH Zurich).", "one_liner": "Automating nutrition tracking.", "team_size": 2, "industry": "Consumer", "subindustry": "Consumer", "launched_at": 1674733041, "tags": [ "Hard Tech", "Computer Vision", "Consumer Health Services", "Fitness", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Consumer" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/rex-fit", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/rex-fit.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/babylonai/", "x_url": "https://twitter.com/rexfit_app", "fb_url": "https://www.instagram.com/rexfit.app/", "cb_url": "https://www.crunchbase.com/organization/babylonai", "github_url": "https://github.com/thebabylonai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c618208d2cb852eeff16409fb7e787701c395b91.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 1207294, "full_name": "Ahmad Roumie", "title": "Founder", "founder_bio": "On a mission to fully automate nutrition tracking - Order the DROP on kickstarter. Roboticist and calisthenics athlete. Previously worked on autonomous retail stores using computer vision, also worked on autonomous robots ((legged, UAVs, and crawlers) ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ahmad-roumie-1a59a417a/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/75d60a8965ff22709d30ee5a749911dd78f6d2d3.jpg" } ], "jobs": [], "news": [ { "title": "Food tracking just got lazy — in the best way possible — with this wearable | Fox News", "url": "https://www.foxnews.com/tech/food-tracking-just-got-lazy-best-way-possible-wearable", "date": "Dec 22, 2024" } ], "launches": [ { "id": 86073, "title": "The Drop: Fully Automated Nutrition Tracker", "tagline": "The Drop automatically tracks your calories and macros as you eat.", "body": "# TL;DR\n\nHi there! We’re Rangel and Ahmad and today we’re excited to be launching [the Drop on Kickstarter. The Drop is our wearable AI pendant and app](https://www.kickstarter.com/projects/rangel/the-drop-fully-automated-nutrition-tracker) that automatically tracks what you eat throughout the day. \n\n[Watch the launch video here!](https://youtu.be/wa0rATqlnQU)\n\n![uploaded image](/media/?type=post&id=86073&key=user_uploads/635063/1041e0f0-9ce8-42e6-ad9e-885502231d89)\n\n# How it works\n\n![uploaded image](/media/?type=post&id=86073&key=user_uploads/635063/356232f6-9e59-4216-afa8-b0f7c808b541)\n\n[The Drop](https://www.getdrop.ai) uses **computer vision and AI** to know when you're eating food. Once the smart AI chip on the device classifies something as food, the LED light turns on and the device starts recording what you're eating. Afterwards, the cropped image of the meal will be sent to the cloud for analysis. The algorithms will then break down your meal into **calories, protein, carbs and fats**, which you could see right away in the app on your phone.\n\n![uploaded image](/media/?type=post&id=86073&key=user_uploads/635063/3d7365f6-f033-44b4-9867-59268af954c8)\n\n# Why are we building this?\n\nWe launched [Rex Nutribot on WhatsApp](https://www.rex.fit) in December 2023. We made calorie tracking as simple as sending a photo or a text to a friend. More than 20,000 users later, we learned that nutrition tracking is still not a solved problem. Every solution on the market requires **too much input from the user**, and most don't manage to create the habit to track what they eat. \n\nA real solution needs to be as **user-friendly and intuitive** as fitness tracking is. It needs to be a wearable that you can just put on in the morning and forget about. For that reason we developed The Drop: the first fully automated nutrition tracker.\n\nBy fully automating meal tracking we want to **enable everyone to learn what and how much they eat**, understand their eating habits and transform their diet to optimize for health and longevity.\n\n# The team\n\n![uploaded image](/media/?type=post&id=86073&key=user_uploads/635063/79e0e320-e8ee-4934-a7e1-0a4e5ebb6627)\n\nOur core team consists of Harvard and ETH Zurich engineers and we are backed by two of the largest and most well-known incubators: [Y Combinator](https://ycombinator.com) and [Entrepreneur First](https://www.joinef.com). Our co-founder and CTO, [Ahmad](https://www.rex.fit/contacts/ahmad), is a computer vision engineer who previously worked in autonomous retail with a Masters in Robotics from ETH Zurich, and our co-founder and CEO, [Rangel](https://www.rex.fit/contacts/rangel), is an AI engineer who graduated with a B.A. in Computer Science from Harvard. We met at Entrepreneur First in Paris in 2022 and have been working together since. \n\n# Get the Drop\n\nYou can find more information and [pre-order the Drop by backing us on Kickstarter](https://www.kickstarter.com/projects/rangel/the-drop-fully-automated-nutrition-tracker). Pricing starts at **199 USD** and it is available in **3 colors**: carbon black, metallic blue and gold. Early adopters and beta testers will start receiving their devices in the summer. \n\n![uploaded image](/media/?type=post&id=86073&key=user_uploads/635063/bc268686-845b-4419-9d5c-ee88333bc390)\n\n", "url": "https://www.ycombinator.com/launches/MOH-the-drop-fully-automated-nutrition-tracker", "votes": 27, "created_at": "2024-12-05T15:21:06.246Z" }, { "id": 75089, "title": "Rex.fit 🦖: Your personal AI workout and nutrition coach", "tagline": "The AI that helps you get and stay in shape.", "body": "Hi everyone! We're excited to share with you [**Rex.fit**](http://Rex.fit). We merge the addictive nature of gaming with the benefits of physical fitness. And we can't wait for you to try it!\n\n![uploaded image](/media/?type=post&id=75089&key=user_uploads/635063/639f56b0-2380-405b-95ce-fcca5714280b)\n\n## 💪 **What is **[**Rex.fit**](http://Rex.fit)**?**\n\nWe gamify workouts using computer vision and AI to provide immediate feedback on your workouts. By tracking your workout intensity, calorie burn, and repetitions in real-time, we ensure you stay engaged, challenged, and hungry for more. \n\n![uploaded image](/media/?type=post&id=75089&key=user_uploads/635063/44015f5d-d594-4346-85db-452f78d8dc49)\n\nWe also offer a nutrition chatbot that simplifies food logging through natural language processing, making it as easy as texting a friend to track your dietary intake.\n\n🎮 **How Does It Work?**\n\n1. Head over to [www.rex.fit](http://www.rex.fit), sign up and choose a workout. The weekly challenge or the fitness test are great places to start.\n2. Make sure your camera is on and stay around 1.5 meters (\\~5 feet) away from your phone or laptop - our AI will track your movements, count your reps and measure your intensity.\n3. You’ll get a final score at the end of each workout based on your performance. Head the leaderboard by beating your own score and that of your friends.\n4. Dive into our variety of gamified workouts. Whether you're into HIIT, or strength training, we have a workout for you! The weekly challenge is a great place to start!\n5. Visualize and track your progress using the profile page:\n\n ![uploaded image](/media/?type=post&id=75089&key=user_uploads/635063/c8de0795-3972-4ccd-8cab-6f18656d3efc)\n\n6. Earn points, beat your high score, challenge friends, and most importantly, get fit while having fun!\n\n## 🍲 **AI-Powered Nutrition Tracking!**\n\nWe understand fitness isn't just about workouts, so we're also introducing a Telegram chatbot you can text to track your meals.\n\n![uploaded image](/media/?type=post&id=75089&key=user_uploads/635063/f534de8b-1eaf-45b6-bb41-3b2683873069)\n\n* **Text your daily meals** to the bot, whether they’re home-cooked, take-out, or store-bought.\n* We use LLMs to break meals down into ingredients and then query our DB to estimate calories, protein, carbs, fats, and other micronutrients, giving you a comprehensive view of your diet.\n* Say goodbye to manual logging and inaccurate guesses. Let our AI take care of your nutritional tracking.\n\n🌟 **Why **[**Rex.fit**](http://Rex.fit)**?**\n\n* **Engagement**: Turn workouts into games, making them more engaging and fun.\n* **Accuracy**: Ensure your workouts are effective with real-time feedback.\n* **All-in-one**: Combine workouts and nutrition in one platform.\n* **Community**: Challenge and compete with friends or the [Rex.fit](http://Rex.fit) community.\n\n🙏 **Join Us On This Journey** We're on a mission to make fitness accessible, fun, and effective for everyone. But we need your help. Check out [www.rex.fit](http://www.rex.fit), try our platform, and give us your feedback. If any of you are or know well fitness trainers, dieticians, or other people who would like their content to be on our platform, please reach out to us.\n\n🔗 **Links & Resources**\n\n* 🏋️‍♂️ Gamified Workouts: [www.rex.fit](http://www.rex.fit)\n* 🤖 Telegram Bot: [t.me/rex_nutribot](http://t.me/rex_nutribot) (you need to sign up on Rex first to gain access)\n* 👍🏿 Like and follow us on Instagram: \n* 📞 Chat with us: \n\nCheers to a healthier, more fun fitness journey! 💪🎮\n\n---\n\n_Note: Always remember to prioritize safety. Ensure you're in a suitable environment before starting a workout, and consult with a healthcare professional if you have any medical concerns._", "url": "https://www.ycombinator.com/launches/JX7-rex-fit-your-personal-ai-workout-and-nutrition-coach", "votes": 169, "created_at": "2023-09-22T18:21:30.459Z" }, { "id": 68843, "title": "Babylog: A lightweight open-source logging library for computer vision", "tagline": "Easily log image and prediction data from edge devices", "body": "Hi everyone,\n\nWe’re excited to launch our new **open-source logging library for ML and computer vision teams**. With Babylog, we aim to empower engineers to **easily log raw image and prediction data from edge devices to the cloud (or on-prem servers).**\n\n![uploaded image](/media/?type=post&id=68843&key=user_uploads/635063/04dbb932-16fa-4e04-bc2c-b7a14f273ebf)\n\n## Problem\n\nYou need to know what your models are running on in the real world. Setting up that infrastructure takes away precious time and money. You need a standard for logging your data so that you know how your models perform. **Babylog (bah-bee-log) provides a standard where all device, model, and prediction information are coupled.**\n\n* If you don’t have a data stream from production, you have no idea what your models are seeing and no way to evaluate their performance.\n* If something suddenly changes in the real world and with it the distribution of your inputs, things could go very wrong so you’d likely want to know (i.e. stop signs suddenly turning blue = Tesla becoming a murder machine)\n\n## Our asks\n\n* [**Go check us out on GitHub and don’t forget to star us ⭐.**](https://github.com/thebabylonai/babylog)\n* Check out our [website](https://www.babylonai.dev), and play with our library and mock dashboard.\n* If you’re interested, have feedback or criticism, or think we should talk to someone, please either:\n * schedule a chat with us: \n * shoot us an email at [founders@babylonai.dev](mailto:founders@babylonai.dev)", "url": "https://www.ycombinator.com/launches/HuN-babylog-a-lightweight-open-source-logging-library-for-computer-vision", "votes": 20, "created_at": "2023-01-30T18:19:49.406Z" } ] }, { "id": 28859, "name": "MICSI", "slug": "micsi", "former_names": [ "MICSI (Microstructure Imaging, Inc)" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/29d35560d45b90c5227b12877556942576e43627.png", "website": "https://www.micsi.co/", "all_locations": "New York City, NY, USA", "long_description": "MICSI is introducing AI software that doubles resolution and halves scan time. This breakthrough enables imaging centers to significantly enhance their capacity and patient throughput, potentially saving countless lives and generating an additional $2 million of revenue per MRI scanner. Our initial offering serves as a stepping stone toward the company’s larger vision of transforming the MRI into a truly quantitative instrument that is capable of providing highly reproducible data for more accurate diagnoses and patient management. ", "one_liner": "Higher resolution MRI with faster scan times.", "team_size": 2, "industry": "Healthcare", "subindustry": "Healthcare -> Diagnostics", "launched_at": 1687381964, "tags": [ "Computer Vision", "Medical Devices", "Healthcare" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "Healthcare", "Diagnostics" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/micsi", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/micsi.json", "year_founded": 2018, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/microstructure-imaging-inc/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/29d35560d45b90c5227b12877556942576e43627.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1352304, "full_name": "Gregory Lemberskiy", "title": "Founder", "founder_bio": "Gregory Lemberskiy (CEO) co-founded MICSI with goal of bringing cutting edge image processing tools, that he encountered in the research setting into clinical practice. His PhD work (2019) focused on algorithm development for image enhancement and biophysical modeling of the MRI signal. His methods have been used to enable high-end quality on low field MRI systems through noise reduction (MICSI’s first product) and use MRI to characterize the physical properties of prostate glandular lumen.", "is_active": true, "linkedin_url": "https://linkedin.com/in/gregory-lemberskiy", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/83bae6427877568348313403492a3c5c90b7ff0c.jpg" }, { "user_id": 1352419, "full_name": "Benjamin Ades-Aron", "title": "Founder", "founder_bio": "I got my PhD in electrical engineering in 2022 from NYU, where I specialized in developing machine learning based computer vision algorithms for the visualization of MRI data. I specialize in developing software to process and route diffusion and functional MRI data, with a goal of improving image signal, reducing noise, and producing clinically viable diagnostic imaging biomarkers.", "is_active": true, "linkedin_url": "https://linkedin.com/in/benjamin-aron-b0313349", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/894ba3227d1a30f75b7086a428e64dbf24f4a090.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 96226, "title": "MICSI-PET: Anatomical Positron Emission Tomography", "tagline": "Making PET imaging fast, anatomical, and scalable for Alzheimer’s care.", "body": "TLDR; MICSI-PET makes PET imaging fast, anatomical, and scalable for Alzheimer’s care.\n\n![uploaded image](/media/?type=post&id=96226&key=user_uploads/1352304/a5488138-be4b-4093-86c1-1b2663488117)\n\n**Problem:** **PET is the bottleneck capping the entire Alzheimer’s market.**\n\n![uploaded image](/media/?type=post&id=96226&key=user_uploads/1352304/40ba7516-1b46-454a-9a26-98fb91fe3169)\n\nDisease modifying Alzheimer’s drugs like Leqembi and Kisunla are here, and they are projected to generate $5-8B/year by 2030. However, to get these drugs, patients need a positive amyloid PET scan.\n\nThis is an ambitious figure that is limited by the fact that there are only 2500 PET/CT scanners in the US, where the average age is 14 years old. PET scanners have a predictable 4-5% drop in sensitivity (or detected count rate) per year \\[Watanuki et al. Ann Nucl Med. 2010\\], suggesting a loss of half of the sensitivity since its initial install. Operationally, this sensitivity loss forces centers to lengthen acquisitions or increase dose just to preserve image quality, reducing throughput or margins right as amyloid PET demand is exploding. It’s also unrealistic to assume that the existing PET fleet is built for imaging Alzheimer’s at scale. PET systems are also heavily utilized for oncology, cardiology, infection, and presurgical planning, where 74% of all PET scans use FDG, a general purpose tracer for cellular metabolism. \n\n**Solution: Transform aging PET fleets into Anatomical PET with MICSI-PET.**\n\n![uploaded image](/media/?type=post&id=96226&key=user_uploads/1352304/be3b873c-3dd7-4326-8447-316161d179db)\n\nMICSI-PET is a turnkey upgrade that dramatically enhances PET image quality by using the MRI as an anatomical guide. This process does not require any additional imaging as all patients should have received an MRI prior to amyloid PET imaging. Operationally, this upgrade removes the primary barrier for scaling Alzheimer’s therapies by (1) enabling high-end PET imaging on low-end or aging PET scanners; (2) enabling shorter PET examinations leading to greater throughput; (3) minimizing ambiguity of positive/negative amyloid PET imaging by showing clearer boundaries between gray and white matter:\n\n![uploaded image](/media/?type=post&id=96226&key=user_uploads/1352304/90dd7722-405d-4a4f-967b-4ec6df183ff6)\n\n**Figure:** Coregistered conventional PET and MICSI-PET amyloid images across a range of amyloid burdens, highlighting differences in apparent cortical sparing: **left, amyloid-negative** with strong gray/white contrast; **right, amyloid-positive** with reduced gray/white contrast.\n\n**Addressing the Radiologist Workforce Crisis:** Scaling Alzheimer's care isn't just a hardware problem; it's a workforce problem. AD imaging is about to become a high-volume modality layered onto a nuclear medicine workforce where 54% of radiologists already report burnout.\n\nBeyond anatomical PET, MICSI-PET is designed to streamline diagnosis through automatic SUVR/Centiloid reporting with built-in QC.\n\n![uploaded image](/media/?type=post&id=96226&key=user_uploads/1352304/b48c9a16-d7b6-4fe9-bf74-509653e159dd)\n\nFast and anatomical PET is just the start. The same MR-guided upgrade would generalize across tracers, and eventually across doses, elevating the global standard of PET toward a new default: molecular imaging that’s both specific and anatomically precise. \n\nOur first product, MICSI-RMT, is already FDA-cleared and in the market. MICSI-PET is currently in clinical validation and we are preparing our FDA submission.\n\n**Ask:** We are expanding pre-FDA clearance pilot collaborations to validate MICSI-PET across sites and workflows, and we are closing a seed extension to support multi-center validation and early installs. If you lead a PET imaging program or invest in medical imaging and Alzheimer’s care, we would love to connect.  \n\n**Email Us:** \n\n[**Gregory.Lemberskiy@micsi.com**](mailto:Gregory.Lemberskiy@micsi.com)\n\n[**Benjamin.Adesaron@micsi.com**](mailto:Benjamin.Adesaron@micsi.com)", "url": "https://www.ycombinator.com/launches/P22-micsi-pet-anatomical-positron-emission-tomography", "votes": 16, "created_at": "2025-12-16T00:02:25.484Z" }, { "id": 73719, "title": "MICSI (Microstructure Imaging)", "tagline": "Higher resolution MRI acquired faster", "body": "TLDR; MICSI is introducing an innovative image processing algorithm to decrease MRI scan time, increase image resolution, and unlock new diagnostic capabilities using Microstructure Imaging.\n\n![](https://lh4.googleusercontent.com/166C-4hbfRCmiC20D9EbOVhdubPqEE3tuTb-ZvNh8bnOXts8Rf9ZfhssNQ8ptJsGYAFqtkSt6w9Cu-aRvnBDBoKesDzc1ou7ddUVUZqTZ0knq8MmlZsbLWYY4rpGoFqUmrbmjEeSjfMx7KMKRdEngWA)\n\n**Problem: MRI Centers are overbooked.**\n\nIf you've ever had an MRI, you've probably waited weeks for an available slot and then spent a long time in the scanner. These long scan times inconvenience patients, create hospital backlogs, and delay appointments. Delays like these can critically impact conditions that require prompt diagnosis and intervention, inflate healthcare costs, increase patient anxiety, and potentially compromise treatment strategies. Why the wait? An MRI of the whole brain can be acquired in [**seconds**](https://mriquestions.com/echo-planar-imaging.html)!\n\n**Compromise: Why is your MRI an hour long?**\n\nMost of the scan time is spent on preserving signal-to-noise ratio (SNR) to produce images of the highest quality. These standard SNR preserving methods include:\n\n* Line-by-line acquisitions: These are fax-machine-like acquisitions that read the image one line at a time, where the time spent on each line is equivalent to the time that could have been spent acquiring the whole image.\n* Averaging: To boost SNR, images may be reacquired and averaged together. However, SNR improves slowly as a square root of scan time (ie 4 images are required to double the SNR).\n\nAn additional compromise is related to the spatial resolution, since MRI is acquired in 3D with “voxels”, doubling the resolution in each dimension from (2x2x2 mm3 to 1x1x1 mm3) would require the SNR to increase by **8x**, which even after 50 years of innovation, seems near impossible.\n\n![](https://lh4.googleusercontent.com/y1p1DzihqAvnmKvtoSJAbVSBrInECVUP2GvzVc5TFmLNqwTQ1hevnLgcPX7iplxNhKchrjAAQq8L8WlPbLNVWX31jAN-VIg6dSJ7Nzv-lKMUTvy8xsUIlzfFOZLlgSoGL0ZAC1lk8xqtYL2V4Q1caJk)\n\n**The MRI exam is full of compromise.**\n\n* Long scan times means fewer patients can be scanned by a given radiology center.\n* Lower resolution means a compromise in anatomical accuracy, where pathologies that could have otherwise been detected at an earlier stage would need to progress further to be MR visible.\n\nWe believe that the ultimate compromise is that MRI is not being used to its fullest potential for microstructure imaging, where MRI could be used to [**image cellular properties non-invasively**](https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/nbm.3998).\n\n**Solution: Boosting SNR over the MRI exam using MICSI-RMT.**\n\nOur patented approach (pending FDA510k clearance in Q4-2023) uses a self-supervised AI method to learn the noise properties of the MRI exam and remove noise uniformly across all images of the dataset. Our algorithm, MICSI-RMT, can be described as a smart averaging approach, whereby many images of the MRI exam are combined while preserving their unique image properties and discarding the noise. This boost in SNR would be approximately, sqrt(M/P), where M is the number of images included in the dataset, whereas P describes how different the images are (physical properties: proton density, T1, T2, diffusion, etc + artifacts: motion, errors, etc).\n\nMICSI-RMT is the commercial implementation of the MP-PCA algorithm that was developed by our research group led by Drs. Dmitry S. Novikov and Els Fieremans of NYU Radiology’s Center for Biomedical Imaging.\n\nMP-PCA is the most popular denoising approach in the MRI community:\n\n* The [**original paper**](https://pubmed.ncbi.nlm.nih.gov/27523449/) introducing the approach has achieved over 1300 citations since November 2016.\n* Denoising of [**task-based FMRI**](https://pubs.rsna.org/doi/full/10.1148/radiol.2020200822) for presurgical planning of Neurosurgery\n* Enabling low field [**0\\.55T prostate diffusion**](https://pubmed.ncbi.nlm.nih.gov/37222526/) MRI\n\nWith MICSI-RMT, MRI centers will not only be able to provide higher quality imaging but also drastically reduce the scan time (up to [50%](https://pubs.rsna.org/doi/full/10.1148/radiol.2020200822)). This improved efficiency allows centers to scan more patients per day, enhancing their operational capacity and throughput. Financially, this increase in capacity translates into a significant boost in revenue for every MRI center. By doubling the number of patients scanned daily, we estimate an additional $2 million in annual revenue per MRI scanner. Furthermore, the improved image quality and resolution could lead to more accurate diagnoses, early disease detection, and reducing overall healthcare costs in the long run.\n\n**Who we are - Greg and Ben**\n\n![](https://lh6.googleusercontent.com/F5FGyVn85R_Oxvkgw_uEOhvkKUEg_2VemnSso8p1nBfc7vgYVd1q1Vnt2BgIJrm2CRenojq9RkwoxbuP0lebYfKIcs6anX6wQEPQj0T-yaTJGLBOvqqzsw4JL4ftzrHLozX-QX592SesWUotkt92ZCw)\n\nWe are scientists and engineers who have spent the last decade developing machine learning software to both improve the quality diagnostic utility of MRI. [**Gregory Lemberskiy, PhD**](https://www.linkedin.com/in/gregory-lemberskiy/) is an [**experimental physicist**](https://scholar.google.com/citations?user=_TvugCIAAAAJ&hl=en). He discovered how to use MRI to measure the diameter of the [**prostate glandular lumen**](https://www.frontiersin.org/articles/10.3389/fphy.2018.00091/full), which may obviate the need for tissue biopsy to detect prostate cancer progression. [**Benjamin Ades-Aron, PhD**](https://www.linkedin.com/in/benjamin-aron-b0313349/) is an [**electrical engineer**](https://scholar.google.com/citations?user=xNhHFuAAAAAJ&hl=en) who developed and deployed the [**DESIGNER**](https://pubmed.ncbi.nlm.nih.gov/30077743/) open-source image processing pipeline, which is the global gold standard of diffusion MRI image processing. He has also developed a number of novel neural network architectures to aid in the denoising of compressed sensing MRI data, to facilitate brain mapping for functional neurosurgery of Epilepsy and brain tumor.\n\nTogether, the MICSI team owns over a dozen approved patents and has a clear vision for the future of MRI. The goal of MICSI is to use our patented denoising and parameter estimation technologies to make MRI quantitative and reproducible for the first time, creating a new category in diagnostic healthcare and improving patient satisfaction and outcomes.\n\n**Our Ask**\n\n* Do you know anyone in private practice, hospitals, or in large academic centers who may be interested in our approach? We want to talk to them.\n* Questions, comments, feedback? We want to hear from you!\n\n**Email us:**\n\n[**Gregory.Lemberskiy@micsi.com**](mailto:Gregory.Lemberskiy@micsi.co)\n\n[**Benjamin.adesaron@micsi.com**](mailto:Benjamin.adesaron@micsi.co)", "url": "https://www.ycombinator.com/launches/JB1-micsi-microstructure-imaging", "votes": 23, "created_at": "2023-08-02T17:20:50.834Z" } ] }, { "id": 28958, "name": "Rimward", "slug": "rimward", "former_names": [ "Quack AI", "Sourcepulse" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd215af97ab581709a04ff6284c6091f29c2058e.png", "website": "https://rimward.ai/", "all_locations": "Paris, Île-de-France, France", "long_description": "Rimward helps solar asset managers detect intrusions using the cameras already installed across their sites. It flags camera failures and coverage gaps, and gathers video evidence from multiple cameras to help their team or monitoring provider filter out false alarms and decide when to act.", "one_liner": "Solar farm security software for existing cameras", "team_size": 1, "industry": "B2B", "subindustry": "B2B -> Security", "launched_at": 1691807496, "tags": [ "Machine Learning", "Computer Vision", "B2B", "Security", "Enterprise" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "B2B", "Security" ], "regions": [ "France", "Europe", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/rimward", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/rimward.json", "year_founded": 2023, "city": "Paris", "country": "FR", "linkedin_url": "https://www.linkedin.com/company/rimward", "x_url": "https://twitter.com/rimward_ai", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/rimward", "github_url": "https://github.com/rimward", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd215af97ab581709a04ff6284c6091f29c2058e.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 1390247, "full_name": "F-G Fernandez", "title": "Founder", "founder_bio": "Founder and CEO of Rimward. FG co-founded Pyronear, an open-source nonprofit, helping turn a Raspberry Pi, his laptop GPU and entry-level camera into a wildfire detection network across Europe and South America. Running AI locally on affordable hardware, it serves French fire services and detected a wildfire 32 km away.\n\nA computer vision researcher by background, he led research at Mindee (YC W21), published conference papers without a PhD and was formerly a top PyTorch contributor.", "is_active": true, "linkedin_url": "https://linkedin.com/in/frgfm", "x_url": "https://twitter.com/FrG_FM", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/eed0d8708a06aa60a305c5d378c1355472e2e065.jpg" } ], "jobs": [ { "id": 92922, "title": "Security / IoT Deployment Engineer", "url": "https://www.ycombinator.com/companies/rimward/jobs/j4L5SKW-security-iot-deployment-engineer", "location": "Paris, IDF, FR / Paris, Île-de-France, FR", "type": "Full-time", "role": "Engineering", "role_type": "Embedded systems", "salary_range": "€50K - €70K EUR", "equity_range": "0.50% - 2.00%", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [ "Linux", "Docker", "Information Security", "Internet of Things (IoT)" ] }, { "id": 92923, "title": "Platform Engineer", "url": "https://www.ycombinator.com/companies/rimward/jobs/Ocflvow-platform-engineer", "location": "Paris, IDF, FR / Paris, Île-de-France, FR", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "€50K - €70K EUR", "equity_range": "0.50% - 2.00%", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [ "Node.js", "TypeScript", "Docker" ] } ], "news": [ { "title": "Here are this year’s top 40 startups at Station F | TechCrunch", "url": "https://techcrunch.com/2023/11/15/here-are-this-years-top-40-startups-at-station-f/", "date": "Nov 15, 2023" } ], "launches": [ { "id": 74260, "title": "🦆 Quack AI - AI companion for developer onboarding", "tagline": "Bridging open-source efficiency to drive enterprise excellence.", "body": "Hello there! I'm FG (short for François-Guillaume 🇫🇷), the founder of [Quack AI](https://www.ycombinator.com/companies/quack-ai).\n\n**tl;dr:** _We help large software companies run like the most efficient open-source projects. In the future, top-tier developers in those organizations will be able to contribute to pivotal projects, regardless of what teams they're from._\n\n### **💬** The Problem\n\nSuccessful OSS projects are well-organized communities where information is shared and valued. Recognizing this gold standard, corporate giants like Microsoft and IBM are shifting towards 'innersource' - a strategy that fosters ownership, augments communication, and bolsters productivity. However, like many cluttered OSS projects, the vast array of solicitations and varying expertise easily gets overwhelming.\n\n### 💻 Our Solution\n\nOur Dual-Fold Approach:\n\n![uploaded image](/media/?type=post&id=74260&key=user_uploads/1390247/195e8fa1-3b7d-4327-9507-c05a168f1531)\n\n* **Clarity for maintainers** (Web interface): we empower maintainers with a framework to streamline contribution processes. Through our intuitive interface, maintainers can design, curate, and sequence the optimal steps for smooth project contributions.\n\n![uploaded image](/media/?type=post&id=74260&key=user_uploads/1390247/94e7fe10-fc64-4df9-893a-8f20fd16e2ea)\n\n* **Guidance for developers** (VSCode extension): with our IDE extension, developers aren't just compliant; they're efficient. It aids in identifying the right issues, ensures alignment with contribution flows, and keeps a pulse on project priorities.\n\nWhile maintaining popular OSS projects and being an active contributor to PyTorch, I’ve often faced the frustration of seeing Pull Requests from newcomers and thinking “If only that person had the reflex to do XYZ.” Those unwritten norms, which often evade CI restrictions, only sink in after repeated code reviews and numerous trials and errors. This uneasy feeling that “it would be so easy if they were to do ABC” highlights the absence of a solid framework for collaborative development.\n\n### 🦆 The Team\n\n![uploaded image](/media/?type=post&id=74260&key=user_uploads/1390247/153b4727-f3fd-4ba1-9020-45fbc7e19d2b)\n\nI tend to describe myself as a _\"Deep learning engineer by day, open source contributor by night 🦇\"_ which is fairly accurate! On top of being among the top contributors of PyTorch, I’ve led the development of the open-source OCR library of devtool company Mindee (YC W21). I experienced first-hand how some open-source communities are structured to achieve high engagement & productivity, which now appeals to software behemoths.\n\n### 🙏 How you can help\n\n* **🤝 Refer**: know someone in a major corporation looming over innersource or part of a substantial OSS project? Please connect us.\n* **⭐ Engage**: star us on [GitHub](https://github.com/quack-ai/companion) & hop into our [Discord](https://discord.gg/E9rY3bVCWd) for the latest updates!\n* **👍👎 Experiment & feedback**: mold your unique coding insights and take our [VSCode extension](https://marketplace.visualstudio.com/items?itemName=quackai.quack-companion) for a whirl.\n* **📣 Share**: share this post with your network, you can find us on [Twitter](https://twitter.com/quack_ai) or [LinkedIn](https://www.linkedin.com/company/quackai)\n\nUp to revolutionize developer collaboration? Let’s get quacking then!", "url": "https://www.ycombinator.com/launches/JJk-quack-ai-ai-companion-for-developer-onboarding", "votes": 157, "created_at": "2023-08-21T14:34:11.158Z" } ] }, { "id": 28959, "name": "sizeless", "slug": "sizeless", "former_names": [ "sizeless GmbH" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/90ad40c9214ea17e0132605fba1e092c86e8b9a0.png", "website": "https://www.sizeless.co", "all_locations": "San Francisco, CA, USA", "long_description": "sizeless is the spatial AI to plan, build, and maintain the world's infrastructure by automating the 3D documentation and planning of the world’s underground infrastructure. Construction crews capture a short smartphone video of a construction site and get an automated, millimeter-precise digital twin with automatic part identification. We compress weeks of manual surveying and back-office planning work into hours. Our documentation and planning platform is currently in production: we have signed 7 paid pilots in 3 months with a potential ACV of $1.4M. \r\n\r\nsizeless (https://sizeless.co) spun out of research in robotics and spatial AI: co-founders Cornelius von Einem (PhD, ETH Zürich) and Roger de Belsunce (Physics & AI postdoc at MIT) lead the technology, while Julius Schilling (insurance, Aon) leads the commercial side. sizeless is building the proprietary training-data layer that all construction robots will run on.", "one_liner": "The spatial AI to plan, build and maintain the world's infrastructure", "team_size": 4, "industry": "Real Estate and Construction", "subindustry": "Real Estate and Construction", "launched_at": 1691128419, "tags": [ "Computer Vision", "Real Estate", "Construction", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "Real Estate and Construction" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/sizeless", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/sizeless.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/sizeless-ai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/90ad40c9214ea17e0132605fba1e092c86e8b9a0.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1260496, "full_name": "Roger de Belsunce", "title": "Founder", "founder_bio": "PhD in astrophysics from University of Cambridge", "is_active": true, "linkedin_url": "https://linkedin.com/in/rogerdb", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1721d9c98133ce985f909f4100bac939d563d2a2.jpg" }, { "user_id": 1260577, "full_name": "Cornelius von Einem", "title": "Founder", "founder_bio": "Cofounder of sizeless (S23). Previously Researcher @ ETH Zurich working on Computer Vision and Robotics", "is_active": true, "linkedin_url": "https://linkedin.com/in/corneliuseinem", "x_url": "https://twitter.com/cornelius_einem", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fe512b64dfa383e361e6a5217f64635d3c6d193c.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 114663, "title": "sizeless - Spatial AI for underground infrastructure 🚧", "tagline": "Turn a 10-minute phone video of an open trench into engineering-grade documentation", "body": "Hey everyone 👋\n\nWe're Julius, Cornelius and Roger — the founders of sizeless.\n\n**TL;DR**\n\nsizeless turns a smartphone video of an open trench into the documentation utilities and contractors are legally required to produce: a 3D model, CAD/BIM plans, and the quantities they bill from. Today that takes months and a surveyor. We do it in hours, from a video the crew films themselves.\n\n![uploaded image](/media/?type=post&id=114663&key=user_uploads/1260577/02c85a25-fa1a-43a2-ad56-6e571b257b37)\n\n### **Problem ❌**\n\nEverything under a street — gas, water, power, heat, fiber — must be documented when it is built or repaired. That documentation is still done by hand: schedule a surveyor, drive out, measure with a tape or a GPS rover, write it on paper, go back to the office and draw it. Then the next contractor does the same thing again, for their own pipe, in the same trench.\n\nOur average customer **waits 6 months** for documentation and spends around **$4M a year per German city** on it.\n\nAnd once the trench is backfilled, nobody can check whether the numbers were right. One person's reading, one moment, unrepeatable. That is why crews keep hitting lines that \"aren't there\".\n\n![uploaded image](/media/?type=post&id=114663&key=user_uploads/1260577/f446f4ae-943d-4c83-ba7e-c48b9220eca7)\n\n### **Solution ✅**\n\nThe crew films the open trench with a phone for about 10 minutes. Then:\n\n**Our SLAM engine** builds a metric 3D model from that video — no surveyor, no scanner, and it works without GPS in basements and house connections\n\n**Our vision engine** recognises the components — pipe types, diameters, fittings, couplings — and attributes them\n\n**Our agent** produces what the customer actually files: CAD/BIM, GIS, quantities, and invoice-ready numbers\n\n![uploaded image](/media/?type=post&id=114663&key=user_uploads/1260577/bbaddb8c-abbf-43ad-8866-025cde7bb93b)\n\nThe crew also talks while filming, and that goes into the record:\n\n> _\"There was a gas pipe crossing, so we had to lay this pipe approximately\\\n> 50 cm below it.\"_\n\nNo point cloud and no plan ever captures the _reason_ for a deviation — and once the trench is closed, it is gone for good. That sentence, anchored to the exact position in 3D, is the part nobody else has.\n\n**Where we are:** seven paid pilots in three months, the first converted to an annual contract, working with utilities and contractors on district heating in Germany.\n\n**Why now:** phones do metric 3D capture, reconstruction got cheap, and vision-language models read attributes rather than just detecting boxes. And the long game: construction robots are coming, and nobody holds the training data for what is underground. That is the layer we are building.\n\n### **Ask 💬**\n\n**Intros to utilities, municipal works, or civil and pipeline contractors** - Europe or the US\n\n**Anyone who has sold into municipalities or utilities:** how did you shorten those cycles?\n\n**Founders in construction tech, geospatial or robotics** — would love to compare notes\n\nIf you know someone running a **family construction business**, that is often our fastest door in\n\nYou can reach us at [julius@sizeless.co](mailto:julius@sizeless.co) (commercial) or [cornelius@sizeless.co](mailto:cornelius@sizeless.co) (technical).\n\n### **Team**\n\n**Julius** (CEO) — ex-Chief of Staff to the CEO at Aon.\n\n**Cornelius** (CTO) — PhD in robotics and computer vision at ETH Zürich's Autonomous Systems Lab. Built perception systems for railways, and a wearable navigation system for blind users.\n\n**Roger** (Science lead) — professor of physics & AI. Spent a decade recovering 3D structure from sparse, noisy measurements; in his case, mapping the universe.\n\n_Fun fact: the hardest part of our capture pipeline is not the maths, it is that trenches are wet, muddy, and full of moving excavators — which breaks every off-the-shelf 3D reconstruction method we tried._", "url": "https://www.ycombinator.com/launches/TpP-sizeless-spatial-ai-for-underground-infrastructure", "votes": 29, "created_at": "2026-09-10T18:00:52.630Z" } ] }, { "id": 29358, "name": "Aedilic", "slug": "aedilic", "former_names": [ "speak&find" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e453f612e8022f9284505dc7f70270165e918ea9.png", "website": "https://www.nonescape.com", "all_locations": "San Francisco, CA, USA", "long_description": "Open-source software for detecting AI-generated images (diffusion, GAN, deepfakes).", "one_liner": "Open-Source AI-generated image/deepfake detection", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Security", "launched_at": 1710792793, "tags": [ "Computer Vision", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "status": "Inactive", "industries": [ "B2B", "Security" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": true, "demo_day_video_public": true, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/aedilic", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/aedilic.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/aedilic", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/organizations/aediliclabs", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e453f612e8022f9284505dc7f70270165e918ea9.png", "app_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/bf-public_profile/29415/app_video.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQC4NIECANGNRRAJW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022717Z&X-Amz-Expires=60&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHEaCXVzLXdlc3QtMiJHMEUCIQC3G81IFSUZtThVVLqv%2FNP8JAonBeKueJ3nqFDE3FxXUwIgKp6ifAPW9xtNk4cHGnVzE9T5oK7HksFpS9B%2BDm%2FLIOQq5QMIOhAAGgwwMDYyMDE4MTEwNzIiDMiErSqdA9fZ5I2ahyrCAyhI4VxFZU5tGwj2zBKwzP3YopALEsVr%2F%2BZE%2BqKG5ExbN%2FLTC2qWrBSqlDY2M5sbNAwfqQqFkcx31kz1vOn4FSuJMRYW%2BkzTF0sHdYK6D3qvReS29sXOai4xEgytMpWv0HHodzBVtqmM6lhZul7EdriU1r3NMgn6kwrys472GaamCoy9cButcMuxdA6JAW7AePBgk0hcnFGzaY5aao9TXEqqqLDlP1z95aeQK0rFfjBuVVTlW6dspQwwAJwsuJjoI9t1YkL4Lit4gUGj1QI7PUIH70gMRgnlfqNLB7jJcwv%2BI4Rfgnv%2FfKmc5%2BoYexcBkjZu9myZq6QOA6uBoHrB0oTKfDnTUxCfp3Zyd%2BYXd%2BRBrzgSL0LnII%2Fr1P71gX8BqFIaJIcILi5J6qUg2doC6qXH8L6zz0Tim2049E5pbQ072LSzepYlZMqQ983uJSL3eUXk86ehR9Lyldz67yIvyCRilZUgb11jcjrMydu%2Fj2RI4J5upBB3IYDl1jhGlV%2FvgsHTaVujKGAhe3lEnUZs9YdCRwpmLb5XcbpM70m15Z8MQhDcr6si85gNENVvPMk4eUw4FkmyLz2%2FrGX%2BhkiQHEAiUzDJ%2FdrWBjqlAa1FguiGB0WcOcaP9Dwwt66PPG2naOQP7x5F5wwTg7gWx2PYYqZ05ZF%2F2dhdA4HJKOntgl0neDqra19sFySGeXNdHt7tNNndMpVniacUOziehXVxpr49zgL%2FhWtvSxfqv2mUAjPVCs%2FBJs6SqN3WzVfiA2RUodOJqzLA7KluY8D8t3zxMbqsQdARPCP3FgqSkJ8A8HTGWYo9w0rLlT%2Fugv6qZCfQcg%3D%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=b9a1da820cb3672732320b401d83c6d2950eb2af941d017c8f6343e8954e8fa0", "dday_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/video_event/64/1712339977791-yvv6ckljiz-e79c1e25bfd273350bca5c313d3b3a5e/c17e3d5ac1bc62323b6fddbb3036b5f8.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQC4NIECABPQ2AHNW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022717Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=e90b9c5cbeba72b544427457de5f6e9b091e08fde83a6d78252d77a72b03fe94", "company_photos": [], "primary_partner": { "name": "Harj Taggar", "url": "https://www.ycombinator.com/people/harj-taggar" }, "founders": [ { "user_id": 942043, "full_name": "Nicholas Waltz", "title": "Founder", "founder_bio": "Founder and CEO of Aedilic (W24)", "is_active": false, "linkedin_url": "https://linkedin.com/in/nicholas-waltz-b95067211", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/707d560a939f3d3ecdcbbaea21074abee0f0935f.jpg" }, { "user_id": 1282773, "full_name": "Lukas Schneider", "title": "Founder", "founder_bio": "Founder and CTO of Aedilic (W24). Before Aedilic, Lukas conducted machine learning research at the Robotic Systems Lab, a leading research institution on robot autonomy. His research has been published to ICRA, the #1 robotics conference on Google Scholar. Before that, he developed drone control algorithms at the German Aerospace Center (DLR). Lukas holds a Master of Science in Robotics, Automation and Control from ETH Zurich.", "is_active": false, "linkedin_url": "https://linkedin.com/in/lukas-schneider-a27498177", "x_url": "https://twitter.com/e3ntity", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/76329d48f50c40cdeb70c6cf9c99f13dcec0e522.jpg" } ], "jobs": [], "news": [ { "title": "Show HN: gpudeploy.com – \"Airbnb\" for GPUs | Hacker News", "url": "https://news.ycombinator.com/item?id=40260259", "date": "May 05, 2024" } ], "launches": [ { "id": 80936, "title": "GPUDeploy.com – Airbnb for GPUs", "tagline": "GPUDeploy is a software solution and marketplace for renting low-cost on-demand GPUs now", "body": "tl;dr [gpudeploy.com](http://gpudeploy.com) is Airbnb for GPUs. It is a marketplace and easy-to-integrate software for renting out the over $1bn in cheap, high-quality compute that is sitting around unused (see e.g. [gpulist.ai](http://gpulist.ai)). We are two engineers from Cambridge and ETH Zürich with experience in Robotics and Deep Learning.\n\n —\n\nHi, we’re Nico and Lukas, the cofounders of Aedilic (doing business as GPUDeploy).\n\n![uploaded image](/media/?type=post&id=80936&key=user_uploads/942043/2bc19071-a70a-48a6-9ad0-8996c9326e36)\n\nWe met in high school when we were both 14 and bonded over our interest in coding. Our first project was a [cookie-clicker game](https://soda-factory.github.io/) that we wrote during a sleepover two weeks after meeting for the first time.\n\nNico has since received a Masters in Econometrics from the University of Cambridge with a research focus on the mathematical theory of Machine Learning algorithms ([Random Forests](https://arxiv.org/abs/2403.06015), [DBSCAN](https://arxiv.org/abs/2403.14798)). Lukas holds a Masters from ETH Zürich, where he trained robot dogs at the Robotics Systems Lab, using the latest Machine Learning models. During his Masters, he wrote the official ETH Zürich [RSL open-source Reinforcement Learning “algorithms” library](https://github.com/leggedrobotics/rsl_rl/tree/algorithms). His [Master's thesis](https://arxiv.org/abs/2309.14246) was published in the highest-ranked Robotics conference (ICRA) this year.\n\n**There is a lot of cheap, high-quality, unused compute sitting around**\n\n[gpulist.ai](https://gpulist.ai) alone lists over $1bn in unrealized contract value. These are the latest Nvidia models in cutting-edge data centers, with Infiniband interconnect and perfect internet. They are cheap because nobody books them. Meanwhile, research labs and AI companies are looking for cheap, high-quality compute but will not go through unknown, unclear websites and the associated sales process. We allow them to book pre-configured GPU instances on-demand at wholesale prices that they would normally need to negotiate.\n\nWe’ve built software that makes idle GPUs in data centers accessible and super simple to book and use via our website. Data-centers and AI companies with idle compute have the option to list instances on the spot market as soon as they are available, it takes 5 minutes to set up. Unlike other solutions, our software enables fractional use of GPU boxes, so 8 GPUs can be rented out to 8 different people.\n\n**Other options are either expensive, booked out, or a pain to use**\n\nWe know how inconvenient current solutions are because we’ve dealt with them, both at university and for the previous iteration of this startup (robotics-related).\n\nFor example, Lukas, during his time at ETH, was given a blank check by his lab to book GPUs for his research. He spent the entire night with his supervisor going through Google Cloud to scrape together a few GPU instances; Google will only tell you that an H100 is unavailable in Region X **after** you configured and launched your instance. Plus, they were expensive, with an A100 SXM 40GB at $3.60/gpu/hour (compare this to $1.28 on our site).\n\n**Solution: GPUDeploy**\n\nGPUDeploy is a software solution and marketplace for renting GPUs. We have made our site simple, to the point, and opinionated - there are no hurdles between you and booking a GPU. We offer very competitive pricing and ensure that the compute instance you book is reliable and meets our benchmarks.\n\n![uploaded image](/media/?type=post&id=80936&key=user_uploads/942043/3b64efe2-a067-41d3-8d8c-039e5c80962d)\n\nWe both have a spot offering and an Airbnb-like experience for long-term bookings. We match your spot request with available compute resources that meet our standards. You can then simply connect to the machine to run your workloads.\n\nWe launched with a [Show HN](https://news.ycombinator.com/item?id=40260259) four weeks ago, which immediately got #1 and stayed on the front page for 2 days.\n\nTry it out!  \n\n**Our ask:**\n\n* If you need GPUs, try it out.\n* If you have compute, try renting it out on our site. It’s really simple (2 commands) and flexible, and we help with custom onboarding. For example, you can make a profit of \\~$3000k a year with an RTX4090. Currently, the majority of our computing is booked out. Anyone is able to add compute, but we will sort out unreliable providers immediately.\n* We collaborate with data centers. If you have a cloud with idle GPUs, reach out.\n\n\\\nMessage [nico@gpudeploy.com](mailto:nico@gpudeploy.com) or [contact@gpudeploy.com](mailto:contact@gpudeploy.com) for any questions.", "url": "https://www.ycombinator.com/launches/L3Q-gpudeploy-com-airbnb-for-gpus", "votes": 14, "created_at": "2024-05-17T22:23:42.882Z" } ] }, { "id": 29396, "name": "EdgeTrace", "slug": "edgetrace", "former_names": [ "EdgeTrace AI", "edgetrace" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/1a5e9f714f08ee19fe05c995f4d3940b758e86ef.png", "website": "https://edgetrace.ai", "all_locations": "San Francisco, CA, USA", "long_description": "EdgeTrace unifies your entire camera network into a single, powerful intelligence platform–helping public safety teams detect threats faster, respond in real time, and protect the community.", "one_liner": "Describe It. Find It. Act On It.", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1710460608, "tags": [ "GovTech", "Computer Vision", "B2B", "Video", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/edgetrace", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/edgetrace.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://linkedin.com/company/edgetrace", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/1a5e9f714f08ee19fe05c995f4d3940b758e86ef.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Garry Tan", "url": "https://www.ycombinator.com/people/garry-tan" }, "founders": [ { "user_id": 1181859, "full_name": "Kyle Orciuch", "title": "Founder/CEO", "founder_bio": "Co-Founder & CEO @ EdgeTrace", "is_active": true, "linkedin_url": "https://linkedin.com/in/kyleorciuch", "x_url": "https://twitter.com/kyleorciuch", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1b1f24f2b92a94a3ba6cff0ce4836338ea7ac8cd.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 29447, "name": "Deepnight", "slug": "deepnight", "former_names": [ "DeepNight" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/419c6b136773af60713e2a4301554b4ef1bbd8a9.png", "website": "https://www.deepnight.com", "all_locations": "San Francisco, CA, USA", "long_description": "DeepNight is building the next generation of night vision with AI.", "one_liner": "Building The Next Generation of Night Vision Devices", "team_size": 16, "industry": "Industrials", "subindustry": "Industrials -> Defense", "launched_at": 1709629638, "tags": [ "Computer Vision", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2024", "status": "Active", "industries": [ "Industrials", "Defense" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/deepnight", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/deepnight.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/deepnight-inc", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/419c6b136773af60713e2a4301554b4ef1bbd8a9.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Pete Koomen", "url": "https://www.ycombinator.com/people/pete-koomen" }, "founders": [ { "user_id": 1583389, "full_name": "Lucas Young", "title": "Founder/CEO", "founder_bio": "Solving night vision with software.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/lucasdyoung/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1db87cb23cdb8e0c210d7b811a4beb540a83c9e6.jpg" } ], "jobs": [ { "id": 84663, "title": "Computer Vision Engineer", "url": "https://www.ycombinator.com/companies/deepnight/jobs/xG3gegU-computer-vision-engineer", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$160K - $240K", "equity_range": "0.10% - 0.40%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "Torch/PyTorch", "Machine Learning", "Deep Learning", "Computer Vision" ] }, { "id": 109948, "title": "Electrical Engineer - Displays & Camera Sensors", "url": "https://www.ycombinator.com/companies/deepnight/jobs/Apr3qXh-electrical-engineer-displays-camera-sensors", "location": "San Diego, CA / San Francisco, CA", "type": "Full-time", "role": "Engineering", "role_type": "Electrical", "salary_range": "$120K - $180K", "equity_range": "0.15% - 0.35%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "PCB design" ] } ], "news": [ { "title": "YC grad Deepnight nabs $5.5M for AI night vision software that disrupts a multi-billion dollar industry | TechCrunch", "url": "https://techcrunch.com/2025/02/27/yc-grad-deepnight-nabs-5-5m-for-ai-night-vision-software-that-disrupts-a-multi-billion-dollar-industry/", "date": "Feb 27, 2025" } ], "launches": [] }, { "id": 29503, "name": "Dragoneye", "slug": "dragoneye", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/354a00433e0332ff17869b4c08d6ee8d10b8a232.png", "website": "https://dragoneye.ai/", "all_locations": "New York City, NY, USA", "long_description": "Dragoneye helps devs build their custom video detection models in less than 5 minutes. These models can then be used to power apps and features that use images and videos. No more arduous process of annotating any training data or doing any machine learning work themselves.\r\n\r\nTry out our demo today at https://playground.dragoneye.ai/ !", "one_liner": "Custom video detection models in less than 5 minutes", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1705986923, "tags": [ "Developer Tools", "Computer Vision", "AI", "ML" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/dragoneye", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/dragoneye.json", "year_founded": 2023, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/dragoneyeai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/354a00433e0332ff17869b4c08d6ee8d10b8a232.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 818332, "full_name": "Alex Liao", "title": "Founder/CEO", "founder_bio": "Building computer vision so every app can see the world. \n\nex-Jane Street, ex-Facebook, UPenn Physics and CS\n\n", "is_active": true, "linkedin_url": "https://linkedin.com/in/alexliaoo", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ed58640fb40fa1b4d8e3bf915680b1f8045c7799.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 95768, "title": "Dragoneye: Production video detection models in <5 minutes", "tagline": "Build zero-shot video models quickly with 0 training data", "body": "![uploaded image](/media/?type=post&id=95768&key=user_uploads/818332/27d779bf-8574-4d51-adbe-d69a806e6d0b)\n\nHey YC! I’m Alex, founder of Dragoneye. \n\n**I want to share with y’all the launch of our new interactive Playground, which allows you to build your own custom video detection models in under 5 minutes.**  \n\n![uploaded image](/media/?type=post&id=95768&key=user_uploads/818332/51fa21e6-bc13-41ad-8f9e-03947fd50b7a)\n\n# **The Problem:**\n\nVideo detection models are incredibly useful. Analyzing security camera footage, ensuring PPE compliance, monitoring production lines. \n\nBut it’s always been hard to build video detection models. Thousands of hours of videos that need to be annotated. You train the model only to find that it doesn’t perform as well as you need it. Really cool ideas that never get to prototype, never mind released in production. Crucial features that take tens of thousands of $$ to trial and months to implement. \n\n# **Our Solution:**\n\nOur new Playground allows you to build and deploy production-ready video detection models in less than 5 minutes, enabled by our powerful zero-shot models.\n\nTo get started:\n\n1. Upload a video\n2. Describe the categories of items you want to detect in your video\n3. The model streams the predictions and you can preview it on your video\n4. Iterate on your categories till you’re set. \n\nOnce you’re happy with your model, you can save it out and it’s live! To make it easier to integrate with your apps, we have libraries for Python and Node.js, with more languages to come.\n\nCheck out the Playground at https://playground.dragoneye.ai/ to get started. You can follow our step by step guide below as well!\n\nhttps://youtu.be/2rhKBO4N7B0\n\nWe can’t wait to see what you build! 🙂 ", "url": "https://www.ycombinator.com/launches/Oue-dragoneye-production-video-detection-models-in-5-minutes", "votes": 11, "created_at": "2025-11-25T16:18:04.833Z" }, { "id": 85450, "title": "Dragoneye - More detailed object detection than Google and Amazon!", "tagline": "We offer vision object detection for 13k+ classes right out of the box.", "body": "Hey YC! I’m Alex, founder of Dragoneye. \n\n**I want to share with y’all the launch of our object detection platform, which can recognize 40x more classes than Amazon Rekognition and 20x more than Google Vision across a wide range of categories.** \n\n![uploaded image](/media/?type=post&id=85450&key=user_uploads/818332/45a1bd0a-16f7-4061-9c0e-ebd5a586450f)\n\n# **The Problem:**\n\nYou’re building a new feature that relies on the ability to recognize things in camera or photos. But all of the off-the-shelf APIs today don’t get the detail you need. You don’t just need to know it’s a dress, but that it’s a checkered short-shift dress! \n\nYou look into how to train your own computer vision model, but you soon realize what a daunting task it is. There are thousands to millions of photos to collect, and it takes weeks of time. What can you do?\n\n# **Our Solution:**\n\nWe want to have the best visual recognition API out there, and enable devs to build powerful vision-driven features in a pinch.\n\n**What we’re focused on:**\n\n* **Latency** - our fastest models can detect objects in 12ms\n* **Detail** - 13,000 different classes over 25 categories now, and we aim to get to 100,000+ by the end of 2024\n* **Implementation ease** - under 10 lines in Python & JS and a simple REST API\n\nSeeing is believing - pun intended - so here is our side-by-side comparison with Google’s Vision API and Amazon’s Rekognition\n\n![](https://bookface.ycombinator.com/media/?type=post&id=85450&key=user_uploads/818332/aa480801-a466-48a9-89a4-1e0bbb8fc9e7)\n\n![](https://bookface.ycombinator.com/media/?type=post&id=85450&key=user_uploads/818332/f6058fa7-c804-4c68-a282-9faab8873005)\n\n![](https://bookface.ycombinator.com/media/?type=post&id=85450&key=user_uploads/818332/4c0c954f-c1c0-4382-8d3d-9998404b461d)\n\n# **Our Ask**\n\n1. Try out our instant demo at . We know we have some bugs to squash, so please let us know what works great and what doesn’t. \n2. [Book a meeting with us](https://calendly.com/dragoneye-cal/intro-call) if you have computer vision/object detection needs - coffee is on us!", "url": "https://www.ycombinator.com/launches/MEE-dragoneye-more-detailed-object-detection-than-google-and-amazon", "votes": 13, "created_at": "2024-11-11T18:54:56.008Z" }, { "id": 79474, "title": "Dragoneye - Computer vision for every developer", "tagline": "We automate the entire process for building computer vision, allowing developers to get custom vision recognition models from just a model spec.", "body": "![uploaded image](/media/?type=post&id=79474&key=user_uploads/818332/3b1dd109-a001-41f9-a1a8-39e46badb5a5)\n\n**TLDR:** [**Dragoneye**](https://dragoneye.ai/) helps developers use computer vision without needing to bring any of their own training data or do any CV/ML work themselves.\n\n## Demo\n\n\n\n## ❓The problem\n\nGetting data from images today is hard. LMMs allow you to get started quickly but have capability limitations and are slow. On the other hand, building your own vision recognition models requires a ton of time and a ton of expertise.\n\nI learned this firsthand when I tried to build my own fashion outfit**\\-**matching app a few years ago. What started as coding a fun consumer app was quickly taken over by the challenges of building custom image models - annotating data, model evaluation, training, deployment, etc. Before long, I realized that I was basically building a computer vision startup - literally!\n\n## ✨ Our solution\n\nDragoneye enables developers to build and use cutting-edge CV models simply by specifying the model spec.\n\n**Specification → Working Model Automagically:** After specifying the model, Dragoneye’s systems scurries away to find the best implementation strategy and then builds it for you!\n\n**Continuous Improvement:** In the background, Dragoneye constantly keeps the models and tech at the cutting edge so you keep getting the best performance without any work on your end at all.\n\n**Grows with your need:** As your needs grow, we grow with you. Simply update your model specification, and Dragoneye will figure out how to make it work best.\n\n## 🙏 Ask\n\nWe just opened up our self-service feature. [Sign up on our site](https://dragoneye.ai/) for access!\n\nAlso, we’d love to hear what you want to build with Dragoneye - please email me at [alex@dragoneye.ai](mailto:alex@dragoneye.ai)!\n\n![uploaded image](/media/?type=post&id=79474&key=user_uploads/818332/dc0fc2db-02eb-44be-99dc-c79bdc7721cd)\n\n", "url": "https://www.ycombinator.com/launches/Kfq-dragoneye-computer-vision-for-every-developer", "votes": 49, "created_at": "2024-03-18T18:59:47.892Z" } ] }, { "id": 29635, "name": "DigitalCarbon", "slug": "digitalcarbon", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/5cd96189d0726eb13cddf2279ecddbb8f1d37895.png", "website": "https://www.digitalcarbon.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Digitalcarbon transforms ordinary images and videos into interactive, photorealistic 3D environments. Our technology can take photos from any device and render 3D scenes at more than 100 frames per second on everyday devices. \r\n\r\nThe founders were the first and second hires at a previous YC company AssemblyAI, and have more than 8 years of experience building AI models. \r\n\r\nCreating immersive 3D experiences typically requires complex equipment and suffers from slow rendering speeds. This has limited the adoption of 3D technology across various industries. Digitalcarbon fixes that, eliminating the need for specialized equipment and dramatically accelerating the rendering process.\r\n\r\nOur technology enables applications across various industries:\r\n* Real estate: Creating virtual property tours that feel remarkably lifelike\r\n* E-commerce: Providing interactive 3D product visualizations to boost consumer experience\r\n* Tourism: Helping tourist businesses attract more travelers with immersive virtual previews\r\n* Drone mapping and inspection: Enhancing aerial surveys and structural assessments with detailed 3D models\r\n\r\nThink Unreal Engine meets Matterport with no hardware, but faster, editable, and more accessible for businesses of all sizes.", "one_liner": "Transform Images And Videos Into Immersive 3D With AI", "team_size": 0, "industry": "B2B", "subindustry": "B2B", "launched_at": 1725300975, "tags": [ "Computer Vision", "Real Estate", "B2B", "E-commerce", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/digitalcarbon", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/digitalcarbon.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/digitalcarbonai", "x_url": "https://x.com/DigitalCarbonAI", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/5cd96189d0726eb13cddf2279ecddbb8f1d37895.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Garry Tan", "url": "https://www.ycombinator.com/people/garry-tan" }, "founders": [ { "user_id": 1105128, "full_name": "Guru Rao", "title": "Founder/CEO", "founder_bio": "Guru is the cofounder and CEO of DigitalCarbon. Previously, Guru was the first hire and AI Lead at AssemblyAI (YC 2017) and was instrumental in scaling the company from 1 to 100+ employees. He has been building in the AI space since 2015.", "is_active": true, "linkedin_url": "https://linkedin.com/in/gururao1", "x_url": "https://x.com/CtrlGuruDelete", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0049b93fd9979606d20678dfd1fecb579b3344dc.jpg" }, { "user_id": 1919588, "full_name": "Michael Nguyen", "title": "Founder/CTO", "founder_bio": "Make GPUs go brrrr", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/michaelpnguyen/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f743950c5065423b0a5f3882ad1b274d61a216b2.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 83730, "title": "DigitalCarbon: Transform images and videos into immersive 3D with AI", "tagline": "Unreal Engine meets Matterport with no hardware.", "body": "Hey everyone! We're [Guru](https://linkedin.com/in/gururao1) and [Michael](https://www.linkedin.com/in/michaelpnguyen/) from [DigitalCarbon](https://www.digitalcarbon.ai/).\\\n\\\n**Tl;dr**\\\nDigitalCarbon transforms images and videos into photorealistic, editable 3D scenes. Create virtual home tours or show off products in 3D using just your phone - no special gear needed!\n\n[ ](https://www.youtube.com/watch?v=c2Pw63zE94I)\n\\\n**❌ The Problem: Creating 3D environments is complex and slow**\n\nBillions of images and videos are captured daily, but turning them into immersive 3D experiences is challenging:\n\n* Requires specialized, expensive equipment\n* Rendering is painfully slow, limiting real-time applications\n* Expertise needed to create and edit 3D scenes\n\nThis complexity has restricted 3D technology adoption across industries, leaving immense potential untapped.\n\n**🚀 Our Solution**\n\nDigitalCarbon turns ordinary photos and videos into interactive, photorealistic 3D environments - all rendered at 100+ FPS on everyday devices.\n\n**With DigitalCarbon, you can:** \\\n❌ Eliminate need for specialized hardware \\\n❌ Eliminate slow rendering times \\\n❌ Eliminate complex 3D modeling skills \\\n✅ Create 3D scenes from simple 2D inputs\\\n✅ Edit and customize 3D environments easily \\\n✅ Deploy across multiple platforms instantly\n\n\\\n**🌟 Transform Your Industry:** \\\n🏠 Real Estate: Create lifelike virtual property tours\\\n🛒 E-commerce: Offer interactive 3D product visualizations \\\n✈️ Tourism: Provide immersive destination previews \\\n🚁 Drone Mapping: Enhance aerial surveys with detailed 3D models\\\n\\\n**🙌 Our Asks**\n\n🧪 Beta Testers: If you're in Real Estate or E-commerce, we'd love your feedback on our platform!\n\n🤝 Intros: Know decision-makers in PropTech or E-commerce? We'd appreciate an intro.\n\nCheck out our demos at [**digitalcarbon.ai**](http://digitalcarbon.ai/) or email us at [**founders@digitalcarbon.ai**](mailto:founders@digitalcarbon.ai).", "url": "https://www.ycombinator.com/launches/LmU-digitalcarbon-transform-images-and-videos-into-immersive-3d-with-ai", "votes": 25, "created_at": "2024-09-04T12:11:56.482Z" } ] }, { "id": 29682, "name": "RunLocal AI", "slug": "runlocal-ai", "former_names": [ "Neuralize", "RunLocal" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0c096a99a018da47706cb093bff201df7e6cbd6d.png", "website": "https://runlocal.ai", "all_locations": "London, England, United Kingdom", "long_description": "RunLocal is an environment that specializes AI agents to optimize model for onboard compute platforms like NVIDIA Orin/Thor and Qualcomm → www.runlocal.ai\r\n\r\nRunLocal is built for Physical AI engineering teams across autonomous vehicles, robotics, smart cameras and more.\r\n\r\nIt tracks every experiment and continuously refines experimentation data into an understanding of what drives performance on your target hardware.\r\n\r\nThis environment means that a generic coding agent (e.g. Codex or Claude Code) can experiment and iterate better, faster and cheaper. \r\n\r\nWith RunLocal, you hit performance targets faster and ship more optimized models – without hiring inference optimization specialists. \r\n\r\nWe’re working with leaders in autonomous vehicles and robotics. We're backed by investors like Y Combinator and 468 Capital.", "one_liner": "AI agent that optimizes inference for embedded compute like Jetson", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1722617907, "tags": [ "Developer Tools", "Computer Vision", "AI", "ML" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United Kingdom", "Europe" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/runlocal-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/runlocal-ai.json", "year_founded": 2024, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/runlocalai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0c096a99a018da47706cb093bff201df7e6cbd6d.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "David Lieb", "url": "https://www.ycombinator.com/people/david-lieb" }, "founders": [ { "user_id": 766869, "full_name": "Ismail Salim", "title": "Founder/CEO", "founder_bio": "Co-Founder & CEO of RunLocal AI\n\nRunLocal is an environment that specializes AI agents to optimize model for onboard compute platforms like NVIDIA Orin/Thor and Qualcomm → www.runlocal.ai\n\nRunLocal is built for Physical AI engineering teams across autonomous vehicles, robotics, smart cameras and more.\n\nPreviously worked on the first edge AI video codec at Deep Render and AR/VR apps at Meta.", "is_active": true, "linkedin_url": "https://linkedin.com/in/ismail-salim-cv", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9c2f30f10d201502b7c1ac8cbb6a5eec3ba09ef6.jpg" }, { "user_id": 1972003, "full_name": "Ivan Chan", "title": "Founder/CTO", "founder_bio": "CTO of Neuralize. Previously a software engineer at Marshall Wace (Europe's largest hedge fund) building an internal AI platform for developers and optimizing low latency streaming systems.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ivan-chan-959610b7/", "x_url": "https://x.com/ivanchanavinah", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/400731efd4eb96439ed56efd47c95302c3db188a.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 29819, "name": "Weel", "slug": "weel", "former_names": [ "Vizia" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/22dac63897681e1a31dc26b877ee786266ae0bff.png", "website": "https://weel.live/", "all_locations": "Paris, Île-de-France, France", "long_description": "Weel is the next-generation all-in-one driver co-pilot that redefines your driving experience by turning your smartphone into a complete safety and navigation hub. Our free app offers a built-in dashcam with easy video sharing, a smart navigation system, and advanced AI features—such as augmented reality navigation and ADAS—that reduce your risk by up to 30%. Say goodbye to expensive dashcams and complex setups – with Weel, you get complete driving safety and guidance in one free smartphone app.", "one_liner": "GPS & dashcam smartphone app", "team_size": 4, "industry": "Consumer", "subindustry": "Consumer -> Transportation Services", "launched_at": 1741795332, "tags": [ "Augmented Reality", "Computer Vision", "Navigation", "Mobility", "Automotive" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "Consumer", "Transportation Services" ], "regions": [ "France", "Europe", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/weel", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/weel.json", "year_founded": 2024, "city": "Paris", "country": "FR", "linkedin_url": "https://www.linkedin.com/company/weel-inc/", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/weel-f331", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/22dac63897681e1a31dc26b877ee786266ae0bff.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/ea42f2eb2a0c83832de97d0a7ae7df0e9513cf61.png" ], "primary_partner": { "name": "Harj Taggar", "url": "https://www.ycombinator.com/people/harj-taggar" }, "founders": [ { "user_id": 1979026, "full_name": "Raphael Krief", "title": "Founder/CEO", "founder_bio": "CEO of Weel (S24).", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/raphaël-krief-237a79217/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8a7ad0f7a53e6df26ab3124b90997de06dbf081e.jpg" }, { "user_id": 1989262, "full_name": "Samuel Marciano", "title": "Founder/CTO", "founder_bio": "Co Founder & CTO\n", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/samuel-marciano/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7b84939d23d7c9a5e7f0aa0315c4e2bb515836d1.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 91925, "title": "Weel: The First GPS App with a Built-in Dashcam", "tagline": "Turn your smartphone into a AI-powered dashcam & GPS.", "body": "### **👋 Hi YC!**\n\nWe’re Raphaël and David, and we’re building **Weel — the first GPS app with a built-in dashcam** that works with the phone you already own.\n\n**TL;DR**\n\nWeel turns your smartphone into a dual-facing dashcam and full GPS navigation app — no cables, no SD cards, no new hardware. It records your trips, uploads them securely to the cloud, and gives you evidence in case of incidents.\\\n→ **100% free to use on iOS & Android**\n\nDownload here:[ https://onelink.to/fk9tyg](https://onelink.to/fk9tyg)\n\n**🎥 Demo Video**\n\nhttps://www.youtube.com/watch?v=Gz5eFEN_WNw\n\n**The Problem: Dashcams today are too expensive, messy, and complicated**\n\nDashcams are incredibly valuable: some insurers offer **up to 30% discounts** to drivers who are equipped with one, and footage can make all the difference in resolving accidents or preventing fraud.\n\nYet most drivers still don’t use them.\n\nWe’ve spent weeks testing the most popular dashcams on the market. What did we find?\n\n💸 High cost (up to $550)\\\n🔧 Installation headaches — some even require professional wiring\\\n📼 Local-only footage (SD cards, no cloud)\\\n🚙 Easy to steal if left in the car\n\n📸 Here's what that looks like in real life:\n\n![uploaded image](/media/?type=post&id=91925&key=user_uploads/1979026/14f1457a-7457-4463-9d3d-7e08e114cd81)\n\n**Our Solution: Weel replaces all of that with a simple app**\n\n![uploaded image](/media/?type=post&id=91925&key=user_uploads/1979026/189d2815-2212-4495-80f4-d82616412f65)\n\n\\\nWeel is a smartphone app that offers:\n\n* **External dashcam** — Records the road ahead and provides incident evidence in case of an accident\n* **Internal dashcam** — Designed for professional drivers (taxis, VTCs, delivery) to ensure security and accountability\n* **Smart navigation** — Real-time GPS guidance with traffic-aware routing\n* **Cloud & encryption** — Video footage is uploaded via Wi-Fi and encrypted for secure storage\n* **Accident analysis** _(in beta)_ — Automatically analyzes and describes what happened during a crash, to simplify reporting and defend the driver\n\n**Why are we building this?**\n\nFor months, we’ve been speaking with both professional and everyday drivers — many shared stories of accidents where, without any footage, they lost time, money, and even saw their insurance premiums double. They had no way to defend themselves.\n\nWe believe that access to protection, evidence, and safer driving shouldn’t be a luxury — this kind of technology should be available to **everyone**, for **free**.\n\n**What’s next?**\n\nOur goal is to develop a full suite of AI computer vision features such as:\n\n* **Real-time alerts** — Warnings for risky driving behavior to help prevent incidents\n* **Augmented reality navigation** — Smarter, clearer GPS guidance using AR overlays\n* **Video-based traffic reports** — Automatic detection of traffic jams, hazards, and incidents from user dashcams\n\nto create a large, connected and safe driver network improving traffic flow across cities.\\\n\\\n**🙏 Ask**\n\nWant to help us make driving safer for everyone?\\\nTry Weel for free today on iOS & Android →[ ](https://onelink.to/fk9tyg)[https://onelink.to/fk9tyg](https://onelink.to/fk9tyg%5C) \\\n\\\nIf you have feedback, product ideas, or just want to connect — feel free to reach out at [**raphael@weel.live**](mailto:raphael@weel.live). We'd love to hear from you.", "url": "https://www.ycombinator.com/launches/Nuf-weel-the-first-gps-app-with-a-built-in-dashcam", "votes": 22, "created_at": "2025-07-07T16:20:49.510Z" } ] }, { "id": 29823, "name": "autarc", "slug": "autarc", "former_names": [ "autarc GmbH" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c6f422aa332e520cce9d631b8dfbcdfb14375c6e.png", "website": "https://www.autarc.energy/global", "all_locations": "Berlin, Berlin, Germany", "long_description": "The problem: Energy installers still waste dozens of hours per project on pen & paper processes. Their CRM, ERP, email, planning tools are completely detached from each other.\r\n\r\nThe solution: autarc lets installers automate their repetitive business tasks on a single platform. They manage everything from one interface: WhatsApp or iMessage. This combines their legacy tools into one system. We save 24+ hours on every home energy project, like heat pump, solar, wallbox or battery installations.\r\n\r\nThe technology: On top of our OS, autarc leverages LiDAR for spatial analysis, computer vision for automated assessment, and AI for system recommendations. This enables installers to confidently transition to and scale up sustainable home energy solutions.", "one_liner": "autarc is the agentic OS for energy installers.", "team_size": 30, "industry": "B2B", "subindustry": "B2B", "launched_at": 1720714870, "tags": [ "Lidar", "Computer Vision", "B2B", "AI", "AI Assistant" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "B2B" ], "regions": [ "Germany", "Europe", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/autarc", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/autarc.json", "year_founded": 2023, "city": "Berlin", "country": "DE", "linkedin_url": "https://www.linkedin.com/company/autarc-energy/", "x_url": "", "fb_url": "https://www.facebook.com/autarcenergy", "cb_url": "https://www.crunchbase.com/organization/autarc", "github_url": "https://github.com/autarcgmbh/", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c6f422aa332e520cce9d631b8dfbcdfb14375c6e.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/b1312628490550b199231798494b86a456421f79.png" ], "primary_partner": { "name": "Nicolas Dessaigne", "url": "https://www.ycombinator.com/people/nicolas-dessaigne" }, "founders": [ { "user_id": 395990, "full_name": "Etienne-Noel Krause", "title": "Founder/CEO", "founder_bio": "Etienne, Co-Founder & CEO of autarc. I used to work in the HVAC industry before launching autarc. Would describe myself as a creative mind that loves building digital products.\n\nGet in touch: etienne@autarc.energy", "is_active": true, "linkedin_url": "https://linkedin.com/in/etienne-krause", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/52b8a09f348885f7c08ffbcda92c0be81dee015d.jpg" }, { "user_id": 1387704, "full_name": "Thies Hansen", "title": "Founder/COO", "founder_bio": "Co-Founder and COO of autarc. Previously worked at a logistics company within process optimizations, later switched to work in startups and VC - helping installers to install more heat pumps is much more fun though.", "is_active": true, "linkedin_url": "https://linkedin.com/in/thies-hansen", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2ec14a90b9dfd9665a13d4bbb43119e45074329e.jpg" }, { "user_id": 397954, "full_name": "Marius Seufzer", "title": "Founder/CTO", "founder_bio": "CTO @autarc.\nPreviously Lead iOS Engineer @Sameday Health and built Passkeys for Apple.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/marius-seufzer", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a13d39088e8b4181c3e8e1653c03fbb47a4735e5.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 83857, "title": "🏡 autarc - The OS for heat pump installers", "tagline": "We help HVAC installers in Europe to sell and design heat pumps.", "body": "**TL;DR:** We are live in Germany with 380 customers and a 7-digit ARR. Our software empowers installers to shift away from oil and gas, focusing instead on heat pump installations. Check out our website: [www.autarc.energy](http://www.autarc.energy)\n\n—\n\nHey YC! Etienne, Thies and Marius here from autarc! 👋🏼\n\n\n\n# **😤  The Problem**\n\nDid you know that 30 % of Europe’s CO2 emissions come from heating buildings with fossil fuels? autarc is at the forefront of addressing this environmental challenge.\n\nAchieving a CO2-neutral life starts with a heat pump installation—it's the most efficient and sustainable alternative available. Yet, at the current installation speed, it will take over 100 years to replace the last gas or oil boiler in Europe.\n\nEtienne encountered this problem after nearly four years in the HVAC industry. Initially, we spoke with almost 80 firms to fully understand their challenges. It turns out installers would love to focus on heat pumps, but they lack the skills and tools to meet the overwhelming demand. These small, fragmented businesses across Europe struggle with scaling their processes and securing growth financing. On top of that, regulatory changes require them to manually capture hundreds of data points, leading to **60 % of installation costs** being attributed to **labor** and **process inefficiencies**.\n\n# **😍 The Solution**\n\nWith autarc, we are building an operating system (OS) for heat pump installers. Our software enables installers to capture hundreds of data points in minutes rather than hours and provides them with everything they need in one place: system design, offer creation, financing options, subsidies, and mandatory reports. By leveraging LiDAR technology and AI, we reduce the overall project time for heat pump installations by 50 %.\n\n![uploaded image](/media/?type=post&id=83857&key=user_uploads/395990/733c46ea-69da-43dd-b604-9bc85328d5cd)\n\n**How it works:** We standardize the pre-installation process, utilizing up to 800+ data points per property. This reduces time wasted on leads that don’t convert by 80% and frees up installer capacity. Our customers tell us that they save up to 20 hours per installation with our solution.\n\n# **🏡 The Team**\n\nWith 15 full-time employees, we are building a European champion from our HQ in Berlin, Germany. Our talented team members have backgrounds in major tech companies, HVAC/solar businesses, and energy consulting. Our greatest asset is the close relationship we maintain with our customers, enabling us to co-develop the features they truly need.\n\n![uploaded image](/media/?type=post&id=83857&key=user_uploads/395990/611c01dc-3a98-41e2-ae50-d4b6fa54bafd)\n\n_From left to right, on the left image: _[_Marius_](https://www.linkedin.com/in/marius-seufzer)_, _[_Thies_](https://linkedin.com/in/thies-hansen)_, and _[_Etienne_](https://linkedin.com/in/etienne-krause)_._\n\n# 💜 **The Ask**\n\nWe are looking for talent in almost every area—from Sales to Frontend and ML Engineering. If you know anyone in the HVAC industry in Germany, we’d love an introduction.\n\nFeel free to drop us a message at [founders@autarc.energy](https://mailto:founders@autarc.energy) 📧 and follow us on [LinkedIn](https://www.linkedin.com/company/autarc-energy).", "url": "https://www.ycombinator.com/launches/LoX-autarc-the-os-for-heat-pump-installers", "votes": 57, "created_at": "2024-09-09T05:37:19.049Z" } ] }, { "id": 29847, "name": "Bucket Robotics", "slug": "bucket-robotics", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/55cc697d9f14eca5a5d45772ac14d97a83df16ae.png", "website": "https://bucket.bot", "all_locations": "San Francisco, CA, USA", "long_description": "Bucket Robotics builds deployable computer vision systems for manufacturing — without labeling, long pilots, or fragile rules.\r\nWe turn CAD and sample data into production-ready vision models that run on existing cameras and edge hardware. No manual labeling. No cold-start problem. Models deploy in minutes and adapt as parts, defects, and lines change.\r\n\r\nOur roots are in self-driving cars (Argo AI, Uber ATG, Stack AV), where we learned how to ship perception systems that operators trust in messy, real-world environments. We’re applying that playbook to factories: robust sensing, fast iteration, and tooling engineers actually enjoy using.\r\n\r\nManufacturers struggle with inspection because parts are too variable for rules-based vision and too expensive to inspect by hand. Legacy systems are rigid, hardware-locked, and slow to customize. We take the opposite approach: software-first vision that fits into existing automation and scales across SKUs, facilities, and workflows.\r\n\r\nAmerican Manufacturing is in the middle of a $700B automation push — but quality is still a trust problem. Bucket Robotics is building the vision infrastructure that lets factories automate inspection with confidence.", "one_liner": "Defect detection for manufacturing built from CAD and synthetic data.", "team_size": 8, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1719001491, "tags": [ "Robotic Process Automation", "Robotics", "Computer Vision", "Manufacturing" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "If we succeed, quality inspection stops being a human bottleneck. Manufacturing quality becomes software—deployed from CAD, scaled instantly across factories, and verified automatically. The world builds complex physical products faster, with less waste, and with machine-verifiable trust." } ], "url": "https://www.ycombinator.com/companies/bucket-robotics", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/bucket-robotics.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/bucketrobotics/", "x_url": "https://x.com/BucketRobotics", "fb_url": "https://www.facebook.com/profile.php?id=61578008815737", "cb_url": null, "github_url": "https://github.com/bucket-robotics", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/55cc697d9f14eca5a5d45772ac14d97a83df16ae.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/72627bb1c990f52131cd9089f7589fc3dc47457f.png" ], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 1918803, "full_name": "Matt Puchalski", "title": "Founder/CEO", "founder_bio": "Matt is the co-founder and CEO of Bucket Robotics, building CAD-driven computer vision for manufacturing inspection and assembly verification. Previously, he worked on autonomous vehicle integration and deployment at Argo AI, Latitude AI, and Stack AV, helping bring self-driving vehicles onto public roads in the United States and Germany.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/matt-puchalski", "x_url": "https://x.com/lasermatts", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/96e15f894eb00281539dee8a84cf516b581aac67.jpg" } ], "jobs": [ { "id": 73634, "title": "ML Engineer", "url": "https://www.ycombinator.com/companies/bucket-robotics/jobs/wamcLUR-ml-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$120K - $165K", "equity_range": "0.70% - 2.80%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "ML", "Python", "Machine Learning", "CUDA" ] }, { "id": 95125, "title": "Senior Full Stack Engineer", "url": "https://www.ycombinator.com/companies/bucket-robotics/jobs/6pT0gS3-senior-full-stack-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$160K - $220K", "equity_range": "1.00% - 3.00%", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [ "Data Modeling" ] }, { "id": 95797, "title": "Forward Deploy Engineer", "url": "https://www.ycombinator.com/companies/bucket-robotics/jobs/va6hXZz-forward-deploy-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Robotics", "salary_range": "$100K - $120K", "equity_range": "0.25% - 1.20%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "Python" ] } ], "news": [ { "title": "Synthetic Data Comes of Age: How Bucket Robotics is Changing the Economics of Machine Vision - MVPro Media", "url": "https://mvpromedia.com/synthetic-data-comes-of-age-how-bucket-robotics-is-changing-the-economics-of-machine-vision/", "date": "Jul 21, 2026" }, { "title": "How YC-backed Bucket Robotics survived its first CES | TechCrunch", "url": "https://techcrunch.com/2026/01/18/how-yc-backed-bucket-robotics-survived-its-first-ces/", "date": "Jan 18, 2026" } ], "launches": [ { "id": 82809, "title": "Bucket Robotics 🤖 Vision for Manufacturing", "tagline": "Go from CAD model to defect detection system in the blink of an eye.", "body": "# ☕ **_TL;DR_**\n\n[_Bucket Robotics_](https://bucket.bot) makes defect detection models way faster and easier to deploy. CAD goes in — detection model comes out.\n\n![uploaded image](/media/?type=post&id=82809&key=user_uploads/1918803/d49ec265-a7cd-4a9c-ad32-62b45df8c825)\n\n# **👥** **The Team**\n\n### Hi everyone, we’re [Matt](https://www.linkedin.com/in/matt-puchalski/) and [Steph](https://www.linkedin.com/in/stephan-wolski/), and we’re Bucket Robotics!\n\n![uploaded image](/media/?type=post&id=82809&key=user_uploads/1918803/a2825a9a-9dd1-4c95-8aa4-46ba2826efae)\n\n# **📉 The Problem**\n\nDefects are a universal challenge in manufacturing, particularly with methods like injection molding, die casting, and 3D printing. Factories using these methods frequently retool their lines for different products making it difficult and costly to gather enough data to train defect detection models.\n\nManufacturers strive to ensure that only the highest quality products reach their customers. Avoiding the shipment of defective products not only protects their reputation but also safeguards expensive machinery from damage, maximizes yield by leveraging advanced analytics, and prevents the progression of faulty parts through the production line, which can lead to compounded waste and increased costs.\n\nA 2017 study estimates that [yield losses account for 44% of sheet metal used](https://www.sciencedirect.com/science/article/abs/pii/S092401361730211X) in the production of passenger vehicles — and we want to bring that number down, saving money and the environment.\n\nTraditionally, defect detection comes in two flavors:\n\n1. Pay a person to watch your line.\n2. Train a vision model the old-fashioned way — wait for defective parts to come out and add your sample to the dataset.\n\nPaying people to check parts is inefficient and a miserable job. Collecting enough labeled data samples to train a model is impractical for rapidly retooled outputs, and the labeling is expensive. This often leaves manufacturers with a critical gap in quality assurance, leading to increased scrap, downtime, and potentially unhappy customers with defective parts.\n\n## **📈 The Solution**\n\nWe help eliminate the need for inefficient human monitoring and outdated defect detection by providing a system where we generate defect detection models using CAD files—something every part designed for modern manufacturing has. Our models can run on an edge computing device as small as a Jetson Orin Nano and be continually refined with real-world data.\n\n### **_Here’s why this is a game-changer:_**\n\n* **Early Deployment:** Your defect detector can arrive at your manufacturing line before you’ve manufactured any parts. This means you can catch defects right from the get-go.\n* **Continuous Improvement:** As defects start to appear, we can automatically refine and improve the model, ensuring your system remains top-notch.\n* **Classification**: The system tells you how the part is defective, allowing you to dial in your tooling configuration and maintenance cycle—e.g. when it's time to create a new mold.\n\nCheck out a video on our process here: [Bucket Robotics | Defect Detection](https://youtu.be/U_bJxaH_U9U)\n\n# **🙏How You Can Help**\n\nWe are seeking connections and collaborations to drive our mission forward. If you or anyone in your network is interested in learning more or helping out, we want to hear from you! We’re specifically looking to connect with:\n\n* Quality Assurance and Factory Automation leaders\n* Companies and brands in high-volume and precision manufacturing\n* Experts in the manufacturing and defect detection industries\n\nIf that’s you or someone you know, [please get in touch!](https://bucket.bot)", "url": "https://www.ycombinator.com/launches/LXd-bucket-robotics-vision-for-manufacturing", "votes": 77, "created_at": "2024-08-07T05:00:27.470Z" } ] }, { "id": 30210, "name": "Conntour", "slug": "conntour", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c972b0925a72b8923c709c1b48733059aafb73d3.png", "website": "https://www.conntour.com/", "all_locations": "Tel Aviv-Yafo, Tel Aviv District, Israel", "long_description": "Conntour enables government agencies to query their video - from security cameras, body cams, and drones.\r\n\r\nUsers can search past footage (“Find a man with tattoos on his left arm”), configure real-time alerts (“Notify me if someone climbs over the wall”), and extract data (“How many pickup trucks entered yesterday?”).\r\n\r\nUnlike traditional video analytics solutions, which are limited to fixed parameters (like a weapon, bag, or car color), Conntour supports an unlimited range of highly specific scenarios described in plain language. This enables a single human to monitor thousands of cameras, or find a specific moment in minutes instead of days.\r\n\r\nWhat makes Conntour's technology unique is its ability to process video using natural language at scale, fully on-premises, with minimal hardware requirements. The system is deployed with mission-critical teams at leading intelligence and homeland security agencies.\r\n\r\nConntour is backed by top-tier investors including General Catalyst and Y Combinator, was selected for the first-ever Palantir Fellowship, and has the former CTO of the CIA on its advisory board.", "one_liner": "Query mission-critical video", "team_size": 20, "industry": "Industrials", "subindustry": "Industrials -> Defense", "launched_at": 1738284252, "tags": [ "Computer Vision", "Defense", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "Industrials", "Defense" ], "regions": [ "Israel", "Middle East and North Africa" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/conntour", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/conntour.json", "year_founded": 2024, "city": "Tel Aviv-Yafo", "country": "IL", "linkedin_url": "https://www.linkedin.com/company/conntour/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c972b0925a72b8923c709c1b48733059aafb73d3.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Tom Blomfield", "url": "https://www.ycombinator.com/people/tom-blomfield" }, "founders": [ { "user_id": 1375912, "full_name": "Matan Goldner", "title": "Founder/CEO", "founder_bio": "I’m the co-founder and CEO of Conntour.\n\nI worked with security cameras for years, leading video surveillance in the Israeli military. I also led surveillance at a private security company - where I worked with most of the existing video analytics solutions.\n\nIn those roles, I used most of the existing solutions and experienced a major need that had no real solution. That experience also helped me build strong connections with global leaders in our industry.", "is_active": true, "linkedin_url": "https://linkedin.com/in/matan-goldner", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/974f6c497f392e466ec1107b5a3e6aa9decbdb9c.jpg" }, { "user_id": 405332, "full_name": "Tomer Kulla", "title": "Founder/CTO", "founder_bio": "Co-Founder and CTO at Conntour, with a BSc in Computer Science focused on computer vision. \nPreviously, I founded and sold Nilus, where I worked on eCommerce ad-tech and recommendation engines (ML). \nMy career spans both hands-on experience—particularly in web, MLOps, and big data—and leadership roles, including managing teams and as a Director of Engineering managing goup of 3 teams.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/tomer-kulla/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4507b1091a9e2181ef456272045e50b99403d0f3.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 87340, "title": "Conntour: Ask Security Cameras Anything", "tagline": "Search and monitor thousands of video feeds with simple text queries", "body": "**TL;DR**: **Conntour lets governments and corporates query their security cameras.**\n\nUsers can search past footage (“Find a man with a blue backpack”), set alerts (“Notify me if someone climbs over the wall”), and extract data (“How many vehicles entered yesterday?”).\n\nIn just a few months, Conntour secured the Singapore government as a paying customer.\n\nhttps://www.tella.tv/video/cm6k221g4000f0bl1csynd0p1/view\n\n# **Who we’re building it for:**\n\nConntour targets organizations with thousands of security cameras. That includes governments (military, police, intelligence) and businesses (corporates, retail, industrial companies). These teams can’t manage so many cameras and it leads to huge losses - money, legal violations, and even human lives.\n\n# **The Problem with existing solutions:**\n\nThey offer low customization, as they’re limited to pre-defined parameters (like shirt color or license plate number). This **lack of flexibility** causes missed critical events, too many false alarms, and countless hours wasted on manual footage review. Conntour solves this by offering **unlimited flexibility** through natural language queries - just ask anything.\n\n# **Team:**\n\nMatan (CEO) led a military video surveillance unit - leveraging security cameras to save lives. Tomer (CTO) is a second-time founder and former director of engineering.\n\nWe’ve known each other for 13 years, first meeting in the special forces during our mandatory military service. Since then, we’ve studied together and tackled many challenges side by side.\n\n![uploaded image](/media/?type=post&id=87340&key=user_uploads/1375912/ab328256-e7c8-44fe-a9c0-ee0427cfab0d)\n\nWe’d be happy to hear from you at: [matan@conntour.com](mailto:matan@conntour.com)", "url": "https://www.ycombinator.com/launches/Mii-conntour-ask-security-cameras-anything", "votes": 24, "created_at": "2025-01-31T21:45:31.892Z" } ] }, { "id": 30251, "name": "Mecha Health", "slug": "mecha-health", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e851087b2c25e5bd175d39d8631791d74e04ebb1.png", "website": "https://www.mecha-health.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Mecha Health builds foundation models to automate x-ray analysis for radiologists. We take medical images and process them using proprietary models to produce accurate draft medical reports. Our first model was built in less than two months, and beat Microsoft, Google, and OpenAI on clinical accuracy metrics. On top of that, it’s two orders of magnitude smaller and trained with a quarter of the data.\r\n\r\nWe are partnering with the largest privately owned radiology practice in the US and a multinational tele-radiology company to provide them with their own foundation model, enabling their radiologists to go from reading 1 scan per hour to 1 scan every 5 minutes. By charging on a per scan basis, x-ray report generation represents a 40B+ market opportunity. ", "one_liner": "Foundation models to automate x-ray analysis for radiologists", "team_size": 4, "industry": "Healthcare", "subindustry": "Healthcare", "launched_at": 1737153298, "tags": [ "Machine Learning", "Computer Vision", "Health Tech", "Healthcare", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "Healthcare" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/mecha-health", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/mecha-health.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/mecha-health/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e851087b2c25e5bd175d39d8631791d74e04ebb1.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1653084, "full_name": "Ahmed Abdulaal", "title": "Founder/CEO", "founder_bio": "Medical doctor @Imperial, where I gained entry to the most competitive deanery in the country. Microsoft PhD scholar in machine learning @UCL - I have more than 20 peer reviewed publications in medicine, digital healthcare, and machine learning - including first author publications in top AI venues such as ICLR. 360+ citations on google scholar. Ex Research Scientist at the Center for Artificial Intelligence @AstraZeneca. Building the future of automated radiology @Mecha Health.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ahmed-abdulaal-5b8b6a295/", "x_url": "https://x.com/AAY1993", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/47903da794d7f4d6b321cfcd16ef868b0cfcf586.jpg" }, { "user_id": 2219237, "full_name": "Hugo Fry", "title": "Founder", "founder_bio": "Mathematics (BA) and Physics (MSc) graduate from the University of Cambridge, ranking top 30 in the university. British Physics Olympiad participant, ranking in the top 11 in the UK. Former ML interpretability research scholar (MATS), where I was the first in the world to use Sparse Autoencoders on vision models. My research has been cited by Anthropic and Google DeepMind. Currently working on building state of the art radiology report generation AI in a way that is interpretable and safe.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/hugo-fry-825828271/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/79920f2bd30552a4582e64400474702ed96fb8eb.jpg" }, { "user_id": 2209530, "full_name": "Ayodeji Ijishakin", "title": "Founder", "founder_bio": "I am a Machine Learning & Medical Imaging PhD candidate from University College London (UCL). During my PhD I published work at tier-one Machine Learning venues (NeurIPS, ICML & ICLR), and was invited to present my work at international medical imaging conferences, Digitas, & Harvard Medical School. Before this startup I was poached by my Professors from UCL including the Head of Computer Science to be CEO for their spin-out. Currently building Radiology 3.0 by Automating reporting. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ayodeji-ijishakin-b6208b1a9/", "x_url": "https://x.com/AIjishakin", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/91a8103e7adc247e6c59cde54f1798dbb2c12e3f.jpg" }, { "user_id": 2272824, "full_name": "Nina Montaña Brown", "title": "Founder/CTO", "founder_bio": "Solving the interpretive moment in radiology.\n\nex-ML Engineer across multiple medical device startups, with experience in FDA and CE marked medical devices, as well as LLM safety in healthcare. My PhD in Medical Imaging (UCL) focussed on surgical vision; my work has been patented, cited 100+ times, and published across the world's highest impact scientific venues (Nature, NeurIPS, ICLR).", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nina-monta%C3%B1a-brown-6aa24b133/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4399f9dcfff9db94bacec22dd876670a6b70d5cb.jpg" } ], "jobs": [], "news": [ { "title": "Mecha Health Raises $4.1M Seed Round", "url": "https://www.mecha-health.ai/blog/Mecha-raises-seed", "date": "Nov 03, 2025" } ], "launches": [ { "id": 117908, "title": "Mecha Wayfinder: locating any finding, anywhere, in any medical image.", "tagline": "Meet Mecha Wayfinder! It locates any finding anywhere in any medical image by inspecting our foundation models, like giving the model an “MRI.”", "body": "# Mecha Wayfinder\n\nOne of the most important challenges for next generation foundation models in radiology is the ability to locate any type of pathology in across anatomies and modalities.\n\nWe’ve recently had a bit of research breakthrough on this, and today we’re introducing Mecha Wayfinder, a new tool for locating findings in medical images.\n\nTraditional systems like segmentation models are too restrictive for the task we’re trying to solve. Grad-CAM weights and upsamples a late convolutional feature map, which can leave localization spatially coarse and obscure boundaries.\n\nWayfinder takes a different approach. Think of giving the underlying AI an “MRI”: a way to inspect where the model looks, requiring zero human annotations.\n\nThe part we’re so excited about is that this approach scales with only images and reports. As we train better underlying systems, we expect Wayfinder to improve too.\n\nYou can learn all about it in our latest blog post linked below:\\\n\n\n", "url": "https://www.ycombinator.com/launches/Ufk-mecha-wayfinder-locating-any-finding-anywhere-in-any-medical-image", "votes": 3, "created_at": "2026-10-01T18:37:27.462Z" }, { "id": 97529, "title": "Mecha Health - Vision-language foundation models, that scale healthcare.", "tagline": "Today Mecha is launching a new set of capabilities, vision-language foundation models that interpret studies the way radiologists do.", "body": "Today we’re launching a new set of capabilities at Mecha: foundation models that interpret studies the way radiologists do, across anatomy, modality, views, and priors, while fitting into any workflow or software environment.\n\n\\\n\n\n\\\nWhat’s new is less about “one more model”, and more about a human-like feature set for real workflow:\n\n* Multi-anatomy interpretation\n* Multi-modality support\n* Multi-view reasoning with full clinical context\n* Prior-aware comparison: identifies clinically relevant change vs prior exams\n* Environment-agnostic delivery: a radiologist opens the study and finds a high-quality draft report already waiting in their template.\n\nThese new capabilities are designed to help frontier practices scale healthcare, without asking radiologists to relearn their workflow.\n\n### Our Ask\n\n* If you’re evaluating next-generation reporting AI, or know a group that is, we’d love to talk.\n\n**Please reach out directly at [ahmed@mecha-health.ai](mailto:ahmed@mecha-health.ai) - you can also check us out at** [**https://mecha-health.ai**](https://www.mecha-health.ai/)", "url": "https://www.ycombinator.com/launches/PN3-mecha-health-vision-language-foundation-models-that-scale-healthcare", "votes": 9, "created_at": "2026-02-19T18:31:53.538Z" }, { "id": 87081, "title": "Mecha Health 🩻 Trustworthy medical image reporting using AI", "tagline": "Automated analysis of medical images that is accurate and interpretable.", "body": "**TLDR;** \\\nMecha Health helps radiologists save time and effort by producing draft reports of medical images using AI. \\\n\\\n**The Problem: Too Many Scans Not Enough Doctors.** \\\nDue to a shortage of experts, US radiologists have to read 100 - 250+ studies per day, and this means that 55% have reported high levels of burnout. Additionally, patients sometimes have to wait days to receive medical reports for their scans due to backlogs. At times, this can result in preventable patient harm. \\\n\\\n**Our Solution: Accurate & Interpretable Medical Image Analysis.** \\\nWe improve the reporting process by producing draft reports with AI and have invented new science to do this both accurately and safely. Our system can trace every finding to its visual source in the image and evidence each finding with examples from the training dataset. This allows for transparent reporting that doctors can trust.\n\n![uploaded image](/media/?type=post&id=87081&key=user_uploads/2272824/2dd6b41e-c6f1-46a8-b932-678f32885952)\n\n**Our Background: Frontier Scientific Expertise.** \\\nWe are leading researchers in machine learning and medical imaging having published in tier-one AI venues (NeurIPS, ICLR, ICML). [Nina](https://www.linkedin.com/in/nina-monta%C3%B1a-brown-6aa24b133/), [Ahmed](https://www.linkedin.com/in/ahmed-abdulaal-5b8b6a295/) & [Deji](https://www.linkedin.com/in/ayodeji-ijishakin-b6208b1a9/) crossed paths through University College London’s Medical Imaging PhD program. [Hugo](https://www.linkedin.com/in/hugo-fry-825828271/) met the team after he published AI research that was cited by Anthropic. Ahmed, a trained medical doctor from Imperial College London, witnessed firsthand how reporting delays harm patient outcomes—driving Mecha Health’s mission to transform radiology.\n\n![uploaded image](/media/?type=post&id=87081&key=user_uploads/2272824/0785d872-c2c8-4a5f-ae6b-f06069c9ec95)\n\n**Our Ask: Intros to Pioneering Medical Professionals and Healthcare Executives.** \\\nPlease reach out to the team at [ahmed@mecha-health.ai](mailto:ahmed@mecha-health.ai) if you know any tech-forward clinicians, radiologists, radiology practices, or healthcare executives who could benefit from our technology. You can also check us out at .\n\n**Watch our full product video here** \\\n[**https://youtu.be/s5uJS7S8EF4**](https://youtu.be/s5uJS7S8EF4)", "url": "https://www.ycombinator.com/launches/MeX-mecha-health-trustworthy-medical-image-reporting-using-ai", "votes": 37, "created_at": "2025-01-23T03:22:50.784Z" } ] }, { "id": 30315, "name": "Optifye.ai", "slug": "optifye-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/d86ffdce2296389239f29bc23023bc7867815208.png", "website": "https://www.optifye.ai", "all_locations": "New Delhi, DL, India", "long_description": "Optifye.ai does AI performance monitoring for factory workers\r\n\r\nWe place cameras in factories and use vision AI to tell supervisors who's working and who's not in real time. \r\n\r\nThe shop floor has historically been a black box. With Optifye, manufacturing companies can now accurately measure worker output and boost efficiency!", "one_liner": "AI performance monitoring for factory workers", "team_size": 8, "industry": "B2B", "subindustry": "B2B", "launched_at": 1739567184, "tags": [ "Hard Tech", "Computer Vision", "Manufacturing" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "B2B" ], "regions": [ "India", "South Asia" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/optifye-ai", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/optifye-ai.json", "year_founded": 2024, "city": "New Delhi", "country": "IN", "linkedin_url": "https://www.linkedin.com/company/optifye-ai/?viewAsMember=true", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/d86ffdce2296389239f29bc23023bc7867815208.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Brad Flora", "url": "https://www.ycombinator.com/people/brad-flora" }, "founders": [ { "user_id": 1289176, "full_name": "Vivaan Baid", "title": "Founder/CEO", "founder_bio": "I studied computer science & psychology at Duke. My family has been running a manufacturing company since before I was born. I've been around assembly lines for as long as I can remember.\n\nAt Optifye, I am using my expertise in computer vision to enable supervisors to solve shop-floor inefficiencies in real-time.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/vivaan-baid-119415177/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f9c6b9b6347741a6d9c5e1f2925f6fe792d6e654.jpg" }, { "user_id": 2122711, "full_name": "Kushal Mohta", "title": "Founder", "founder_bio": "Building Optifye.ai. Previously built a system to boost efficiency by 105% of semi-automated lines at an automobile gears manufacturing company. My family also runs several manufacturing plants in various industries, which has given me unrestricted access to assembly lines since I was 15.\n\n Studied for a B.S.E in Electrical and Computer Engineering and B.S. in Computer Science at Duke University.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/kushal-mohta-6926931b4/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d2a8ab3d0987d09ccf90ab6ef67b652990a20d12.jpg" } ], "jobs": [ { "id": 74288, "title": "Founding Engineer", "url": "https://www.ycombinator.com/companies/optifye-ai/jobs/sDfGXkK-founding-engineer", "location": "New Delhi, DL, IN / New Delhi, Delhi, IN / Gurugram, HR, IN / Gurugram, Haryana, IN", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "₹2.4M - ₹4.8M INR", "equity_range": "0.10% - 0.30%", "experience": "1+ years", "visa": "US citizenship/visa not required", "skills": [ "Amazon Web Services (AWS)", "Kubernetes", "PostgreSQL", "Machine Learning", "Docker", "Computer Vision" ] }, { "id": 79224, "title": "Founding Engineer - AI/ML", "url": "https://www.ycombinator.com/companies/optifye-ai/jobs/NQxhJPr-founding-engineer-ai-ml", "location": "Gurugram, HR, IN / Gurugram, Haryana, IN", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "₹2.4M - ₹3.6M INR", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [ { "id": 87931, "title": "Optifye.ai - AI Performance monitoring for factory workers", "tagline": "Solve shop-floor inefficiencies in real-time", "body": "Hi everyone, we’re Kushal & Vivaan - co-founders of Optifye.\n\nWe’re CS grads from Duke and because our families run manufacturing companies, we’ve seen more assembly lines than most industrial engineers!\n\n**Tl;dr;** We place cameras in manufacturing lines and use computer vision to help supervisors solve shop-floor inefficiencies in real-time.\n\n# **The Shop floor is a black-box.**\n\nThere has never been a way to accurately measure shop-floor performance. It is also understaffed; each supervisor, on average, is responsible for 50+ workers. \n\nCompanies struggle to boost efficiency because they cannot determine what caused the problem. \n\nAsk any manufacturing company owner about the last time they reached their productivity targets and you will get a very long, frustrated answer.\n\n# **For the first time ever, Optifye makes it… not a blackbox!**\n\nWe place cameras in manufacturing lines and use computer vision to count the number of pieces each workspace makes in real-time.\n\nWe then relay this information on a dashboard.\n\n![uploaded image](/media/?type=post&id=87931&key=user_uploads/1289176/58cd8240-7fe5-4884-b809-581905b11f89)\n\nWe believe that if you can measure it, you can improve it. \n\n![uploaded image](/media/?type=post&id=87931&key=user_uploads/1289176/dd1da477-3736-42fb-8f82-f76d7d32c77b)\n\n# **What this means for the ____**\n\n**Owner**: Gets accurate real-time factory, line, and workspace productivity metrics - no need to rely on your production head (who never has the incentive to say production looks terrible)\n\n**Production Head**: Gets line-wise and workspace-wise metrics to identify areas for improvement\n\n**Shopfloor supervisor**: Can finally identify what is causing inefficiency in the line and fix the problem on the go\n\nWe align every stakeholder with manufacturing companies’ primary KPI – efficiency.\n\nKnow any manufacturing company owners? \n\nLet us know at [founders@optifye.ai](mailto:founders@optifye.ai), and we’ll help them drop their cortisol levels :) \n\n![uploaded image](/media/?type=post&id=87931&key=user_uploads/1289176/387126da-5b03-41ab-b442-9f2ca0dbcb86)\n\n", "url": "https://www.ycombinator.com/launches/MsF-optifye-ai-ai-performance-monitoring-for-factory-workers", "votes": 82, "created_at": "2025-02-23T02:27:13.182Z" } ] }, { "id": 30317, "name": "Permitify", "slug": "permitify", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e0fb1e449f45c8f9ca33c6947992cac6af3e8c0f.png", "website": "https://permitify.com", "all_locations": "Salt Lake City, UT, USA", "long_description": "City building departments are understaffed and overworked. Meanwhile, builders lobby to remove power from building departments. Because building departments are usually the only profitable/self-sustaining branch of a local government, this is problematic. Permitify allows city build-plan reviewers to 10x their productivity covering the labor shortage, reducing the time required to issue a building permit, and allowing the city government's only profitable branch to stay intact.", "one_liner": "AI co-pilot for building plan review and building code compliance", "team_size": 2, "industry": "Real Estate and Construction", "subindustry": "Real Estate and Construction -> Construction", "launched_at": 1737593819, "tags": [ "Generative AI", "GovTech", "Computer Vision", "Construction" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "Real Estate and Construction", "Construction" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/permitify", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/permitify.json", "year_founded": 2025, "city": "Salt Lake City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/permitifyco", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e0fb1e449f45c8f9ca33c6947992cac6af3e8c0f.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/3ca481c7dbe72abf2aeea46d976b91f712a68630.png" ], "primary_partner": null, "founders": [ { "user_id": 1681156, "full_name": "Adam Chandler", "title": "Founder/CEO", "founder_bio": "Adam is the co-founder and CEO of Permitify. \n\nBefore founding Permitify, I built and scaled a permit-expediting company with my co-founder, Alexander. Within the first month, we scaled from $0 to $40k ARR, serving clients across six states. While working to scale the business, we encountered the problems that we now solve with Permitify.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/adam-chandler99/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2d6b8a2ae6491b7161ad189b8ae3fd4e2901fd52.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 30446, "name": "Kirana AI", "slug": "kirana-ai", "former_names": [ "Kirana" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6bc9cf5c0f4cd5c6141c30f400757b42039051c6.png", "website": "https://www.kirana-ai.com", "all_locations": "New York City, NY, USA", "long_description": "Kirana AI is building the first full-stack AI store manager.\r\n\r\nWith 100 years of family experience owning and operating grocery stores, we are starting there to prove our impact.\r\n\r\nOur first product utilizes an on-premise GPU that monitors the store 24/7, scans for theft, looks for workplace health and safety issues, and optimizes customer service. \r\n\r\nWith future POS/ordering data integrations, our manager will automate many of the day-to-day tasks of running a store, such as ordering, pricing, assortment, and delegating.", "one_liner": "AI manager for physical stores", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Retail", "launched_at": 1747354989, "tags": [ "Grocery", "Computer Vision", "Retail", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 2025", "status": "Acquired", "industries": [ "B2B", "Retail" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/kirana-ai", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/kirana-ai.json", "year_founded": 2025, "city": "New York City", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6bc9cf5c0f4cd5c6141c30f400757b42039051c6.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "David Lieb", "url": "https://www.ycombinator.com/people/david-lieb" }, "founders": [ { "user_id": 1910327, "full_name": "Nicholas Sleeper", "title": "Founder/CEO", "founder_bio": "Co-Founder & CEO of Kirana AI, an AI store manager for grocery stores. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nicholas-sleeper-209641179/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/89e4a39ad68ce5459dc028589523d71290e5c6d6.jpg" }, { "user_id": 2456105, "full_name": "Prateek Pinisetti", "title": "Founder/CTO", "founder_bio": "CTO @ Kirana AI, Ex-Optiver SWE, Chess National Master.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/prateek-pinisetti-606287171/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2efcc2a13a7461027d545752f71a8c5551865165.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 90643, "title": "Kirana AI: Full-Stack AI Store Manager", "tagline": "Grocery stores just got smarter—say hello to our AI manager.", "body": "Prateek and I are building a new full-stack manager for grocery stores. Growing up, in the summers I worked at my family's grocery store, and saw first hand the challenges my father went through on a day to day basis. My family's store was started over 100 years ago, and with this industry experience, Prateek and I are able to offer new insights.\\\n[Overview](https://youtu.be/VEAZGZZ4_RU) \n\n\\\n**The Problem: Operating an independent grocery store means surviving on razor-thin margins.**\n\n* 3 % of sales vanish due to shrinkage.\n* Out of stocks wipe out 4% more in lost sales\n* Slip-and-fall hazards lead to liability risks.\n* These slim margins prevent grocers from hiring back office staff.\n\n**What We Built-**\n\n* 24/7 Theft Detection – Real-time alerts when someone crosses the “no-go” zones behind registers or swipes merchandise.\n* Safety Monitoring – Automatic slip-and-fall hazard spotting so you can fix risks before they become claims.\n* Shelf & Stock Insights – Instant out-of-stock notifications to keep product levels optimized.\n* Pricing Research - Soon we will introduce an agentic workflow to manage one of grocers' most tedious tasks, looking up competitors prices.\n\n**Our Team-**\n\n* Nic Sleeper, CEO – Former SWE at PredictionStrike.\n* Prateek Pinisetti, CTO – Ex-Optiver SWE and chess National Master; real-time systems expert.\n* We met as Harvard roommates, did psets together in our dorm, and have been collaborating ever since.\n\n\\*\\*The Vision-\\*\\*\\\nWe chose to work in this space to help local grocers thrive in the AI age and be on the forefront of innovation. Our cameras are just the first step- by combining POS, ordering, and camera data, we can automate every back-office process in the store, allowing them to achieve their optimal margins.\\\n\\*\\*Our Ask-\\*\\*\\\nIf you know any grocery or retail store owners who’d love a tireless 24/7 assistant to cut shrink, boost safety, and maximize sales, please loop us in!", "url": "https://www.ycombinator.com/launches/NZz-kirana-ai-full-stack-ai-store-manager", "votes": 17, "created_at": "2025-05-22T14:43:07.181Z" } ] }, { "id": 30541, "name": "Code Four", "slug": "code-four", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/484fe167f81b504f3783e0d04f12060ec5bf7d11.png", "website": "https://codefour.us/", "all_locations": "San Francisco, CA, USA", "long_description": "Code Four slashes the administrative burden on law enforcement through AI-driven automation. Officers spend up to a third of their shifts manually transcribing body camera footage, creating inaccuracies, reducing transparency, and cutting into frontline duties. We're here to eliminate these inefficiencies by integrating with body cameras, record management systems, and digital evidence management systems—acting as a software layer for data processing. \r\n\r\nWe free the most overworked and underappreciated people in the world: police officers.", "one_liner": "Modernizing the police workflow.", "team_size": 3, "industry": "Government", "subindustry": "Government", "launched_at": 1744335489, "tags": [ "GovTech", "Computer Vision", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Spring 2025", "status": "Active", "industries": [ "Government" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/code-four", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/code-four.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/codefourus/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/484fe167f81b504f3783e0d04f12060ec5bf7d11.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/02800ca23a7b095f32e58dcf05e366f9f6e2d11e.png" ], "primary_partner": { "name": "David Lieb", "url": "https://www.ycombinator.com/people/david-lieb" }, "founders": [ { "user_id": 2369150, "full_name": "George Cheng", "title": "Founder/CEO", "founder_bio": "AI/CS + Brain & Cog Sci @ MIT", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/cheng-george/", "x_url": "https://www.x.com/gcheng713", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0cd8b9757ee003ab7c5551c608054194ef7a749c.jpg" }, { "user_id": 2382592, "full_name": "Dylan Nguyen", "title": "Founder/CTO", "founder_bio": "Founder @ Code Four | Applied Math + CS @ MIT", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/dylan-nguyenn", "x_url": "https://x.com/dynguyen0", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d848f67c19bfca713193918fa239a74c062cedad.jpg" } ], "jobs": [ { "id": 79247, "title": "Founding Fullstack Software Engineer", "url": "https://www.ycombinator.com/companies/code-four/jobs/tIBkEwS-founding-fullstack-software-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$130K - $300K", "equity_range": "0.25% - 2.00%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Google Cloud", "GraphQL", "Node.js", "Python", "TypeScript", "Firebase", "Docker", "Next.js" ] }, { "id": 85122, "title": "Account Executive", "url": "https://www.ycombinator.com/companies/code-four/jobs/MSOzEim-account-executive", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$100K - $250K", "equity_range": "0.25% - 1.00%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [ { "id": 90692, "title": "Code Four – Bringing Our Cops Home", "tagline": "Code Four handles paperwork and frees officers from administrative burdens.", "body": "#### **TL;DR**\n\nWe auto-generate reports, verify narratives, and surface evidence instantly — cutting desk time by 60% and putting officers where communities need them most.\n\nhttps://youtu.be/5K72MBx20fg\n\n#### **👮‍♂️ The Problem**\n\n* **40% of a typical shift** vanishes to transcribing body-cam footage, filling forms, and locating clips across siloed systems.\n* Manual redaction and video scrubbing **delay** investigations, **strain** budgets, and **erode** public trust.\n* Departments **juggle 8–10 incompatible** software tools that weren’t built for today’s fast-paced operations\n\n#### **🚓 Our Solution – the Intelligence Layer for Digital Evidence**\n\n1. **Real-time transcription & summarization** — every body-cam or interview video becomes searchable text + key-frame timelined.\n2. **One-click redaction** — faces, plates, minors’ voices, and PII are masked with CJIS-compliant logs.\n3. **Context-aware search** — ask “show me every time suspect #42 handled a weapon” and jump straight to the frames.\n4. **Autofilled narrative reports** that match your RMS schema and chain-of-custody rules.\n5. **Plug-and-play integrations** with leading body-cam vendors, evidence lockers, and CAD/RMS APIs—no rip-and-replace.\n\n#### **👥 Team**\n\nGeorge Cheng, Dylan Nguyen, and Natnael Kahssay started Code Four inside their dorm rooms at MIT and dropped out to continue building the next generation of police tech. We’ve processed hundreds of videos and know every pain point from ride-alongs to record rooms.\n\n![uploaded image](/media/?type=post&id=90692&key=user_uploads/2369150/be5e77c0-a578-4632-873a-a4faf530de19)\n\n#### **🙌 Our Ask**\n\n* **Intros to Police, Sheriff, Transit, or University PDs** that wrestle with video or paperwork overload.\n* **Design-partner agencies** (10–200 officers) willing to co-shape features.\n* **We’ll fly anywhere in North America to set you up within 48 hours—no strings attached.**\n\nEmail → [founders@codefour.us](mailto:founders@codefour.us)   \n\nCalendar →[ https://cal.com/codefour/30min](https://cal.com/codefour/30min)   \n\nWebsite →[ https://www.codefour.us](https://www.codefour.us)\n\n**Code Four. Built for Heros. Made to bring them home.**\n\n![uploaded image](/media/?type=post&id=90692&key=user_uploads/2369150/8c0bbba7-4edd-42c3-8efd-12facc362e44)\n\n", "url": "https://www.ycombinator.com/launches/Nam-code-four-bringing-our-cops-home", "votes": 29, "created_at": "2025-05-23T21:20:05.965Z" } ] }, { "id": 30620, "name": "Bidflow", "slug": "bidflow", "former_names": [ "Playdo.ai", "Ghostship" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/fb5bdff2af780675e7950b4a0fb5dcad2dfdc802.png", "website": "https://usebidflow.com", "all_locations": "New York City, NY, USA", "long_description": "We use AI to help electricians speed up the time it takes to submit a competitive bid to a job. We do this by scanning complex CAD drawings and 10X-ing the time it takes to estimate a job. This involves using vision models to read and count complex symbols in large PDFs, which typically take hours (sometimes days) to do precisely. Every missed symbol can cost thousands, and our AI can count to 99% accuracy.", "one_liner": "AI Takeoffs for Electrical", "team_size": 2, "industry": "Real Estate and Construction", "subindustry": "Real Estate and Construction -> Construction", "launched_at": 1754285936, "tags": [ "SaaS", "Computer Vision", "Construction", "Infrastructure" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2026", "status": "Active", "industries": [ "Real Estate and Construction", "Construction" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/bidflow", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/bidflow.json", "year_founded": 2025, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/110460044", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/fb5bdff2af780675e7950b4a0fb5dcad2dfdc802.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/2ccef533b872120f2c409dfa7281f478dc5d02ff.png" ], "primary_partner": { "name": "Ankit Gupta", "url": "https://www.ycombinator.com/people/ankit-gupta" }, "founders": [ { "user_id": 1512811, "full_name": "Jesse Choe", "title": "Founder/CEO", "founder_bio": "College dropout. Building AI software for electrical contractors.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jc10/", "x_url": "https://x.com/jesse_choe10", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/df7d3df3631ef86be1acce92e6aa46ae90ba4344.jpg" }, { "user_id": 2694876, "full_name": "Gautham Ramachandran", "title": "Founder/CTO", "founder_bio": "taking care of change orders", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/gautham-ramachandran-74b5712a4/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7f61fd9ece592bdfecd3b2104c37e873db832e7d.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 96918, "title": "Bidflow - AI Copilot for Electrical Estimating", "tagline": "We use AI to help electricians compete for 4x more contracts", "body": "Hi!\n\nWe’re [Jesse](https://www.linkedin.com/in/jc10/) and [Gautham](https://www.linkedin.com/in/gautham-ramachandran-74b5712a4/), and we’re building [Bidflow](https://usebidflow.com/) to help electricians bid on more jobs. Bidflow is an AI copilot that helps electrician companies sift through tedious documents and automate the repetitive work that goes into quoting a job.\n\nAsk: Do you know any electrical contractors? If so, introduce us at [founders@tryghostship.dev](mailto:founders@tryghostship.dev) or [book a demo with us](https://calendly.com/jessechoe10/demo-with-bidflow).\n\n# The Problem\n\nElectrical estimating - the process of figuring out how much an electrical construction project will cost is super tedious and can take anywhere from a few hours to months. Imagine reading through 500-page PDFs, counting hundreds of small symbols per page on 50 pages of complicated circuits, and manually entering thousands of numbers on slow, legacy software.\n\nElectrical estimators do this process everyday but manually, and most of the time, they lose the job by a small margin. Estimating is the only way electrical contractors make revenue, and they do it manually because one mistake can cost thousands of dollars and they need to be careful!\n\n# Our Solution\n\nBecause estimating is such a careful process, electrical contractors still do everything manually, especially because AI is hard to trust. That’s why we built Bidflow - an AI copilot for electrical estimating that serves as an assistant not a replacement.\n\nBidflow is designed with context to think like an electrician and most importantly, show the estimator “proof of work”, turning the estimators job more into reviewing the AI’s mistakes. We’re able to process all of your project documents and save you time in reading through hundreds of pages of fine print.\n\nBidflow also has the same features as the legacy softwares these estimators use, but it combines everything together, making it an all-in-one platform that also saves estimators time switching between the softwares.\n\n\n\n### Our Team\n\nWe decided to target electrical contractors because we both have family in the industry who’ve complained about how slow work has been. We then decided to go door-to-door in the Bay Area and got our first design partner.\n\nJesse (CEO, right) - **Top 1%** competitive coder in the US; **Jane Street** extern. Gautham (CTO, left) - Bootstrapped to **$120K** at **16**.\n\n![uploaded image](/media/?type=post&id=96918&key=user_uploads/1512811/3c223688-5b55-49b7-b1bf-e08439b4b1e4)\n\n### Why Now?\n\nConstruction is picking up in the United States, and the demand for electrical work is going up due to the data center boom. New data centers are being built left and right, meaning there’s a lot of electrical construction projects in the market. A huge boost in estimating efficiency would give the faster electrical contractors a huge advantage in winning jobs, and with AI agents getting better now more than ever, now is the time to bring AI into the electrical estimating workforce.", "url": "https://www.ycombinator.com/launches/PDC-bidflow-ai-copilot-for-electrical-estimating", "votes": 16, "created_at": "2026-01-19T04:48:20.442Z" }, { "id": 92813, "title": "Ghostship - QA agents to find bugs in your product", "tagline": "Ghostship helps you 10x your UX by catching bugs before production.", "body": "Hey guys! \n\nWe're [Jesse](https://x.com/jesse_choe10) and [Gautham](https://x.com/TheGauthamRam), and we're building [Ghostship](https://tryghostship.dev/?utm_source=yc&utm_medium=launch&utm_campaign=yc_s25) to help you never ship a bug again. Ghostship spins up AI agents to find bugs in your product by crawling through your interface and simulating users.\n\nAsk: Do you care about shipping a good user experience? You definitely should and if so, email us at [founders@tryghostship.dev](mailto:founders@tryghostship.dev) or [book a demo with us](https://calendly.com/jessechoe10/demo-with-ghostship-yc-s25).\n\n# The Problem\n\nTo grow quickly, you probably rush shipping new features, which often introduces bugs and causes customer churn. Current solutions like manual testing is expensive and slow and automated testing is flaky.\n\n# Our Solution\n\nImagine a QA team of 5,000 testers available 24/7 on every new feature you ship. That's Ghostship, but we use AI agents. \n\nOur agents will crawl through your product and interact with it like users would to find the bugs (especially edge cases) that lead to churn—all at a fraction of the cost.\n\nHere’s a demo video of our product:\n\nhttps://youtu.be/THa43hQMDEU\n\nOur Team\n\n**TL;DR: We are competitive programmers that never finished college, so we're all in on Ghostship.**\n\nJesse (CEO, right) - Jesse was a **top 1%** competitive coder in the US and was the **first high schooler** to write competitive coding questions. He was a **Jane Street** intern at **18** before dropping out.\n\nGautham (CTO, left) - Gautham was a **top 5%** competitive coder in the US. He also has 3 preprints related to **reinforcement learning** in the **ICML** conference and bootstrapped his way to **$120K** at **16**.\n\n![uploaded image](/media/?type=post&id=92813&key=user_uploads/1512811/b271ce7f-ac3f-465c-95b6-0fe28b05a408)\n\nBoth of us have been developers for more than half our lives, and we've experienced the pain of shipping bugs throughout our lives. We want to solve this problem once and for all, and make developers' lives way easier.\n\n# Our Ask\n\n\\- Follow us on [LinkedIn](https://www.linkedin.com/company/ghostship), [X](https://x.com/ghostship_ai), and [book a demo](https://calendly.com/jessechoe10/demo-with-ghostship-yc-s25) with us.\n\n\\- **Share this post!** - If you know any startup founders who are having trouble with growth due to technical debt or product people (PMs, QAs, etc.) at larger companies who care about shipping quality, please share this post with them.\n\n\\- Visit [our website](https://tryghostship.dev/) for more info.", "url": "https://www.ycombinator.com/launches/O8z-ghostship-qa-agents-to-find-bugs-in-your-product", "votes": 132, "created_at": "2025-08-07T19:28:30.592Z" } ] }, { "id": 30652, "name": "OnDeck AI", "slug": "ondeck-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9fb6ad4cbd4cdfac49ff1b40404d7053d5789883.png", "website": "https://www.ondeckai.com", "all_locations": "Vancouver, BC, Canada", "long_description": "OnDeck is the infrastructure layer that makes Vision Language Models accessible and scalable for enterprise. Our model, Perception-0, let organizations instantly find any object, behavior or event, in any footage, without needing to train a model or collect any training data.\r\n\r\nThe Pain: Creating vision models usually takes months: collecting training data, training, then deployment. Worse yet: \r\n+ it’s often impossible to get enough data for a specific task, and \r\n+ even the best cv models struggle to generalize across diverse camera setups, workflows and environments. \r\n\r\nTo overcome these blockers, we bet early on the power of VLMs and built a vision engine that can generalize across any task and doesn’t need any training data. We published a NeurIPS workshop paper showing our new methods with VLMs beat traditional CV even at niche tasks. Now, our model Perception-0 beats the best VLMs including Gemini at long video understanding.\r\n\r\nOur current customers include:\r\n- National Defense Organizations\r\n- Robotics Research\r\n- Security cameras\r\n- Behaviour analysis for port monitoring\r\n- Off-shore oil & gas monitoring", "one_liner": "Analyze any footage, without training a model", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1755488960, "tags": [ "Machine Learning", "SaaS", "Computer Vision", "B2B", "Video" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "B2B", "Analytics" ], "regions": [ "Canada", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/ondeck-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/ondeck-ai.json", "year_founded": 2025, "city": "Vancouver", "country": "CA", "linkedin_url": "https://www.linkedin.com/company/ondeckai/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/9fb6ad4cbd4cdfac49ff1b40404d7053d5789883.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Brad Flora", "url": "https://www.ycombinator.com/people/brad-flora" }, "founders": [ { "user_id": 1161477, "full_name": "Alexander Dungate", "title": "Founder/CEO", "founder_bio": "Solving vision @ OnDeck. BSc in CS + biology, National Geographic Explorer.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alexander-dungate/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2cb92fb1e2f11f9bd040c8681d3875328c486091.jpg" }, { "user_id": 1148628, "full_name": "Sepand Dyanatkar", "title": "Founder/CTO", "founder_bio": "Solving vision. Masters @ Cambridge in multi-agent RL. Research in swarm robotics for space, and built software at Amazon and the European Space Agency. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/sepandd", "x_url": "https://x.com/SepandD", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9b396c35a4599135d95d8b377d1bace2b0ca3453.jpg" } ], "jobs": [ { "id": 81971, "title": "Founding Engineer", "url": "https://www.ycombinator.com/companies/ondeck-ai/jobs/IQTL9o5-founding-engineer", "location": "Vancouver, BC, CA / Vancouver, British Columbia, CA", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$100K - $200K CAD", "equity_range": "0.10% - 1.50%", "experience": "Any (new grads ok)", "visa": "US citizenship/visa not required", "skills": [] } ], "news": [], "launches": [ { "id": 93172, "title": "OnDeck AI - Analyze any video, without training a model.", "tagline": "We let organizations instantly find any object, behavior or event, in any footage, without needing to train a model or collect any training data.", "body": "Hey everyone 👋\n\nWe’re Alexander and Sepand, cofounders of [OnDeck AI](https://ondeckai.com).\n\n### TL;DR\n\nOnDeck lets you analyze any video, without training a model. Instantly find any event, behaviour, or object without needing any training data.\n\n### Ask:\n\n**Do you know anyone who needs to analyze tons of video or imagery? Please introduce us!**  [alexander@ondeck-ai.com](mailto:alexander@ondeck-ai.com)\n\n\\[[https://youtu.be/MafiM94IkV8\\\\\\]](https://youtu.be/MafiM94IkV8%5C%5D)\n\n### The Pain:\n\nCreating vision models can take months of engineering time: collecting training data, training, then deployment.  Worse yet: \n\n* it’s often impossible to get enough data for a specific task, and \n* even the best cv models struggle to generalize across diverse camera setups, workflows and environments.\n\n \n\n### Our Magic:\n\nTo overcome these blockers, we bet early on the power of VLMs and built a vision engine that can generalize across any task and doesn’t need any training data. We **published a NeurIPS workshop paper** showing our new methods with VLMs beat traditional CV even at niche tasks.\n\nWhy this is good:\n\n* We never train on customer data, unlocking sensitive use cases.\n* Deploy instantly and skip months of R&D.\n* Identify things that have never been seen before.\n* Unlike traditional cv, OnDeck can **_understand_** qualitative behaviour and sequences of events.\n\nThis summer we’ve analyzed thousands of hours of footage across:\n\n* Fleets of Autonomous Surface Vessels \n* Robotics Research\n* Security cameras \n* Behaviour analysis for port monitoring\n* Off-shore oil & gas monitoring\n\n### The Team:\n\nSepand (CTO) has a Master in ML from Cambridge and worked at the European Space Agency, Amazon, and swarm robotics research labs. We both studied cs in university together and we’ve been best friends since grade 6. We originally started OnDeck to automate fisheries monitoring (counting fish in video 🐠). We generated six-figure revenue before pivoting in January.\n\n![uploaded image](/media/?type=post&id=93172&key=user_uploads/1161477/796316a1-6848-4ece-a2b3-cc5e56514825)\n\n**Ask: Do you know anyone with loads of video or imagery to analyze? Please introduce us!\\\n\\\nPing us anytime:** [alexander@ondeck-ai.com](mailto:alexander@ondeck-ai.com) / [ondeckai.com](http://ondeck-ai.com) / [Linkedin](https://www.linkedin.com/company/ondeckai/) / [Twitter](https://x.com/ondeckai)", "url": "https://www.ycombinator.com/launches/OEm-ondeck-ai-analyze-any-video-without-training-a-model", "votes": 34, "created_at": "2025-08-20T01:13:05.704Z" } ] }, { "id": 30821, "name": "Efference", "slug": "efference", "former_names": [ "Remnant Robotics" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/54d1482241918ee48c1c994945fd8c443280d29b.png", "website": "https://efference.ai", "all_locations": "San Francisco, CA, USA", "long_description": "", "one_liner": "", "team_size": 12, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1762259214, "tags": [ "Robotics", "Computer Vision" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2025", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/efference", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/efference.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "https://x.com/EfferenceAI", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/54d1482241918ee48c1c994945fd8c443280d29b.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Pete Koomen", "url": "https://www.ycombinator.com/people/pete-koomen" }, "founders": [ { "user_id": 1945409, "full_name": "Gianluca Bencomo", "title": "Founder/CEO", "founder_bio": "-", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/gianluca-bencomo-326469133", "x_url": "https://x.com/gianlucabencomo/", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/abf3b37c947d65d07ff29aff6adf54e459eb04ac.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 95175, "title": "Efference - Helping robots see like humans do", "tagline": "Efference is building the most intelligent robotic camera on the market -- at half the cost. ", "body": "![uploaded image](/media/?type=post&id=95175&key=user_uploads/1945409/8214e17c-76b2-4c77-9b83-f11af13ff298)\n\nHey YC, I’m Gianluca. I’m building robotic cameras (stereo cameras) that treat vision as a software problem, not a hardware problem – just like humans do.\\\n\\\nWe are actively taking pre-orders with delivery in March: https://efference.ai/preorder\n\n[**https://youtu.be/0v0p2t3y4fI**](https://youtu.be/0v0p2t3y4fI)\n\nMany robotics founders that I talk to are frustrated with current stereo cameras. They are expensive, require calibration, and still produce unreliable depth measurements despite their costs. Vision shouldn’t be an additional bottleneck when training robotic policies is already hard enough.\n\nOur insight is that humans don’t have precise sensors: our eyes are noisy and imprecise but we run extremely sophisticated algorithms on top of them. Robotics already embraced this philosophy for arms (_cheap hardware + great algorithms_ → \\[Zhao et al., 2023\\]). We’re applying it to cameras by developing better algorithms.\n\nWhat we built:\n\n* A 3D perception model that combines stereo triangulation + learned priors about objects and environments.\n* It runs in real time (unlike current neural depth approaches).\n* We are distilling the model into our own hardware and shipping model wrappers for Realsense/ZED cameras so you can run these cameras better.\n\nCheck out the designs (going into production soon):\n\n[**https://www.youtube.com/watch?v=oOmf4k2ZwaY**](https://www.youtube.com/watch?v=oOmf4k2ZwaY)\n\n**Does it work?**\n\nSelf-Driving: [**https://www.youtube.com/watch?v=0f058JAk64A**](https://www.youtube.com/watch?v=0f058JAk64A)\n\nRobotic Manipulation (via Functional Manipulation Benchmark): [**https://www.youtube.com/watch?v=GyV12gqGOMY**](https://www.youtube.com/watch?v=GyV12gqGOMY)\n\nAnd many more demos coming to the YouTube channel: [https://www.youtube.com/@Efference](https://www.youtube.com/@Efference%5C)\n\n**Why do we need this?**\n\nConverting a vehicle to full autonomy can cost upwards of **$100,000**. The economics are terrible: the sensor stack dominates lifetime cost, keeping cost-per-mile high and slowing deployment. A stereo-vision stack like ours cuts that cost by **10–100×**. Lower integration cost → cheaper replacements → larger fleets → autonomy that scales. We know we can drive a car using a stereo camera because that is what humans use. It’s just a matter of getting the camera to work as well as the one humans have.\n\nThe same dynamic applies to humanoids, perhaps at an even greater scale. Companies like Unitree ship tens of thousands of robots every year, all with stereo cameras, and consumers often complain about quality. We’re building a camera that is both **affordable and usable**, so teams can actually build with it. As humanoid production scales to millions of units per year, we hope to be the camera that scales with it.\n\nIn urban drone environments, GPS fails. Reliable RGB-D depth enables SLAM, safe navigation, and collision avoidance; critical for companies like Skydio operating in urban valleys. Again, drones are another vertical that requires thousands of cameras per year that are cheap and effective.\\\n\\\n**Additional Background**\n\nAny two-dimensional image is consistent with an infinite number of possible 3D worlds. “Seeing” is not just recording sensory input but resolving ambiguity; selecting the _most probable_ world given the available evidence **\\[Born & Bencomo, 2021\\]**.\n\nHumans and nearly every other mammal have evolved the same solution to this problem over the past 500 million years of evolution: stereo triangulation + data-driven inference. We integrate every available depth cue – shadows, perspective, occlusions, and much more – along with what we know about objects and the environment to accurately represent 3D scenes and act accordingly.\n\nRobots do not do this, but they need to.\\\n\\\n**Team**\n\n![uploaded image](/media/?type=post&id=95175&key=user_uploads/1945409/2474fb24-fae7-4604-a549-2a875fad6452)\n\nI spent the past four years at Princeton (on-leave from PhD) studying foundational topics in machine learning and neuroscience. I funded my research by writing two successful NSF grants to build biologically-inspired vision models based on the **fruit fly connectome** where the general research direction for myself and certainly my collaborators was the question of **“how do we make this fruit fly live inside a computer simulation?”**\n\nPrior to Princeton, I spent two years at Harvard Medical School studying visual decision-making in primate models and publishing influential theoretical work on **dopamine’s role in schizophrenia and its effects on visual perception**. I did my undergraduate degree in Biochemistry and got to do some cool non-vision work at the NASA Jet Propulsion Laboratory, figuring out a **new way of converting CO2 into O2 on Mars** that recycled water and did not require heat (e.g. MOXIE).\n\nGenerally, I like to work on really hard problems.\n\n**The Ask**\n\nIf you’re a roboticist, I’d love to learn what vision requirements matter most to you. If you know teams in autonomous vehicles, drones, or humanoid robotics, and believe this product is cool, sharing / intros would mean a lot.\n\nWe are actively taking pre-orders with delivery in March: https://efference.ai/preorder\n\nThank you — Gianluca.\n\n**\\[Born & Bencomo, 2021\\] [https://karger.com/bbe/article-abstract/95/5/272/47302/Illusions-Delusions-and-Your-Backwards-Bayesian](https://karger.com/bbe/article-abstract/95/5/272/47302/Illusions-Delusions-and-Your-Backwards-Bayesian)**\n\n**\\[Zhao et. al., 2023\\] [https://arxiv.org/abs/2304.13705](https://arxiv.org/abs/2304.13705)**", "url": "https://www.ycombinator.com/launches/Ol5-efference-helping-robots-see-like-humans-do", "votes": 122, "created_at": "2025-11-04T22:53:53.320Z" } ] }, { "id": 30845, "name": "Structured AI", "slug": "structured-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7b994637fa890bb7d41d69d022b3d15f6e4f3e8c.png", "website": "https://www.getstructured.ai/", "all_locations": "New York City, NY, USA", "long_description": "Structured AI is the most advanced AI-powered QA/QC platform for drawing packages, working across both PDF drawings and native Revit models. Architects and engineers use our platform to make every drawing set leave the office with certainty. \r\n\r\nOur AI agents learn your firm's standards, apply the relevant codes, and read every page of a set, then return a clear list of what's wrong, where it sits, and how to fix it. Run compliance reviews across thousands of pages in minutes, chat with your drawings for sourced answers, build custom checks in plain English, compare versions, overlay disciplines, and close out comments - all within a single platform, with a full audit trail on every finding.\r\n\r\nFor decades, the last line of defense on a drawing set has been a red pen at the end of a deadline week. But buildings have outgrown the red pen. A modern set runs thousands of drawings, and the errors hiding between them threaten quality and safety, not just cost. What manual review misses resurfaces as RFIs, change orders, and field rework, paid for in time, margin, and reputation.\r\n\r\nStructured AI catches those errors before they leave the office, capturing the judgment of your best reviewer once and applying it everywhere.\r\n\r\nBetter drawings build a better world. #GetStructured", "one_liner": "The AI drawing review platform for architects and engineers.", "team_size": 11, "industry": "Real Estate and Construction", "subindustry": "Real Estate and Construction -> Construction", "launched_at": 1759212406, "tags": [ "Computer Vision", "Construction", "Architecture", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Fall 2025", "status": "Active", "industries": [ "Real Estate and Construction", "Construction" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/structured-ai", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/structured-ai.json", "year_founded": 2025, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/getstructured-ai/", "x_url": "https://x.com/getstructuredai", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/structured-ai", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7b994637fa890bb7d41d69d022b3d15f6e4f3e8c.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Tom Blomfield", "url": "https://www.ycombinator.com/people/tom-blomfield" }, "founders": [ { "user_id": 1527607, "full_name": "Raymond Zhao", "title": "Founder/CEO", "founder_bio": "Cofounder & CEO @ Structured AI, the AI workforce for construction engineering, \nMaths & Stats @ Oxford, ex quant @ Goldman Sachs. \n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/raymondz1/", "x_url": "https://x.com/stingrayzz22", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1cbbee4d694fafca111cf86dc84d19d825ff0d50.jpg" }, { "user_id": 1980623, "full_name": "Issy Greenslade", "title": "Founder/COO", "founder_bio": "Cofounder & COO @ Structured AI, the AI workforce for construction engineering. Top of year Economics & Management @ Oxford University, previously IB at Greenhill & VC at Future Planet Capital.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/isabelgreenslade/", "x_url": "https://x.com/Green181024", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e1d9ab429d7fe98ac409eb160b5330e6c4be1745.jpg" }, { "user_id": 2338111, "full_name": "Brandon Abreu Smith", "title": "Founder/CTO", "founder_bio": "Cofounder & CTO @ Structured AI, making the AI workforce for construction design engineering.\nPhysics @ Oxford University | First authored NeurIPS paper, 30 citations.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/brandon-abreu-smith/", "x_url": "https://x.com/brandonstarxel", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7aa033f9847f2ead9af2c7e8315fa9408138279e.jpg" } ], "jobs": [ { "id": 85823, "title": "Founding Engineer", "url": "https://www.ycombinator.com/companies/structured-ai/jobs/Cxvgr61-founding-engineer", "location": "New York, NY, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$120K - $250K", "equity_range": "0.50% - 2.00%", "experience": "Any (new grads ok)", "visa": "Will sponsor", "skills": [ "Python", "TypeScript", "Computer Vision" ] }, { "id": 98428, "title": "Founding Account Executive ", "url": "https://www.ycombinator.com/companies/structured-ai/jobs/DaRbXNw-founding-account-executive", "location": "New York, NY, US", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$130K - $220K", "equity_range": "0.30% - 1.50%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 115779, "title": "Founding Infrastructure Engineer", "url": "https://www.ycombinator.com/companies/structured-ai/jobs/vZdpLe1-founding-infrastructure-engineer", "location": "New York, NY", "type": "Full-time", "role": "Engineering", "role_type": "Devops", "salary_range": "$180K - $250K", "equity_range": "0.50% - 1.00%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Microsoft Azure", "Kubernetes", "Python", "Docker", "Terraform" ] } ], "news": [ { "title": "Construction Quality Startup Structured AI Raises $4.2M Seed Round", "url": "https://www.enr.com/articles/63139-construction-quality-startup-structured-ai-raises-42m-seed-round", "date": "Jun 09, 2026" }, { "title": "Structured AI Founders named Forbes 30 Under 30", "url": "https://www.forbes.com/profile/structured-ai/?list=30under30-europe-ai/", "date": "Apr 13, 2026" }, { "title": "I'm Glad I Gave up My Goldman Sachs Dream: Y Combinator AI Startup CEO - Business Insider", "url": "https://www.businessinsider.com/accepted-y-combinator-ai-startup-batch-dream-working-goldman-sachs-2025-10", "date": "Oct 23, 2025" }, { "title": "WSJ: Europe’s AI Startups Look Stateside for Bigger Checks, Quicker Deals", "url": "https://www.wsj.com/tech/ai/europes-ai-startups-look-stateside-for-bigger-checks-quicker-deals-1a1dff43?", "date": "Oct 04, 2025" } ], "launches": [ { "id": 94607, "title": "Structured AI📐: Agentic workforce for construction design engineering", "tagline": "Our AI workforce generates design documents and checks them for errors, right inside your existing software.", "body": "### **TL;DR**\n\nStructured AI is building the **AI workforce for construction design engineering**, starting with AI agents that perform quality control on technical documents and drawings. Our agents learn your standards, apply the right building codes, and **automatically review your drawings**, so you see fewer clashes, fewer inconsistencies, and fewer costly RFIs and change orders.\n\n* ⚙️ **Automates QA/QC** across mechanical, electrical, structural and more\n* 🧩 Used by **multi-disciplinary design firms** to deliver projects faster with less risk\n* 🚀 **Mission:** Free engineers to focus on creative design, not repetitive admin\n\nLaunch Video: https://youtu.be/CRFsqOTynng\n\n### **❌ The Problem**\n\nThe construction industry's reliance on manual \"spot-checking\" for design errors costs **$177 billion** annually to fix mistakes that are discovered too late.\n\nSenior Engineers spend up to **50%** of their time cross-referencing 100-page drawing sets against 2,000-page building regulations by hand. This process is slow, tedious, and unreliable.\n\nErrors that are missed become exponentially more expensive to correct once construction is underway, causing massive project delays and budget overruns.\n\n---\n\n### **⚙️ The Solution**\n\nStructured AI is the **AI Quality Assurance platform.**\n\n1. **Proprietary Vision & Agents:** We trained a foundational vision model on **thousands** of engineering symbols to read plans like an expert.\n2. **100x Faster, 150% More Errors:** Our AI checks 100-page blueprints against 2,000 pages of building codes **in minutes.**\n3. **Cross-trade Checks:** Learns firm standards to detect inconsistencies across documents, drawings and Revit models, reducing cross-trade clashes before they occur.\n\n ![uploaded image](/media/?type=post&id=94607&key=user_uploads/1527607/252c0ccd-bd5b-4118-b05f-1f9767d981a6)\n\n\n---\n\n### **👷‍♀️ The Team**\n\nThe team is made up of **Oxford University–trained AI researchers** with deep experience building software tools for AEC and enterprise automation, having developed and scaled in-house AI tools at **Syska Hennessy Group**, a top US MEP (mechanical, electrical, plumbing) engineering firm. \n\n* **Brandon (CTO):** Authored papers for **NeurIPS** and **ChromaDB** (reviewed by Stanford's AI Lab) and created the first open-source **RAG chunking evaluation** framework.\n* **Raymond (CEO):** Built automation at **Goldman Sachs** and the **London Stock Exchange**; implemented an AI system at a £1B VC that increased deal flow 20×.\n* **Issy (COO):** First undergrad Director at Oxford's VC Fund, led investments later backed by a16z and Tim Draper; previously investment banking at **Greenhill.**\n\n### **🏗️ Why Construction Engineering?**\n\nBrandon started coding and making games when he was 12. At 17, a YouTube video he made demonstrating a fighter jet trained with deep reinforcement learning to dodge missiles went viral. This led to him being scouted by a US construction contractor to apply that same algorithm to MEP pipe routing optimization. He then spent the next six years, throughout high school and university, contracting and building AI tools for the construction and engineering industry. He later led innovation and scaled AI tools in-house at a top US engineering firm, ultimately spinning out with his team to form Structured AI.\n\nRaymond initially pursued architecture and engineering, but was talked out of the profession during an internship. Junior architects warned him about the extensive manual \"grunt work\" that defined their roles, a prospect that deterred him so much he switched his studies to Maths & Stats at Oxford. He now views his work freeing engineers from these very tasks as a full-circle moment, allowing them to focus on their passions.\n\n![uploaded image](/media/?type=post&id=94607&key=user_uploads/1527607/1e2f9415-f8f3-419c-92d8-0e007f9919b9)\n\n---\n\n### **🙏 Our Ask**\n\nIf you lead **mechanical, electrical, civil, or structural design teams** and want to automate documentation or QA workflows, we’d love to show you Structured AI.\n\n👉 Book a call: https://www.getstructured.ai/ \n\n📩 Contact: [info@getstructured.ai](mailto:info@getstructured.ai) ", "url": "https://www.ycombinator.com/launches/Obv-structured-ai-agentic-workforce-for-construction-design-engineering", "votes": 212, "created_at": "2025-10-14T19:17:28.011Z" } ] }, { "id": 30895, "name": "Allus AI", "slug": "allus-ai", "former_names": [ "Allus AI Inc." ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e556d72ba8162e6d77731f52ccea802c98b54fa3.png", "website": "https://allus.ai", "all_locations": "Atlanta, GA, USA", "long_description": "Allus builds next-gen vision foundation models that bring real intelligence to manufacturing. Enabling factories to see, understand, and improve production in real time. ", "one_liner": "Transforming manufacturing with our next-gen Vision Foundation Model", "team_size": 6, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1761108820, "tags": [ "Machine Learning", "SaaS", "Computer Vision", "Manufacturing", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Fall 2025", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/allus-ai", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/allus-ai.json", "year_founded": 2025, "city": "Atlanta", "country": "US", "linkedin_url": "https://www.linkedin.com/company/allus-ai/", "x_url": "https://x.com/allus_ai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e556d72ba8162e6d77731f52ccea802c98b54fa3.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/19ed8bfd28856edefbda0d3ee4d4d3017054ed8a.png", "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/02b1652b4ead3ef7bd979f7db3e7461b111e93bf.png" ], "primary_partner": { "name": "Pete Koomen", "url": "https://www.ycombinator.com/people/pete-koomen" }, "founders": [ { "user_id": 1667481, "full_name": "Kai Cui", "title": "Founder/CEO", "founder_bio": "Founder & CEO @ Allus AI. BS + MS in CS from Georgia Tech. Building vision foundation model to transform manufacturing.", "is_active": true, "linkedin_url": "https://linkedin.com/in/kaixiang-cui", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d012cfddf81fb9af30743cc5270cb84579450398.jpg" }, { "user_id": 2667730, "full_name": "Zhisen An", "title": "Founder/COO", "founder_bio": "Co-Founder @ Allus AI Inc. | MS CS @ GaTech", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/zhisenan/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d36b723641b04f1a88fef304237446298bdc8919.jpg" }, { "user_id": 2673772, "full_name": "Shijie Wang", "title": "Founder/CTO", "founder_bio": "Co-Founder & CTO @ Allus AI | CS @ Georgia Tech", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/shijiew/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/26e530b95b0469b4c70162d6c284bcf3d4df99b7.jpg" } ], "jobs": [ { "id": 92048, "title": "Founding AI Agent Engineer", "url": "https://www.ycombinator.com/companies/allus-ai/jobs/AcYMVC9-founding-ai-agent-engineer", "location": "Cupertino, CA, US / Atlanta, GA, US / Remote (CA, US; GA, US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$100K - $200K", "equity_range": "0.30% - 1.00%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Amazon Web Services (AWS)", "Microsoft Azure", "Node.js", "Python", "React", "Redis", "Rust", "TypeScript", "SQL", "Docker", "Software Architecture" ] } ], "news": [], "launches": [ { "id": 95347, "title": "Allus AI — Vision Foundation Model for Manufacturing", "tagline": "Bringing real intelligence to factories", "body": "Hi YC,\n\nThis is [Kai](https://bookface.ycombinator.com/user/1667481), [Shijie](https://bookface.ycombinator.com/user/2673772), and [Jason](https://bookface.ycombinator.com/user/2667730), cofounders of Allus AI.\n\nWe’re building a vision foundation model for manufacturing.\n\n**TL;DR**: Traditional manufacturing vision systems are slow, complex, and expensive. Allus delivers a million-dollar vision solution in minutes.\n\nhttps://www.youtube.com/watch?v=pNV7Ekyzfm0\n\n**A Vision Foundation Model for Manufacturing**\n\nAllus is a 1B parameter vision foundation model trained on over 1.5 billion industrial, robotics, and manufacturing data pairs collected over five years. It achieves state-of-the-art accuracy in defect detection (> 99.95%) and process monitoring (> 99.2%).\n\nAllus understands manufacturing the same way ChatGPT understands language. The dataset we used to train Allus is more than 100x larger than comparable industrial datasets, giving Allus a strong baseline familiarity with the entire factory ecosystem, including machines, people, and processes.\n\nToday 95% of manufacturers still rely on manual inspection to detect defects and monitor process compliance on the factory floor. This work can be automated with computer vision, but these solutions are domain-specific, take months to implement and cost millions of dollars. \n\nWith our implementation agent, Allus can be trained to handle specific use cases in only a few minutes using a small number of reference examples. \n\nIn the last three months, we’ve deployed it on production lines run by five global fortune 500 manufacturers, where we’ve been able to improve defect detection accuracy in some cases by more than 100x.\n\n**Our Team**\n\n![uploaded image](/media/?type=post&id=95347&key=user_uploads/1667481/c1a959c6-45ec-441b-b904-0ef212f17f53)\n\nWe combine cutting-edge AI with challenges that have troubled manufacturing for over a century. Kai, Shijie, and Jason began exploring how AI could empower industry while studying at Georgia Tech. Kai comes from a family deeply rooted in manufacturing and grew up in factories, managing production for IPhone back panel at the age of 18. Shijie previously worked as an LLM scientist at NVIDIA Lab, where he gained firsthand insight into the pain points of manufacturing. Jason has always operated on the front lines of industrial enterprises and developed universal solutions for robotics companies.\n\n**Ask**\n\nWe’re working with manufacturers across the globe, from electronics and automotive to food & beverage, FMCG, and high-performance materials.\n\nIf you want to bring intelligence to your factory, talk to us!\n\n👉 [Allus.ai](http://Allus.ai)\n\n📧 [kai@allus.ai](mailto:kai@allus.ai)\n\n![uploaded image](/media/?type=post&id=95347&key=user_uploads/1667481/802e4649-e04c-4d8f-beec-a357eef0f593)\n\n", "url": "https://www.ycombinator.com/launches/Onr-allus-ai-vision-foundation-model-for-manufacturing", "votes": 282, "created_at": "2025-11-10T07:02:52.620Z" } ] }, { "id": 31083, "name": "Kita", "slug": "kita", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0e2c404c5bbc49c9c8e25d9c60779b50f507ba65.png", "website": "https://www.kita.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "In emerging markets, open finance is still nascent. Most of the population is traditionally unbanked, banking APIs don’t exist, and a borrower’s financial history lives in documents: e-wallet records, bank statements, utility bills, and more. Because this data is unstructured, credit and risk teams are forced into manual review. This slows decisioning, increases costs, and caps lending volume. \r\n\r\nKita is the AI platform for global lending operations. We help lenders in emerging markets automate application completion, document verification, and underwriting from messy financial documents — using AI to extract fraud-checked data and localized risk signals that power faster, better credit decisions.\r\n\r\nUnder the hood, Kita is a learning engine. We link document-level signals to repayment outcomes, allowing our models to continuously improve fraud detection and risk assessment over time. This creates a compounding advantage for lenders as their distinct underwriting decisions feed back into the system.\r\n\r\nWe’re Carmel and Rhea. We met before Stanford and have been building together ever since. Carmel is from Manila, is a repeat founder, and spent three years in product at Apple. Rhea has a research background in computer vision and received the highest honor in Stanford Computer Science. Together, we combine deep local context with strong technical execution to build the infrastructure that expands access to credit in emerging markets.", "one_liner": "Underwrite borrowers around the world in minutes", "team_size": 3, "industry": "Fintech", "subindustry": "Fintech", "launched_at": 1767738733, "tags": [ "Fintech", "Computer Vision", "Lending", "Emerging Markets" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2026", "status": "Active", "industries": [ "Fintech" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": true, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/kita", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/kita.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/kitafi/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0e2c404c5bbc49c9c8e25d9c60779b50f507ba65.png", "app_video_url": null, "dday_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/video_event/81/1774380993-db0dbd44c2140f7bda4ffe83a4e00da4/e196deff5409ad3bcec6d1491f2ca310.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQC4NIECABPQ2AHNW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022732Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=8c33ac15b728e7fabaeb0ef7b95f2ed0abf0a9af632563f8172cbb9433860c19", "company_photos": [], "primary_partner": { "name": "Ankit Gupta", "url": "https://www.ycombinator.com/people/ankit-gupta" }, "founders": [ { "user_id": 2926213, "full_name": "Carmel Limcaoco", "title": "Founder/CEO", "founder_bio": "Carmel is the co-founder and CEO of Kita.\n\nShe is from Manila and studied Symbolic Systems & Music at Stanford. Prior to Kita, she was completing her Master’s in Computer Science. She spent three summers in audio and music product at Apple, where she was one of few interns to ever ship a feature in iOS.\n\nShe was awarded by the United Nations at 16, launched the first Product Fellowship in the Philippines, and co-founded DAHA in 2022, where she took Stanford’s first marketplace from 0 to 1.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/carmellimcaoco/", "x_url": "https://x.com/carmelkita", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3f01d97aa2ddb42ff43b3ff920557ed3e357c93a.jpg" }, { "user_id": 933442, "full_name": "Rhea Malhotra", "title": "Founder/CTO", "founder_bio": "Rhea is the co-founder and CTO of Kita.\n\nRhea completed her bachelor’s and master’s degrees in Computer Science at Stanford, and was an incoming PhD in Computer Vision and Robotics at Princeton. \n\nShe received the Firestone Medal, the sole highest honor of the Stanford CS Department, for her research in 2025.\n\nRhea joined her first research lab at age 13, worked on the COVID-19 vaccine at Pfizer at 17, and has since authored 7 papers recognized and awarded internationally.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/rhea-malhotra-198744142", "x_url": "https://x.com/_rheamalhotra_", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/41cd921951ca9a07459a11145a54b179b7e9353f.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 97013, "title": "Kita: Turn financial documents into risk signals for lenders", "tagline": "Make document-driven decisions 70x faster, with built-in fraud checks", "body": "Hi YC! We’re Carmel Limcaoco and Rhea Malhotra, the co-founders of Kita. We met over our gap year during COVID while we were undergrads at Stanford, and have been best friends ever since. Carmel is from Manila, is a past founder, and worked in computational music and audio at Apple. Rhea has a background in computer vision & robotics and was awarded the highest honor in Stanford Computer Science. We were both completing our Master’s in CS at Stanford when we chose to build Kita full time. \n\n**TLDR:** We turn messy borrower documents into the data layer that powers lending in emerging and undertapped domestic markets.\\\n\\\n[Kita (YC W26) Launch Video](https://www.youtube.com/watch?v=_z9lR5p9NMg)\n\n**The problem**\n\nIn emerging markets like the Philippines, where there are no banking APIs, a borrower’s financial history is locked in noisy documents: bank statements, payslips, utility bills, and more. For lenders, accessing and validating a borrower’s data is nearly impossible.\n\nToday, lenders either review these documents manually or rely on legacy OCR. Manual review is slow and expensive, while OCR breaks on real-world inputs and still requires human verification. This slows loan decisioning, raises costs, and caps lending volume.\n\n**What is Kita?**\n\nKita uses vision-language models to outperform traditional OCR, turning noisy borrower documents into fraud-checked, decision-ready signals for underwriting models. We are built specifically for emerging and undertapped markets, hyperlocalized around the signals that actually drive underwriting outcomes in places like the Philippines, Indonesia, Mexico, and beyond.\n\nOver time, Kita links historical repayment outcomes to document-level signals, becoming a custom learning engine trained on a lender’s own data and decisions.\n\nWe’re starting in Southeast Asia with plans to expand to other emerging markets. We’re also exploring underserved segments in more developed markets, like parts of the U.S. where borrowers still rely on documents.\n\n**Our ask**\n\n* **Intros:** We’d love introductions to lenders or fintechs operating in lending or credit, in emerging markets or in the U.S.\n* **Founder wisdom:** If you’ve built or scaled lending, underwriting, or risk infrastructure, we’d love to learn from you and compare notes.\n* **Operators in financial services:** If you work with document-heavy workflows, we’d love to understand the problems with turning documents into usable data.\n\n![uploaded image](/media/?type=post&id=97013&key=user_uploads/2926213/83392dd0-d24f-4c82-b04a-61c6649bcaeb)\n\n", "url": "https://www.ycombinator.com/launches/PEj-kita-turn-financial-documents-into-risk-signals-for-lenders", "votes": 23, "created_at": "2026-01-23T20:12:27.746Z" } ] }, { "id": 31258, "name": "Dream", "slug": "dream", "former_names": [ "Dream Browser" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/479d7e4f9e6b74b1df9f87c67fe614babba0a9b5.png", "website": "https://www.pingdream.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Dream makes AI cameras that catch damage teams miss. Car dealerships, equipment rental companies, fleets, and more move expensive assets in and out all day, and undocumented damage is a costly leak.\r\n\r\nDream cameras are simple to set up and work automatically. Each vehicle or machine that passes is photographed from every angle, identified, and reconstructed into a complete condition record. On return or intake, Dream instantly flags new dents, scrapes, and changes your team might miss, giving one source of truth for every asset's condition over time.\r\n\r\nRyland worked at X/xAI and built aerial damage-detection vision at Vexcel Imaging. Alexander worked at AWS and trained edge-camera models deployed in the wild across Africa and India to detect poachers.", "one_liner": "Using VLMs to Catch Damage", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1786646316, "tags": [ "Hardware", "Machine Learning", "Computer Vision", "B2B", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/dream", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/dream.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/pingdream", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/479d7e4f9e6b74b1df9f87c67fe614babba0a9b5.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 1393934, "full_name": "Ryland Birchmeier", "title": "Founder", "founder_bio": "Ryland Birchmeier is Co-founder of Dream (YC S26), building pocket-sized, battery-powered AI cameras that automatically capture assets and catch damage teams miss at car dealerships, equipment rental companies, and more.\n\nPreviously, Ryland worked at X/xAI and built aerial computer vision at Vexcel Imaging. He studied computer science at the University of Virginia.", "is_active": true, "linkedin_url": "https://linkedin.com/in/rylandbirchmeier", "x_url": "https://x.com/rylandbirch", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/37f594b7f6b8754e9966db95609cba5942fd3185.jpg" }, { "user_id": 1456894, "full_name": "Alexander Halpern", "title": "Founder", "founder_bio": "Alexander Halpern is Co-founder of Dream (YC S26), building pocket-sized, battery-powered AI cameras that automatically capture assets and catch damage teams miss at car dealerships, equipment rental companies, and more.\n\nPreviously, Alexander built agents for AWS Marketplace and trained models for edge computing cameras deployed across Africa and India to detect poachers hunting endangered wild cats at Panthera. He studied computer science at the University of Virginia.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alexanderhalpern", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4702d251513a653e5c3f7e4d1307df73e45916e3.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 110473, "title": "Dream - Pocket-sized AI cameras that catch asset damage", "tagline": "Dream makes pocket-sized, battery-powered AI cameras that catch damage teams miss.", "body": "Hey everyone 👋, we are Ryland and Alexander, co-founders of Dream.\n\n**TL;DR: Dream automatically photographs every vehicle or machine that passes through your gate, builds a 3D condition record, and uses AI to catch new damage the moment an asset passes through.**\n\n**Launch Video: [https://youtu.be/uMP12lCb12E](https://youtu.be/uMP12lCb12E)**\n\n**The problem**\n\nDamage is an expensive leak for anyone who moves large, expensive assets in and out all day. It shows up two different ways:\n\n**Car dealership service departments**\n\n* As a car pulls into the service entrance, damage that could be identified and fixed goes unnoticed, which is lost revenue.\n* Without a record of the car's condition on arrival, disputes come down to the customer's word against the dealership's, and the dealership gets blamed for pre-existing damage.\n\n**High-value asset rentals (heavy equipment, trucks, boats, planes, trailers)**\n\n* Teams are inconsistent about photographing assets as they go out and come back, so new damage gets missed before the customer is billed.\n* When damage is missed, there's no evidence to charge the customer, so the company eats the repair cost out of pocket.\n\n**What Dream does**\n\nDream cameras sit at your gate, lane, or service drive and turn every pass into evidence, automatically:\n\n* **Continuous capture:** every vehicle or machine that moves through is photographed from every angle. No one has to remember to do it.\n* **Asset identification:** Dream knows exactly what you need to look at.\n* **3D reconstruction:** every pass becomes a complete 3D condition record for that asset.\n* **AI damage detection:** on return or intake, Dream compares against the last record and flags new dents, scrapes, and visible changes right away.\n\nThe result is one clear source of truth for every asset's condition over time: rental companies catch damage before they bill so they stop eating repair costs, and dealership service departments find more fixable damage and resolve customer disputes with evidence.\n\nWorks across cars, vans, trucks, heavy equipment, and more.\n\n**Why us**\n\nWe have spent years training models for aerial imaging and edge computer vision devices. Ryland has worked on the exact problem of detecting damage to assets from aerial imaging, such as degraded paint. Alexander has trained models that run on edge cameras deployed in the wild in Africa and India to detect poachers.\n\n**Our ask**\n\nWe're onboarding partners now. **We'd love intros to:**\n\n* **Car dealerships**\n* **High-value asset rental companies** (heavy equipment, trucks, boats, planes, trailers)\n\nIf you run one of these, or know someone who does, please message us on LinkedIn!", "url": "https://www.ycombinator.com/launches/Sjp-dream-pocket-sized-ai-cameras-that-catch-asset-damage", "votes": 7, "created_at": "2026-08-14T17:00:00.348Z" } ] }, { "id": 31285, "name": "Brumby (Formerly GrazeMate)", "slug": "brumby", "former_names": [ "GrazeMate" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e7f64c3261f306e1859d503d40ad84601c127c82.png", "website": "https://brumby", "all_locations": "Sydney, NSW, Australia", "long_description": "Brumby builds autonomous drones that herd cattle.\r\n\r\nOn command, our drones fly to a paddock, position themselves around the mob, and move them where they need to go. What used to take a full day of helicopters, motorbikes, and horses now runs on a schedule.\r\n\r\nWe work with some of the largest cattle ranches in the world. While the drones are herding, they're also estimating animal weights, measuring grass biomass, monitoring water levels, and flagging sick animals. We're building physical AI that lets a grazier manage thousands of head across millions of acres from their phone.", "one_liner": "Robot Cowboys that Herd Cattle with AI Drones", "team_size": 3, "industry": "Industrials", "subindustry": "Industrials -> Agriculture", "launched_at": 1768433444, "tags": [ "Reinforcement Learning", "Drones", "Computer Vision", "Agriculture" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2026", "status": "Active", "industries": [ "Industrials", "Agriculture" ], "regions": [ "Australia", "Oceania", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/brumby", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/brumby.json", "year_founded": 2025, "city": "Sydney", "country": "AU", "linkedin_url": "https://www.linkedin.com/company/105884860", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e7f64c3261f306e1859d503d40ad84601c127c82.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Tyler Bosmeny", "url": "https://www.ycombinator.com/people/tyler-bosmeny" }, "founders": [ { "user_id": 2933487, "full_name": "Sam Rogers", "title": "Founder/CEO", "founder_bio": "CEO of Brumby.\n\nSpent half my life on a cattle farm, and the other half building robots.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/samson-rogers/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9019d14f0cd67dacb9f12edba5539dbeb3470e9f.jpg" } ], "jobs": [ { "id": 112676, "title": "Remote Pilot", "url": "https://www.ycombinator.com/companies/brumby/jobs/4L3nppN-remote-pilot", "location": "Sydney", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$85K - $150K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizenship/visa not required", "skills": [] }, { "id": 112677, "title": "Embedded Systems Engineer", "url": "https://www.ycombinator.com/companies/brumby/jobs/d4z6kbg-embedded-systems-engineer", "location": "Sydney", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$100K - $150K", "equity_range": "", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [ "Bash/Shell", "C++", "Microcontrollers", "PCB design" ] }, { "id": 112678, "title": "Operations Associate", "url": "https://www.ycombinator.com/companies/brumby/jobs/n8GBsUB-operations-associate", "location": "Sydney", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$100K - $130K", "equity_range": "", "experience": "1+ years", "visa": "US citizenship/visa not required", "skills": [] }, { "id": 112679, "title": "Perception Engineer", "url": "https://www.ycombinator.com/companies/brumby/jobs/A0m4omK-perception-engineer", "location": "Sydney", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$100K - $150K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "C++", "Python", "Machine Learning", "Deep Learning", "OpenCV", "Computer Vision" ] }, { "id": 112680, "title": "Software Engineer", "url": "https://www.ycombinator.com/companies/brumby/jobs/ar7aJ98-software-engineer", "location": "Sydney", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$100K - $160K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "Amazon Web Services (AWS)", "iOS", "PostgreSQL", "Distributed Systems", "Terraform", "FastAPI" ] } ], "news": [ { "title": "GrazeMate Raises $1.2 Million As 19-Year-Old CEO Automates Cattle Mustering", "url": "https://www.forbes.com.au/news/innovation/grazemate-raises-1-2-million-19-year-old-ceo-cattle-mustering/", "date": "Jan 13, 2026" } ], "launches": [ { "id": 97388, "title": "GrazeMate (Now Brumby): Drones that Herd Cattle", "tagline": "We build autonomous “robot cowboys” that help ranchers move and monitor cattle.", "body": "**TL;DR** - Ranchers can spend hundreds of thousands per year moving cattle between fields. GrazeMate builds autonomous drone software to guide cattle - doing the work of helicopters and motorbikes with AI that understands animal behaviour.\n\n\n\n**❌ The problem**\n\nI'm Sam. I grew up on my family's cattle station in Australia, where we move cattle on motorbikes, helicopters, and horseback. It took hours each day and >$100k/year.\n\nIn the US, ranches spend anywhere from $10k–$1M per year and hours each day moving cattle just to keep them on fresh grass. It's expensive, risky, and hard to staff.\n\n**✅ The solution**\n\nGrazeMate's autonomous drones herd your cattle for you. What could take teams a full day now takes three clicks from a phone.\n\nOur system:\n\n* Finds your herd and guides them calmly to the next pasture\n* Uses AI that reads animal behaviour and applies pressure like an experienced stockman\n* Learns how your cattle respond and improves with every move\n\nWhy ranchers are switching:\n\n* More frequent moves → Better grazing, pasture use, and stocking rates\n* More affordable than traditional methods\n* Hours of herding → seconds on a phone\n* Real-time weight estimates, pasture data, and infrastructure insights\n\n![uploaded image](/media/?type=post&id=97388&key=user_uploads/2933487/a7e07817-bfde-4784-90ab-e4f9d16579b8)\n\n**🤠 Why us**\n\nMy family runs 6,000 head. I dropped out of robotics at University of Sydney after seeing both the challenges with modern ranching, and the capacity robotics has to help. Our team combines PhDs and engineers who grew up on ranches.\n\n**🙏 Our ask**\n\nWe're onboarding early partners. We’d be extremely grateful for introductions to:\n\n* Large grazing operations (US and beyond)\n* Ranch managers exploring rotational grazing\n* Industry leaders, cattle agents, feedlots, processors\n\nAnd if that’s you, schedule a demo on grazemate.com, or contact me at [sam@grazemate.com](mailto:sam@grazemate.com). I’d love to chat!", "url": "https://www.ycombinator.com/launches/PKm-grazemate-now-brumby-drones-that-herd-cattle", "votes": 30, "created_at": "2026-02-02T15:42:29.970Z" } ] }, { "id": 31301, "name": "OpenVector", "slug": "openvector", "former_names": [ "TeleOps", "BotZero", "Foveal" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/54df715903ce304e2b9e3b2fe1f375caf7811ea6.png", "website": "https://openvector.com", "all_locations": "San Francisco, CA, USA", "long_description": "OpenVector transforms any existing camera into an automated 24/7 workforce without requiring new hardware. We've built the world's fastest vision-language model inference engine that allows users to simply describe a task in plain English and automatically build a custom AI vision model to monitor their feed. When it detects an important physical event—like a stalled production cycle, a safety violation, or a waiting customer—it operates a computer just like a human to take immediate action. It integrates with legacy systems, makes calls, sends emails, updates a POS/CRM, or triggers webhooks in real time. OpenVector enables any camera to do real work.", "one_liner": "AI that turns any camera to an autonomous worker with real-time vision", "team_size": 4, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1785369468, "tags": [ "Robotics", "Computer Vision", "Infrastructure", "Industrial" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/openvector", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/openvector.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://linked.com/in/openvectorlabs", "x_url": "https://x.com/openvectorlabs", "fb_url": "https://www.facebook.com/profile.php?id=61591381283372", "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/54df715903ce304e2b9e3b2fe1f375caf7811ea6.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jon Xu", "url": "https://www.ycombinator.com/people/jon-xu" }, "founders": [ { "user_id": 633157, "full_name": "Andrey Gizdov", "title": "Founder/CEO", "founder_bio": "Dropped out of PhD. Computer Vision @ Harvard.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/andreygizdov/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/994295941ffb048fecb964921a35db1494d051b3.jpg" }, { "user_id": 2384953, "full_name": "Vishal Urlam", "title": "Founder", "founder_bio": "CMU'23, ECE'20 | MIT GrandHack '25 | Kellogg Design Challenge '23", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/vishal-urlam/", "x_url": "https://x.com/vishalurlam", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/398802b070ebc9a50a9873e3441541de12ccedfb.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 108360, "title": "OpenVector - Self-improving AI that turns any camera into an autonomous worker.", "tagline": "Most cameras just record. We give them the ability to do real work.", "body": "**TLDR;** OpenVector turns existing cameras into an automated 24/7 workforce. Describe a task in plain English, and it auto-builds a custom vision model that takes real-time actions on a computer when it spots an event.\n\n**Ask;** If you or someone you know runs physical operations (manufacturing, logistics, retail, security) and wants to automate visual tasks without buying new hardware, we’d love to connect!\n\n**Our Launch Video:**\n\n\n\n**Our Team:**\n\nOpenVector was founded by a team of builders and engineers obsessed with bringing self-improving visual intelligence to the physical world.\n\n* [Andrey Gizdov](https://www.linkedin.com/in/andreygizdov/), CEO, dropped a fully-funded **PhD** scholarship from Fulbright, worth **$170,000**, with invitations at **Harvard and MIT**, where he worked on real-time vision models.\n* [Vishal Urlam](https://www.linkedin.com/in/vishal-urlam/), CTO, previously automated camera systems and network protocols for Tata, the **second-largest network infrastructure in the world**. CMU embedded systems engineer.\n\n**Problem**:\n\nMany important business moments happen in the physical world: Someone tailgates. A production cycle stalls. A worker skips a step in the manufacturing process. A customer arrives, and no one responds. There are over 1 billion cameras worldwide, but they only record it for later. For physical operations, the best technology available to catch these events in real-time has been a human staring at a monitor.\n\n**Solution:**\n\nOpenVector is an AI that brings action to any camera.\n\n* **Plain English to AI Model:** No coding or expensive setup. Simply describe your task in plain English, and OpenVector automatically builds the best AI vision model for it, and watches your camera feeds 24/7. It’s an app that takes 2 minutes to download and set up, and works with any camera.\n* **Acts Like a Human:** OpenVector operates a computer just like a human based on what it sees.\n* **Legacy Integration:** It automatically integrates with your existing systems to make a call, send an email, update your POS or CRM, or trigger a webhook the second an event happens.\n* **Zero New Hardware:** Works entirely with the cameras you already have installed (custom end-to-end solutions also available).\n\n**We’ve Already Started:**\n\nOpenVector is already live and seeing use cases across security, retail, manufacturing, and logistics. We are tracking production cycle efficiency directly from existing CCTV, ensuring good-manufacturing-practices, and catching physical design errors.\n\n**Now Onboarding:**\n\nOpenVector is ready to deploy on your camera feeds today. Contact us if you’re tired of cameras that only watch and want them to actually do work for you. [founders@openvector.com](mailto:founders@openvector.com)\n\n![uploaded image](/media/?type=post&id=108360&key=user_uploads/633157/46faabca-90e0-4c69-a364-138588d733d5)\n\n", "url": "https://www.ycombinator.com/launches/SBk-openvector-self-improving-ai-that-turns-any-camera-into-an-autonomous-worker", "votes": 16, "created_at": "2026-07-31T18:00:43.590Z" } ] }, { "id": 32206, "name": "DeepReach Inc.", "slug": "deepreach-inc", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/165caaebecbd3f0b9470bb5817a0a1817520c935.png", "website": "http://www.deepreach.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Physical AI — robots and world models — is bottlenecked on one thing: diverse,\r\nreal-world data at massive scale showing how humans actually do work in the\r\nphysical world. It was never written down, and you can't buy it, because no one\r\nhas ever captured it. Only real local experts, the people who do this work every\r\nday, can produce data this authentic and valuable.\r\n\r\nDeepReach builds the network that reaches them. We design and manufacture\r\nwearable stereo capture devices, then place them with local data partners who\r\nrun collection as their own business — in warehouses, workshops, farms, kitchens\r\nand repair shops. They own the local relationships; we supply the hardware, the\r\nsoftware platform, the quality pipeline and the payments. Anyone can run a data\r\nbusiness.\r\n\r\nThat structure is what produces diversity. Instead of thousands of hours inside\r\none environment, we deliver hours spread across thousands of environments —\r\nwhich is what actually makes robot policies generalize.\r\n\r\n475 devices in the field and 100+ data partners are live across 7 countries, adding new\r\npartners every week. They've collected nearly 150,000 clips in the past three\r\nmonths, with volume roughly doubling month over month. Our data is in production\r\nwith multiple frontier model and robotics companies, and we're scaling to\r\n10,000+ devices across 50 countries.", "one_liner": "Network for Physical AI Data Business", "team_size": 6, "industry": "B2B", "subindustry": "B2B", "launched_at": 1786563914, "tags": [ "Hardware", "Machine Learning", "Robotics", "Computer Vision", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)", "answer": "Chris and I are from the same hometown and have known each other for ten years, long before either of us thought about robots.\r\n\r\nIn that time I built two companies around the same problem: how do you organize people you'll never meet, in places you'll never visit, to do consistent work? The first was HireIO, a cross-border staffing platform out of Silicon Valley. The second was Talex, an expert network for LLM data annotation — around 70,000 people across ten countries. Chris went the other direction: a CS PhD at USC in computer vision, ML infrastructure at Meta, then founding ML engineer at Aven.\r\n\r\nRunning an annotation network for language models, I kept hitting the same wall from the supply side. Text was running out, and the next generation of models wasn't going to learn from the internet. It was going to learn from the physical world — and nobody had captured it. What a warehouse picker, a line cook or a phone repair technician knows is real expertise, and none of it was ever written down. You can't buy that data, because it doesn't exist yet. Chris was hitting the same wall from the model side, watching what was missing from training sets.\r\n\r\nThe default answer at the time was to put a lab in one city and record thousands of hours in the same room. That produces volume, not diversity, and robot policies don't generalize from volume. What the models needed was the opposite shape: a few hours each from thousands of different real workplaces, all over the world. Which meant the bottleneck wasn't an AI problem. It was a distributed human network problem — reaching thousands of ordinary businesses across dozens of countries, and giving the people already working in them a reason to capture what they do. That was the problem I'd spent two companies learning to solve.\r\n\r\nWe started DeepReach in 2025. Chris owns perception and the data engine; I own the network, hardware operations and the commercial side. Ten years of knowing each other means we skipped most of the negotiation that takes co-founders their first year." }, { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Core problem: robot models can't generalize, because the data they learn from all looks the same.\r\n\r\nPhysical AI needs to see how work actually happens — a mechanic torquing a bolt, a baker shaping dough, a technician opening a phone. None of it was ever written down. The internet captured what people say about the world; it never captured how people act in it. So unlike language models, this generation can't be trained on scraped text. Every hour has to be collected on purpose. \r\n\r\nToday the field has roughly tens of thousands of hours of this data against a need estimated in the hundreds of millions. But the gap isn't really a volume gap. It's a diversity gap. The standard approach is to build a lab, hire operators, and record for months in one controlled space. That yields thousands of hours that all look alike, and a policy trained on it fails the moment the lighting, the counter height, or the tool changes. Our customers' acceptance specs now reflect this directly: they cap how much of a delivery can come from any single environment type, and reject batches that are too concentrated. Diversity is the binding constraint, and it's now contractual.\r\n\r\nDiversity is hard for an economic reason, not a technical one. Collecting one hour each from a thousand real workplaces means negotiating access to a thousand real businesses — each with an owner who has no reason to let a stranger film their operation, in a country where you have no presence. That cost is why everyone else builds the lab instead. It's a distribution problem wearing an AI problem's clothes. \\\r\n\\\r\nThat's why we built the company as a network rather than an operation. The people who already work in those places, and already have the trust and the access, run collection as their own local business. We give them the hardware, the software and the payments. Access stops being something we buy and becomes something the network already has. \\\r\n\\\r\nI decided to work on this from the supply side. I'd spent two companies building cross-border human networks — a staffing platform, then a 70,000-person annotation network across ten countries — and kept watching demand arrive for data that no existing structure could produce. The bottleneck was never the model. It was that nobody had built the network that reaches the physical world." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "The long-term vision is a network that can reach any workplace on earth, and a record of how human work is actually done.\r\n\r\nConcretely: 10,000+ devices, 50 countries, one million commercial scenes, ten million task types. Every deployment adds to what we call the Human Skill Library — a permanent record of how people do physical work, built from the inside by the people who do it. Much of this knowledge exists nowhere else. It lives in the hands of a mechanic who has been doing the job for thirty years, and when he retires it's gone. Text and video on the internet captured what people say about work. Nobody has captured the work. \r\n\r\n\\\r\nIf we succeed, two things are different：\\\r\nFirst, robots stop being confined to the places that can afford to build around them. Today automation only pays at Amazon scale — a facility large enough to redesign itself for the machine. That's a rounding error of the world's actual workplaces. A policy trained on a million real environments doesn't need the environment to change; it works in a repair shop with bad lighting and a counter at the wrong height. That's the difference between robotics as a capability the largest companies buy and robotics as infrastructure a small business can use.\\\r\nSecond, the people whose work teaches these models are inside the economy that results. The last generation of AI was trained on data scraped from people who were never asked and never paid. This one has to be collected on purpose, from real workers in real workplaces, which means it can be built the other way — as something people opt into and earn from. We already have data partners in ten countries running this as a business they own. At 10,000 operators that isn't a side effect of how we source data. It's the point." } ], "url": "https://www.ycombinator.com/companies/deepreach-inc", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/deepreach-inc.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/in/timothyli-deepreach/", "x_url": "https://x.com/DeepReach_AI?s=20", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/deepreach-ai", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/165caaebecbd3f0b9470bb5817a0a1817520c935.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/482534d82410b1adf1774b961acd50c6704093e0.png" ], "primary_partner": { "name": "Brad Flora", "url": "https://www.ycombinator.com/people/brad-flora" }, "founders": [ { "user_id": 3114257, "full_name": "Chris Liu", "title": "Founder/CTO", "founder_bio": "Co-founder at DeepReach Inc. Founding MLE & tech lead of Aven scaling to #1 in DM volume in the US. Research Scientist at Meta working on core ML systems. PhD in computer vision from USC. B.S. from Peking University.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/drxcliu", "x_url": "https://x.com/drxcliu", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1f9e9212493e67c1bb9a3a913e2ab4c6fa9b897e.jpg" } ], "jobs": [ { "id": 112083, "title": "VP / Head of Global Data Partner Network & Operations", "url": "https://www.ycombinator.com/companies/deepreach-inc/jobs/r0itCEs-vp-head-of-global-data-partner-network-operations", "location": "San Jose, CA, US", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$150K - $200K", "equity_range": "0.50% - 1.00%", "experience": "3+ years", "visa": "Will sponsor", "skills": [] } ], "news": [], "launches": [ { "id": 110389, "title": "DeepReach: Launch a real-world data business today", "tagline": "Multi-million-dollar contracts secured with frontier AI labs — and growing", "body": "**TL;DR:** DeepReach is a global data network that equips local entrepreneurs with wearable devices to capture the real-world operation data for Physical AI training. By decentralizing collections across thousands of entrepreneurs who have access to local businesses, they provide high-diversity training data that cannot be accessed elsewhere. DeepReach already signed multiple-million-dollar contracts with frontier AI labs.\\\n\\\n**DeepReach is building a global network for real-world human data to train Physical AI.**\n\nWe enable local entrepreneurs to operate data-collection businesses using our wearable capture devices, while we provide the hardware, software, QA, and customers.\n\nThe result is a distributed network that produces the diverse human demonstrations Physical AI labs cannot obtain anywhere else. Every new data partner expands the diversity and value of the network.\n\nIn just three months, DeepReach has helped **150+ entrepreneurs** build data businesses employing **1,000+ local experts** across **7 countries**. Together, they’ve collected **500,000+ real-world operation clips**, captured **150,000+ professional skill demonstrations**, and secured **several million dollars in customer contracts** with frontier AI labs and robotics companies.\n\n▶ **Watch how it works:** \n\n---\n\n# **The Problem**\n\nFrontier AI is already learning from the physical world.\n\nThe challenge is no longer collecting data, it’s collecting **diverse** real-world data at scale.\n\nMost companies solve this by building collection labs or hiring workers directly. Both approaches scale headcount, but neither scales access. The hardest part is reaching millions of real workplaces across different industries, countries, and working conditions.\n\nWithout that diversity, AI struggles to generalize beyond a small number of controlled environments.\n\n---\n\n# **What We Built**\n\nInstead of building another data collection company, **we built a platform where entrepreneurs launch independent real-world data businesses.**\n\nDeepReach provides the wearable devices, software platform, quality infrastructure, customer demand, operational support, and payments. **Entrepreneurs build and grow the businesses.**\n\nThey recruit local experts, develop trusted relationships with local businesses, and continuously expand into new industries, communities, and real-world environments.\n\n**Every new business expands the platform’s reach into the physical world, creating access to places, people, and skills that centralized data collection simply can’t reach.**\n\nThe result isn’t a larger workforce, it’s **a growing ecosystem of independent data businesses** that continuously expands the world’s supply of diverse real-world data for Physical AI.\n\n---\n\n# **Where We Are**\n\nIn just three months:\n\n* **150+ entrepreneurs** have launched data businesses on DeepReach\n* Those businesses now employ **1,000+ local experts** across **7 countries**\n* **500,000+ real-world operation clips** collected\n* **150,000+ professional skill demonstrations** captured\n* **Several million dollars in signed customer contracts** with frontier AI labs and robotics companies\n* Scaling toward **10,000+ devices** across **20 countries**\n\n# **Where This Goes**\n\nOur model is simple: labs list the tasks and environments they need, our entrepreneurs pick up the orders and go record them, and every delivery makes the network more capable of fulfilling the next one. It also compounds into something bigger, what we call the **Human Skill Library**: a growing record of how people interact with the physical world.\n\nMost of this knowledge has never been documented. It lives in the hands of experienced workers, passed down through practice rather than words, and it often disappears when they retire.\n\nOur vision is to preserve and scale humanity's physical knowledge, so every AI system can learn from the collective experience of human work.\n\n# **About Us**\n\n\\\nTim built HireIO into a leading global workforce company with 15 millions in annual revenue, then founded [Talex.ai](http://Talex.ai), an AI-powered expert network, before starting DeepReach. Chris comes from a computer vision background. We discovered the problem firsthand while labeling data for robotics startups: the real bottleneck wasn’t annotation, it was capturing diverse real-world human skills at scale.\n\n![uploaded image](/media/?type=post&id=110389&key=user_uploads/1294315/9bb8c293-ab57-42aa-b756-58562e546b78)\n\n---\n\n# **How You Can Help**\n\nIf you’re building frontier AI systems and need more diverse real-world data, we’d love to talk.\n\nIf you’re an entrepreneur interested in building a real-world data business, applications are open here: [www.deepreach.ai/join](https://www.deepreach.ai/join)\n\nTim Li\\\nFounder & CEO, DeepReach AI\n\n[**tim.li@deepreach.ai**](mailto:tim.li@deepreach.ai)", "url": "https://www.ycombinator.com/launches/SiT-deepreach-launch-a-real-world-data-business-today", "votes": 470, "created_at": "2026-08-13T18:33:15.556Z" } ] }, { "id": 33013, "name": "Operon", "slug": "operon", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/aaeb7f2ff7aa5b0b7e64888230be2a7e46342f66.png", "website": "https://operonsolutions.com/en", "all_locations": "San Francisco, CA, USA", "long_description": "Operon is the data intelligence layer for heavy industry. We ingest the messy documents a plant runs on (P&IDs, schematics, datasheets, scans) and turn them into one structured, queryable model of the facility.\r\n\r\nOn top of it, engineers query their plant in natural language and build agentic workflows for safety, compliance, and design. \r\n\r\n", "one_liner": "Agentic data layer for manufacturing & process industries. ", "team_size": 0, "industry": "Industrials", "subindustry": "Industrials", "launched_at": 1782593141, "tags": [ "Computer Vision", "Manufacturing", "Infrastructure" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "Industrials" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/operon", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/operon.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/operonsolutions/?viewAsMember=true", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/aaeb7f2ff7aa5b0b7e64888230be2a7e46342f66.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jon Xu", "url": "https://www.ycombinator.com/people/jon-xu" }, "founders": [ { "user_id": 2155140, "full_name": "Anderson Chen", "title": "Founder/CEO", "founder_bio": "Founde/CEO @ Operon Solutions.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/anderson-chen-2b6941216/", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/289ff08d5218c7b14ea8d2d173dfb9330c17e927.jpg" } ], "jobs": [ { "id": 96232, "title": "Founding FDE", "url": "https://www.ycombinator.com/companies/operon/jobs/w7qf3HJ-founding-fde", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$100K - $150K", "equity_range": "0.20% - 1.00%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [] }, { "id": 33313, "name": "SubVysion", "slug": "subvysion", "former_names": [ "SubVysion, Inc." ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0df819d609df4c8e15ebdc21bbc1f518d56e52d8.png", "website": "https://subvysion.com", "all_locations": "San Francisco, CA, USA", "long_description": "We build centimeter-accurate 3D maps of underground utilities to help construction contractors dig safer and faster. With underground intelligence, contractors can avoid dangerous utility strikes and costly project delays.", "one_liner": "Autonomous rovers to make the ‘Google Maps’ of underground utilities", "team_size": 2, "industry": "Real Estate and Construction", "subindustry": "Real Estate and Construction -> Construction", "launched_at": 1783960374, "tags": [ "Hard Tech", "Computer Vision", "Real Estate", "Construction", "Infrastructure" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "Real Estate and Construction", "Construction" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/subvysion", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/subvysion.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/subvysion/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0df819d609df4c8e15ebdc21bbc1f518d56e52d8.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Gustaf Alstromer", "url": "https://www.ycombinator.com/people/gustaf-alstromer" }, "founders": [ { "user_id": 1363066, "full_name": "Maxwell Stone", "title": "Founder/CEO", "founder_bio": "Founder at SubVysion. Previously Machine Learning Engineer at Roboflow, where he built on-device inference SDKs deployed in 10,000+ pharmacies for a Fortune 100 healthcare company and developed ML solutions for enterprise customers including railyard mapping and defect detection. MSE and BSE candidate in Computer Science from UPenn.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/maxwell-stone-2b5b6b1a0?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/534fdc481c3e4f0ac1b1ba8eb1dbfcf6b3c19b39.jpg" }, { "user_id": 3411443, "full_name": "Wenson Tang", "title": "Founder/COO", "founder_bio": "Co-founder and COO at SubVysion, Inc. Computer Science and Consumer Psychology from UPenn. Previously Software Developer at CentralSquare Technologies, working cross-jurisdiction emergency dispatch allowing first responders to reach emergencies three minutes faster.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/wenson-tang", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6138c520a0b71cd0f533baf3b0cd765d9ffc1ddd.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 111406, "title": "SubVysion: Preventing $150B is wasted construction spending", "tagline": "Autonomous rovers building the 'Google Maps' of subsurface infrastructure", "body": "\n\nConstruction has a mapping problem. Of the $2.2T in annual US construction spending, $150B is wasted due to a lack of subsurface knowledge. All construction starts from the ground up, and more often than not, unexpected assets underneath the surface will hold a construction project back. Whether it's an unknown utility line, unexpected rock layer, or old foundation that wasn’t on any design map, the costs of delays from underground conflicts quickly add up. \n\nTake utility strikes, when an excavator hits an active utility line. For every dollar spent on the explicit repair cost of that utility line itself, the contractor loses an additional $30 per repair-dollar in project delays, insurance claims, idle work crews, and increased insurance premiums. For a hyperscale project such as data center construction, a single day of delay can cost the cloud-provider up to $14 million dollars for that single day.\n\nIn metropolitan areas, maps are incomplete at best and none create a unified picture of critical infrastructure, making excavation especially dangerous when natural gas or electric lines are broken.\n\nEnter SubVysion. We’ve built a fleet of autonomous rovers carrying ground-penetrating radar and other sensors that create a centimeter-accurate map of subsurface infrastructure. Our hardware is the first and the best to map the entirety of US infrastructure. Using our rovers, we comprehensively map construction sites, city streets, and even larger indoor buildings, at 100x better granularity and 10x the speed (with a single rover) compared to our competitors. We process vast amounts of data using machine learning and agents, and output that data into an easy-to-understand 3D scene. \n\nWho are we? Maxwell Stone was an early ML engineer at Roboflow (YC S20), and Wenson Tang built the world’s smallest and cheapest LiDAR-capable drone for adverse environments. We both studied computer science at the University of Pennsylvania. Together, we’re building SubVysion to map the entirety of America’s subsurface infrastructure.\n\nWe’re currently on job sites in Philadelphia and Texas, helping our first market of utility contractors save hundreds of thousands of dollars every year. We’re soon to expand our mapping services to municipalities and energy providers as we scale up our rover fleet and capacity!\n\nIf you’re in the energy, infrastructure, or hyperscale construction world, we’d love to hear from you at [founders@subvysion.com](mailto:founders@subvysion.com)!", "url": "https://www.ycombinator.com/launches/Sys-subvysion-preventing-150b-is-wasted-construction-spending", "votes": 7, "created_at": "2026-08-19T23:30:02.157Z" } ] }, { "id": 37468, "name": "Ageospatial", "slug": "ageospatial", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/74f6c9e933a7a4a3c86d3c81c6964ca26989ba26.png", "website": "https://ageospatial.com/", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "Ageospatial helps organizations value the risk on their physical assets (properties, land, infrastructure) remotely using satellite imagery.\r\n\r\n97% of commercial properties are never physically inspected because it's too slow and expensive. So insurers price risk based on what the client or broker tells them, and about half the time at least one of those records is wrong. The result is mispricing, fraud, and an estimated $36B in premium leakage every year in the US alone, which gets exposed the moment a disaster hits.\r\n\r\nUpload a list of addresses and their GeoAI reads satellite and street-level imagery, maps, real-time data, public records and the insurer's own data to determine what's really there, which natural and human hazards surround it, what changed over time, and which properties are worth inspecting. Each property becomes a living record that underwriting, claims and pricing teams can trace from past to present. Ageospatial deploys inside carriers and MGAs as an AI-native service, so insurers get outcomes delivered.", "one_liner": "Physical intelligence for every decision with Geospatial AI.", "team_size": 1, "industry": "Fintech", "subindustry": "Fintech -> Insurance", "launched_at": 1790148781, "tags": [ "Computer Vision", "Insurance", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "Fintech", "Insurance" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/ageospatial", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/ageospatial.json", "year_founded": null, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/in/maazhsheikh/", "x_url": "https://x.com/maazinsf", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/ageospatial", "github_url": "https://github.com/AGEOspatial", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/74f6c9e933a7a4a3c86d3c81c6964ca26989ba26.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Andrew Miklas", "url": "https://www.ycombinator.com/people/andrew-miklas" }, "founders": [ { "user_id": 2973429, "full_name": "Maaz Sheikh", "title": "Founder/CEO", "founder_bio": "Maaz has been making maps since he was 6. His superpower is having a spatial-memory, he can draw a 1 km-resolution world map from memory and was ranked top 100 in the world on GeoGuessr. He was hired at 19 to support wartime and natural-disaster decisions from satellite imagery and at 21 analyzed drones in the Arctic to study whether food could grow there if the ice melted. He spent 7 years building geospatial software for insurance, energy and governments.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/maazhsheikh/", "x_url": "https://x.com/maazinsf", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5b2709e2ef464249284afc8292d11ab5ebfe04ba.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 116579, "title": "Ageospatial - pricing the risk of every property, infrastructure and land on Earth from satellites", "tagline": "know the assets that you insure in the physical world", "body": "**TL;DR: insurers cover buildings, land and infrastructure they've never seen. we look at every one of them from space and tell them what they're really insuring and how risky it is.**\n\n\n\n![uploaded image](/media/?type=post&id=116579&key=user_uploads/2973429/b3467b52-ec9a-41b2-881c-6f84b0f0ba29)\n\n**the problem**\n\n* 97% of commercial properties are never inspected. it's too slow and too expensive to send someone.\n* so insurers rely on what they're told such as forms filled in by owners and brokers and half the time, at least one detail is wrong.\n* wrong info means wrong prices, fraud, and money lost. insured disaster losses passed $100B in 2025. in the US alone, insurers lose $36B a year to underpriced policies.\n\n**how it works**\n\n* give us a list of addresses, type one in, or click anywhere on the map\n* our AI reads satellite images, street-view, maps, public records, internal and live data\n* for each asset it tells you: what it really is, what hazards surround it, what changed, and whether it's worth inspecting\n* every asset gets a record that updates over time, and those records make the AI better at finding other risky ones\n* we set it up inside the insurer’s systems with our own engineers, so they get outcomes without hiring anyone\n\n**who am I** [@Maaz Sheikh](https://bookface.ycombinator.com/user/2973429)\n\n* I've been obsessed with maps since I was 6. I can draw the world map from memory\n* I was top 100 globally on GeoGuessr\n* modded video games and added location data to them as a teen, made money on mods\n* went to college at 17, studied Geomatics Engineering (CS + Geography)\n* hired at 19 to support decisions during wars and disasters using satellite imagery, worked on some of the largest crises of our time\n* went to the arctic to see if we can grow food where permafrost melts using drone imagery, lived in a container at -22°F\n* freelanced for large clients in insurance, energy and government\n\n[ageospatial.com](http://ageospatial.com)\n\n![uploaded image](/media/?type=post&id=116579&key=user_uploads/2973429/ed2be0a7-c6a8-4406-8120-da1b616fc0ad)\n\n![uploaded image](/media/?type=post&id=116579&key=user_uploads/2973429/e7f5d214-9e00-4ec2-b7f0-70bbde642b15)\n\n![uploaded image](/media/?type=post&id=116579&key=user_uploads/2973429/1abf4461-f8dc-405f-a184-300713a629a4)\n\n", "url": "https://www.ycombinator.com/launches/UKJ-ageospatial-pricing-the-risk-of-every-property-infrastructure-and-land-on-earth-from-satellites", "votes": 8, "created_at": "2026-09-29T16:00:00.089Z" } ] }, { "id": 37732, "name": "Hundred", "slug": "hundred", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f9f26995d92445a7e77c0839448bb04ca0c89f08.png", "website": "http://hundred.systems/", "all_locations": "San Francisco, CA, USA", "long_description": "Hundred is an automated inspection system for the components that make up consumer devices like phones, smartwatches, and laptops. Hundred helps factories cut waste and prevent line-down issues by catching sub-millimeter cosmetic defects before parts reach the assembly line. \r\n\r\nIn the consumer electronics industry, designs change so fast that traditional machine vision can't keep up, forcing factories to rely on manual inspection, literally people checking parts under microscopes to sort out bad parts. Human inspectors miss roughly 15% of defects, and a single bad part can turn a finished device into scrap. Hundred is replacing manual inspection with an imaging rig that scans an entire shipping tray of parts at once. \r\n\r\nThe Hundred rig blocks ambient light and uses varied controlled lighting configurations to detect defects smaller than 100µm on a range of materials. The controlled environment means Hundred only needs to scan a single good part before it can find defects, categorize them, and assess their severity accurately.\r\n\r\nDefects cost U.S. discrete manufacturers $45 billion a year, a problem Hundred's CEO, Erica, saw firsthand as a mechanical engineer designing devices at Meta, Google, and Magic Leap. She met Imani, Hundred's CPO, at Columbia University's admitted-students day in 2017 and Imani went on to spend five years at Twitch building software that handles billions of requests a day. Erica later met Iris, Hundred's CTO, in the Bay Area. Iris worked as a quant at Citadel before pivoting to study at Imperial College London where she researched machine learning on 3D meshes and other structured data. Together, they're building Hundred because manual inspection shouldn't still exist in 2026.", "one_liner": "100% automated inspection for consumer electronics parts", "team_size": 3, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1790785673, "tags": [ "Hard Tech", "Hardware", "Machine Learning", "Computer Vision", "Industrial" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/hundred", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/hundred.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/hundred-systems-inc/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f9f26995d92445a7e77c0839448bb04ca0c89f08.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Grey Baker", "url": "https://www.ycombinator.com/people/grey-baker" }, "founders": [ { "user_id": 3535016, "full_name": "Erica Diaz", "title": "Founder/CEO", "founder_bio": "Erica is co-founder and CEO of Hundred (YC F26).\nPreviously spent 5.5+ years at Meta, Google, and Magic Leap designing hardware for consumer electronics, and resolving issues on the ground with vendors in China and Mexico.\n\nColumbia University, BS Mechanical Engineering.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/erica-diaz1/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5ec31907973b972abef2c8b6371b1ae58f453c85.jpg" }, { "user_id": 317644, "full_name": "Imani Gosserand", "title": "Founder/CPO", "founder_bio": "Co-founder and CPO of Hundred (YC F26).\nPreviously Software Engineer at Twitch working at the intersection of notifications, short-form content, and ML.\n\nBS Computer Science from Columbia University", "is_active": true, "linkedin_url": "https://linkedin.com/in/imani-gosserand-804a10175", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/21384b93ca0a4b32ae2f6ed591a1d96b794799e0.jpg" }, { "user_id": 4149004, "full_name": "Iris Liao", "title": "Founder/CTO", "founder_bio": "Co-founder and CTO of Hundred (YC F26).\nPreviously Quantitative Researcher at Citadel.\nSpent the last year researching machine learning methods on 3D meshes and other structured data. \n\nMSc Applied Mathematics, Imperial College London\nBA Mathematics, Columbia University", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/iris-liao-03b041152", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e8c56193fe88be22d7e68840f5171512f9f41919.jpg" } ], "jobs": [], "news": [], "launches": [] } ]