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We get\ncustomers up and running in a day instead of weeks, and let them focus\non business instead of wrestling infrastructure.", "one_liner": "The BI bridge for unstructured data. We automatically bridge the gap…", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1322045630, "tags": [ "Analytics", "Big Data" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2012", "status": "Acquired", "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/amiato", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2012/amiato.json", "year_founded": null, "city": "San Francisco", "country": "US", "linkedin_url": null, "x_url": null, "fb_url": null, "cb_url": null, "github_url": null, "logo_url": null, "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 28952, "full_name": "Nathan Binkert", "title": "Founder", "founder_bio": null, "is_active": true, "linkedin_url": null, "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2077388f4d2dd7f53d1e0ecdf82c3e80febcb43e.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 89, "name": "Citus Data", "slug": "citus-data", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/202e7e02db190c7ab3eef78d63f9fe5d31e43275.png", "website": "http://citusdata.com", "all_locations": "San Francisco, CA, USA; Istanbul, Istanbul, Turkey; Amsterdam, NH, Netherlands", "long_description": "The amount of time businesses spend on their databases is altogether too much time. 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Using fixed sensors and robots that climb, crawl, swim, and fly, we combine first-order data layers with the predictive power of AI into a single source of truth for the physical world. 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Kevin, Jake and Troy talked through that path and Jake and Troy turned in their application almost immediately afterwards. Going through the batch was formative for helping Jake and Troy chart Gecko’s path forward. As the batch was starting they were weighing an acquisition offer but, thanks in large part to the advice they were getting at YC, they decided to reject the offer and build the company together." }, { "question": "How have you kept in touch with the YC community and continued to use YC resources & programs since the batch ended?", "answer": "Yes. Mostly through interactions with Kevin Hale, Jared Friedman, Sam Altman and Jon Levy. " }, { "question": "What's the history of your company from getting started until the present day? What were the big inflection points?", "answer": "Jake built the world’s first wall-climbing ultrasonic testing robot in 2012. Soon after we were making money but weren’t sure on the path to scale. Going though YC was a key inflection point that lead us to our model for scaling Gecko. The decision to turn down early acquisition attempts and investments in favor of building the company we wanted build to was another key inflection point. Gecko turned down offers right before demo day from investors in favor of walking. Fast forward to today and Gecko is closing deals with some of the biggest energy providers on the planet as well as global institutions like the U.S. Air Force and U.S. Navy." }, { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Jake visited a power plant while in college. The owner talked about how assets at the plant just fail and the plant shuts down regularly and that was seen as just the cost of doing business. That seemed wrong to Jake. It made him think about the importance of the built world and how much we rely on physical assets to provide power, water, safety and other critical services. Yet at the same time they are aging and starting to breakdown - Jake thought he had found a problem hiding in plain sight. Jake and Troy talked a lot about that in the early days of Gecko and committed the company on a path to deploy advanced robotics to solve this critical problem that no one else seemed to be working on." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "The long-term vision for Gecko Robotics is to transform the world’s most important industries that have been left behind in the digitalization movement. Gecko aims to be one of the first truly impactful robotics companies and prove that the best way to build incredible hardware and software is around solving real world problems and creating impactful outcomes for customers. 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Scale to millions.", "team_size": 120, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1596180346, "tags": [ "Developer Tools", "Open Source", "Big Data", "Data Engineering", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2020", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/supabase", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2020/supabase.json", "year_founded": 2020, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/supabase/", "x_url": "https://twitter.com/supabase", "fb_url": null, "cb_url": null, "github_url": "https://github.com/supabase", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/9e8c106f46123a6f28b7a7da41f0ee695da48786.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": 244419, "full_name": "Paul Copplestone", "title": "Founder", "founder_bio": "CEO, Supabase.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/paulcopplestone/", "x_url": "https://twitter.com/kiwicopple", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d8e9228a74c698fae602238ea69bf4699490a5e7.jpg" }, { "user_id": 189161, "full_name": "Ant Wilson", "title": "Founder", "founder_bio": "CTO @ Supabase (YC S20)", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/anthony-wilson-46179937/", "x_url": "https://twitter.com/antwilson", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7894aeaa75df52968edeaadd9115e60b84aeb442.jpg" } ], "jobs": [], "news": [ { "title": "Supabase Series F", "url": "https://supabase.com/blog/supabase-series-f", "date": "Jun 04, 2026" }, { "title": "Supabase nabs $5B valuation, four months after hitting $2B | TechCrunch", "url": "https://techcrunch.com/2025/10/03/supabase-nabs-5b-valuation-four-months-after-hitting-2b/", "date": "Oct 03, 2025" }, { "title": "Exclusive: Supabase raises $200 million Series D at $2 billion valuation | Fortune", "url": "https://fortune.com/2025/04/22/exclusive-supabase-raises-200-million-series-d-at-2-billion-valuation/", "date": "Apr 22, 2025" }, { "title": "Supabase, a Postgres-centric developer platform, raises $80M Series C | TechCrunch", "url": "https://techcrunch.com/2024/09/25/supabase-a-postgres-centric-developer-platform-raises-80m-series-c/", "date": "Sep 25, 2024" }, { "title": "Supabase raises $80M Series B for its open source Firebase alternative – TechCrunch", "url": "https://techcrunch.com/2022/05/10/supabase-raises-80m-series-b-for-its-open-source-firebase-alternative/", "date": "May 10, 2022" } ], "launches": [] }, { "id": 24156, "name": "Lariat Data", "slug": "lariat-data", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/48e2e81fa04d812d60bdd7e093944b8090c8742d.png", "website": "https://www.lariatdata.com", "all_locations": "New York City, NY, USA", "long_description": "Lariat is a Continuous Data Quality monitoring platform to discover data bugs before your consumers do. Ensure data products don’t break even as business logic, input data and infrastructure change.\r\n\r\nUse Lariat to define and then automatically extract, store and visualize data quality metrics on raw event-level data through to delivered data products.\r\n", "one_liner": "Observability for Data Engineering Teams", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1626560864, "tags": [ "Machine Learning", "Big Data", "Data Engineering" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Inactive", "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/lariat-data", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/lariat-data.json", "year_founded": 2021, "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/48e2e81fa04d812d60bdd7e093944b8090c8742d.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 283225, "full_name": "Aaditya Talwai", "title": "Founder", "founder_bio": "Co-founder & Chief Technology Officer, Lariat Data. \n\nAaditya has spent 10 years of his career building DevOps and developer infrastructure products. He was previously a software architect at Datadog, helping launch and scale the APM product across 100+ software runtimes and integrations. He has also built developer-facing products at Confluent and Bloomberg. He leads the technical team at Lariat Data, guiding product and architecture direction.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/talwai", "x_url": "https://twitter.com/AadityaTalwai", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/53586e1009253153eb206d40d628e8e4aab543d1.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 24195, "name": "Tarsal", "slug": "tarsal", "former_names": [ "V-diff", "vdiff" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/75747b61e59eadbd8831c406fbe1beed2fd4957b.png", "website": "https://www.tarsal.co/", "all_locations": "New York City, NY, USA; San Francisco, CA, USA; Remote", "long_description": "Tarsal is a data pipeline custom built for security teams. As security data grows 25% year over year, security teams desperately need access to best-in-class data infrastructure. 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Some give you JSON data, some give you unstructured text. Schematized data is rare.\n* There’s _a lot_ of it. Security data is growing at 23% CAGR, and log storage costs are incredibly high.\n\n## **✨ Our Solution**\n\n[**Tarsal**](https://tarsal.co) is the first data pipeline built for modern security teams. With zero-hassle, one-click connectors, security teams can ingest normalized data and route it to the destination of their choice.\n\n**Our unified schema is specifically designed to reduce the complexity in incident response.** By normalizing a set of standard fields across all log sources, security teams no longer have to write complex queries to track an incident across their systems. Query once against Tarsal fields — let us figure out how that maps back to the original data.\n\n**We save you money on log storage costs by letting you route logs to the cheapest destination.** With data growing 23% year over year, organizations face ballooning infrastructure cost. Tarsal lets you filter + route logs to any of our supported destinations, including major SIEMs, Amazon S3, or a modern data warehouse like Snowflake, BigQuery, or Databricks.\n\n**Tarsal lets you write real-time detections as code.** Write expressive and flexible detections in Python for needs specific to your enterprise. Benefit from pre-built detections that are mapped to specific threats.\n\n**We alert you if anything breaks in your log ingestion.** Rest assured that you’re collecting the data you’d need during an incident. Set up Slack or PagerDuty alerts if any of your logs stop syncing over.\n\n![](https://lh6.googleusercontent.com/s_iuAJD6ZiI_jJBjGYE8ndHSN0grI-BEHRg_unkfAwwFlRvqhiWxfDPsNQ14mUlakvOPbedvfdc_ITMJfFlT3KdMUYolC43L-wwZx39-LbomTRhIstS4EofeIhj2WWBFLqCZEnaoCwbV9DL9_0FP-zKCbw=nw)\n\n### **🎉 Interested?**\n\nEmail us at [founders@tarsal.co](mailto:founders@tarsal.co) and we’ll give you a link to sign up. You can also a book a call with me [here](https://calendly.com/rohit-rekhi/30min) to learn more.", "url": "https://www.ycombinator.com/launches/IU6-tarsal-data-pipeline-built-for-modern-security-teams", "votes": 58, "created_at": "2023-04-19T14:52:36.010Z" } ] }, { "id": 24499, "name": "Endla", "slug": "endla", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3964fbff527341490472ba6e23806a1eec6a7469.png", "website": "https://endla.com", "all_locations": "Brisbane, QLD, Australia; Remote", "long_description": "Endla increases the value of oil & gas wells by providing software that helps design the well that maximizes ROI.\r\n\r\nOur product AlphaSpace, optimizes the well design by automatically producing many high-quality options which the engineer can then measure against their business objectives (auto-design). Having software that finds the optimal solution, empowers the engineer to work a layer up on understanding the problem and specifying the important objectives.\r\n\r\nOur vision is to make auto-design and auto-operation software part of the workflow for every engineer working with physical assets.", "one_liner": "Software that increases the value of oil & gas wells", "team_size": 8, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1625096409, "tags": [ "Analytics", "Energy", "Big Data" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "Australia", "Oceania", "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/endla", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/endla.json", "year_founded": 2019, "city": "Brisbane", "country": "AU", "linkedin_url": "https://www.linkedin.com/company/endla", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/madebyendla", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3964fbff527341490472ba6e23806a1eec6a7469.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 715040, "full_name": "Riley O'Donnell", "title": "Founder", "founder_bio": "I worked in industry as a process engineer and built internal software to help my team do its job at both ExxonMobil and Santos. I saw the benefit of the software first hand, however, I also saw that these organisations were unable to maintain it long term. I'm passionate about combining engineer and software skill sets to address these shortcomings and help move the traditional engineering sector forwards.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/rileyodonnell-1/", "x_url": "https://twitter.com/riley_odo", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f85e18d8c84e5a8de5831f4d33a413bd35e5250a.jpg" }, { "user_id": 780197, "full_name": "Michael Moore", "title": "Founder", "founder_bio": "I’m a software specialist with a strong software architecture and mathematics background. Before Endla I consulted on software and infrastructure architecture design to solve pressing problems for businesses of all sizes. Through this I have seen many ways which software fails and how to avoid these issues. I cofounded Endla with Riley to solve a class of problems in a high value and underserviced industry, bringing the energy industry forward.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/michaelmoore1997/", "x_url": "https://twitter.com/michaeljmoore97", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/aa5c147858175b0e0f0dd8e075931585721cebe9.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 26860, "name": "NewsCatcher", "slug": "newscatcher", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4e6df1d33e5151e6b36a83a91e94b8bb2550c930.png", "website": "https://newscatcherapi.com/", "all_locations": "Kyiv, Ukraine; Remote", "long_description": "CatchAll by NewsCatcher is a recall-first web search API built for queries where the results are spread across hundreds or thousands of pages on the web. Instead of returning the top ranked links like traditional search engines, CatchAll retrieves a large candidate set from the web, validates which pages actually match the query, and extracts structured records of real-world events.\r\n\r\nDevelopers and data teams use CatchAll to answer “long-list” questions such as tracking regulatory actions, funding rounds, product launches, corporate expansions, or cybersecurity incidents. The output is not just links but clean, deduplicated datasets that can power AI agents, monitoring systems, analytics pipelines, and market intelligence workflows.\r\n\r\nCatchAll runs on the data infrastructure developed by NewsCatcher, which continuously indexes millions of articles and public web pages across a global network of sources.", "one_liner": "Turning the web into a structured database of real world events.", "team_size": 22, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1659698356, "tags": [ "SaaS", "Enterprise", "Big Data", "Enterprise Software", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "B2B", "Analytics" ], "regions": [ "Ukraine", "Europe", "Remote", "Fully Remote" ], "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": "Me (Artem) and Maksym, childhood pals since school days and university companions, teamed up to launch NewsCatcher in February 2020. Both Data Engineers with a knack for problem-solving, we were fed up with the status quo but passionate about data, laying the groundwork for our startup. Embracing the ethos of rapid iteration, we wasted no time, kicking off our journey by creating a Telegram chat dubbed \"First million\".Following Y Combinator's mantra of swift action, we rolled out our beta in April 2020, fuelled by AWS credits from YC Startup School. Our early strides were apparent when a Python package we crafted gained traction on Reddit, attracting a flurry of sign-ups. By June 2020, we debuted our V1 product on RapidAPI, landing our first paying customer—a serendipitous encounter with a US student bound for YC W24 batch. Our trajectory continued with applications to accelerators like TinySeed and YC, ultimately opting for TinySeed's rapid application process, albeit declining YC's offer later on. With strategic hires and laser-focused growth, we had a revenue surge from $3,000 to $25,000 MRR within a year. We circled back to YC, securing entry in May 2022 with $300,000 ARR. Despite hurdles, like my temporary detour to aid the Armed Forces of Ukraine, we thrived, clinching enterprise contracts and elevating ARR to $450,000 by Demo Day. Post-YC, while grappling with revenue challenges hindering our progress, we gleaned valuable insights into customer alignment, reaffirming our dedication to efficient growth and customer-centricity." }, { "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": "Deciding to apply to Y Combinator was a big step for us. We wanted to take NewsCatcher to the next level, and YC seemed like the perfect fit with its reputation for helping startups succeed. In early 2021, we applied to TinySeed, Alchemist Accelerator, and YC for the third time. TinySeed offered us a spot, which we accepted, even though we also got an interview invitation from YC. We had a monthly revenue of $3,000 at that time. Over the next year, we hired Aditya, Aakash, and Ibrahim and grew our monthly revenue to $25,000. This encouraged us to apply to YC again. In May 2022, despite the challenges of the Ukraine conflict and Artem's temporary absence to help the Armed Forces, we decided to reapply to YC. Our dream came true when Michael Seibel interviewed us, and we got in. Joining YC with around $300,000 in yearly revenue, we embraced their focus on fast growth and solving real problems. Throughout the program, we learned a lot from mentors and other founders. By Demo Day, our yearly revenue had grown to $450,000. After YC, Artem spent two days fundraising and found a great investor in Uncommon Capital, led by Jamie Quint. We also got support from other YC alums." }, { "question": "How have you kept in touch with the YC community and continued to use YC resources & programs since the batch ended?", "answer": "Staying connected with the YC community has been super important for us as we figure out life after our time in the batch with NewsCatcher. We've been using all the cool stuff YC offers to help us grow and get better. Their knowledge base has been a goldmine of tips and tricks that have really helped us make NewsCatcher bigger and better.We've also been talking to other folks in the YC community for advice on running our business and bouncing around ideas. It's been awesome to have a bunch of other founders and experienced mentors cheering us on and giving us advice as we tackle the challenges of starting up.Plus, we've been taking advantage of discounts YC offers for different services, which has been a huge help in making our operations smoother and saving us some money. The YC community has been super generous in sharing their resources and expertise, and we're really grateful for all the support and camaraderie. It's been a big part of our journey as founders, and we couldn't have done it without them!" }, { "question": "What's the history of your company from getting started until the present day? What were the big inflection points?", "answer": "**February 2020:** me and Maksym, both Data Engineers, co-found NewsCatcher driven by a shared passion for data and dissatisfaction with existing solutions. **April 2020:** Launch of NewsCatcher's beta, followed by the release of V1 product on RapidAPI in June, attracting first paying customers. **January-February 2021:** Joining TinySeed accelerates growth, leading to rapid revenue increase and team expansion. **May 2022:** Reapplication and acceptance into Y Combinator highlight commitment to excellence and innovation. **2023:** NewsCatcher experiences significant growth, achieving an ARR of $1.13 million. **2024:** Setting ambitious goals, aiming to grow ARR to $3.7 million. Focus on building scalable customer success processes to maximize retention and upsells. Additionally, hiring first Account Executives to drive sales growth and expand market reach." }, { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "NewsCatcher is all about tackling the big challenge of making real-time news data easy to access from a wide range of sources. Many businesses and researchers struggle to get up-to-date info for things like market analysis, trend tracking, and content creation. It's a pain to manually gather news from lots of places—it takes forever and isn't very efficient. We decided to tackle this problem because, as Data Engineers ourselves, we know the frustration of trying to get good news data for projects. We realized that making this info more accessible could have a huge impact, so we decided to use our skills to do something about it.With NewsCatcher, our goal is to make real-time news data available to everyone. We've gathered news from over 70,000 sources and made it easy and affordable for anyone who needs it. We believe that by solving this problem, we can help spark innovation and improve decision-making.We're all about prioritizing data quality, adaptability, great customer service, and keeping things simple. That way, our customers can always count on getting the news they need to succeed." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "Our long-term vision for NewsCatcher is to change the game when it comes to how businesses get and use news data, making the business world more informed and connected. If we truly succeed:\\* Businesses of all sizes will have equal access to real-time news data.\\* Decision-making across industries will be more informed and agile.\\* Innovation will be accelerated, driving economic growth.\\* Transparency and accountability in business operations will be enhanced.\\* Global connectivity and collaboration among businesses will be strengthened.Basically, we want to give businesses the tools they need to succeed in today's fast-paced and interconnected global market." } ], "url": "https://www.ycombinator.com/companies/newscatcher", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/newscatcher.json", "year_founded": 2021, "city": "Kyiv", "country": "UA", "linkedin_url": "https://www.linkedin.com/company/newscatcherapi/?viewAsMember=true", "x_url": "https://twitter.com/newscatcherapi", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/4e6df1d33e5151e6b36a83a91e94b8bb2550c930.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 444913, "full_name": "Artem Bugara", "title": "Founder", "founder_bio": "Artem Bugara is the co-founder, CEO of NewsCatcher. Artem works with data and risk teams to help them integrate actionable signals from online news. Previously he ran a team of data engineers for one of the largest underwriting companies within the aviation, energy, and space insurance sectors.\n\nHe earned a degree in Econometrics from Toulouse School of Economics. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/artem-bugara/", "x_url": "https://twitter.com/bugaralife", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/717c77848f35ff3f50a1978f5ec2430ab5b55273.jpg" }, { "user_id": 472880, "full_name": "Maksym Sugonyaka", "title": "Founder", "founder_bio": "Maksym Sugonyaka is the co-founder and CTO of NewsCatcher. After one year of a stable job, Maksym understood that the only place where he could realize his potential was entrepreneurship. Now in NewsCatcher, Maksym is the one to take key strategic and technical decisions in order to make the best News API on the market. \n\nHe has a Master's Degree in Data Science and Finance from Paris Sorbonne. But then, he became a self-taught Data Engineer. \n\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/msugonyaka/", "x_url": "https://twitter.com/kizy625", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0d3748b7ed0c74799452205a4a62beb4d707d66b.jpg" } ], "jobs": [ { "id": 92394, "title": "Software Engineer (Backend & LLM)", "url": "https://www.ycombinator.com/companies/newscatcher/jobs/cuGf2UW-software-engineer-backend-llm", "location": "LB / UA / PL / LT / RO / Remote (LB; UA; PL; LT; RO)", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$60K - $85K", "equity_range": "0.10% - 0.30%", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [ "Kubernetes", "Python", "Docker" ] } ], "news": [], "launches": [ { "id": 99812, "title": "CatchAll: Recall-first web search API", "tagline": "Turning the web into a structured database of real-world events", "body": "Hello, hackers! We’re Artem and Maksym, founders of NewsCatcher.\\\n\\\nWe’re building a structured, event-centric index of what happens in the real world. Today, that starts with CatchAll — a recall-first search API that finds all relevant matches to a query, extracts structured facts, and delivers clean, ready-to-use datasets.\\\n\\\n\\\n\\\n**The problem**\\\nTraditional web search — and most search APIs — were designed for humans. They optimize for **speed and ranking**, which works well when one page contains the answer:\n\n* “Who is the CEO of a company?”\n* “What’s the latest acquisition in X?”\n\nBut many high-stakes questions don’t have a single answer:\n\n* _All_ regulatory actions affecting an industry\n* _Every_ cybersecurity incident in a given week\n* _All_ funding rounds, policy changes, or facility expansions\n\nIn these cases, **recall is everything**.\\\nIf 200 valid events exist and your system surfaces 5, your recall is 2.5%. No amount of prompting or post-processing fixes that.\n\nLLMs and “deep research agents” haven’t solved this. They still open and read pages one by one, hit context limits, and end up sampling rather than exhausting a topic.\n\n\\\n**The solution**\\\nCatchAll is a recall-first web search API built for long-list answers — answers distributed across the open web.\n\nCatchAll:\n\n* Pulls a large candidate set from NewsCatcher’s proprietary web index (often tens of thousands of pages)\n* Validates which pages actually match the criteria\n* Extracts structured fields (companies, dates, locations, actions, amounts — defined by the user)\n* Normalizes and deduplicates everything into clean records\n\nThe output isn’t links.\\\nIt’s a dataset that didn’t exist before — and can be kept up to date by turning the query into a **live monitor**.\n\nFor teams, the end result is fewer searches and more reliable alerts.\\\n\\\n**Backstory**\\\nMaksym and I have known each other for almost 20 years. NewsCatcher is our first startup — we've been building it since 2020. It started as a bootstrapped self-serve news API for startups, and then grew into custom enterprise solutions (powering the US Department of State, Transparency International, Samsung, 50+ enterprise clients). CatchAll is us coming back to self-serve: same infrastructure, now optimized for the AI agent ecosystem.\\\n\\\nSign up [here](https://platform.newscatcherapi.com/catchall?utm_source=launch_yc&utm_medium=social&utm_campaign=Launch_YC_CatchAll&utm_id=catch_all_YC), test CatchAll (for free), and give us feedback (we’ll 5x your credits!).\\\n\\\nShare your use-cases:\\\n[artem@newscatcherapi.com](mailto:artem@newscatcherapi.com) or DM on [https://www.linkedin.com/in/artem-bugara](https://www.linkedin.com/in/artem-bugara/)\n\n![uploaded image](/media/?type=post&id=99812&key=user_uploads/444913/74de4236-76fc-4c2b-8ef4-d02248dd0837)\n\n", "url": "https://www.ycombinator.com/launches/Pxs-catchall-recall-first-web-search-api", "votes": 5, "created_at": "2026-04-16T15:13:11.193Z" }, { "id": 65153, "title": "NewsCatcher - News API for risk and data teams", "tagline": "We help data and risk enterprise teams get instant access to machine-readable news from over 40,000 online news sources", "body": "**tl;dr:** We make it easy for data, market research, and risk teams to integrate machine-readable news feed into their internal analytics system. \\\n\\\nHey Fam, we're [Artem](https://www.linkedin.com/in/artem-bugara/) and [Maksym](https://www.linkedin.com/in/msugonyaka/) and we're building [NewsCatcher](https://newscatcherapi.com/). We're on a mission to empower data-driven organizations with the highest quality and easiest-to-use News API. \\\n\\\n**❌ The Problem**\n\nIt’s easy to consume news as a human: you open Google News and read.\n\nNow, imagine:\n\n* you work for a bank, and you have to perform negative news searches for all new & existing accounts for anti-money laundering & financial crime checks\n* you work for a PR firm that has to monitor news mentions on your clients and their competitors\n* you’re an analyst in an investment firm, and you need to calculate news sentiment for each company on the market over the past N years\n* you want to show relevant news to the users on your app to make them spend more time on it\n\n“Humans reading the news” is not a scalable way to resolve a problem anymore, and it’s expensive.\n\n**✨ Our Solution**\n\nSearch and integrate news with code. Find news articles by any topic, country, language, website, or keyword with REST JSON News API.\\\n**Go ahead, and make some API calls on our** [**demo page**](https://demo.newscatcherapi.com/)**.**\n\nWe’ve built a generic news crawler that automatically finds new articles, normalizes them, and indexes them. So, you can now automate the news consumption and analysis via an API.\n\nYou can read more over this [link](https://docs.newscatcherapi.com/knowledge-base/how-newscatcher-works) from our knowledge base.\n\nHere is how an article would look in our search:\n\n![uploaded image](/media/?type=post&id=65153&key=user_uploads/444913/1c4d6e37-3e46-4c2a-a864-a969a80279e4)\n\n\\\nTop 3 use cases from our clients:\n\n* **risk intelligence**: find insights in the news before they get visible in financial reports days or months later\n* **integrate news** into your app\n* **media monitoring** on millions of companies all around the world\n\n\\\n\\\n**🙏 Asks**\n\n1. Try our News API: claim your [API key](https://app.newscatcherapi.com/auth/register), or/and [book a technical session with us](https://savvycal.com/newscatcher/chat)\n2. Are you experienced with extracting data from HTML, or you’re an NLP expert? We’d love to chat!", "url": "https://www.ycombinator.com/launches/Gwr-newscatcher-news-api-for-risk-and-data-teams", "votes": 46, "created_at": "2022-08-05T13:27:00.532Z" } ] }, { "id": 28787, "name": "Deasy Labs", "slug": "deasy-labs", "former_names": [ "Deasie" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b2f7a8dffc7dc8a6fdb4c13dce765cc7d2820b9a.png", "website": "https://www.deasylabs.com/", "all_locations": "New York City, NY, USA", "long_description": "Deasy Labs was acquired by Collibra in July 2025 (global leader in enterprise data governance).\r\n\r\nDeasy Labs provides metadata orchestration for AI workflows. Deasie's platform provides the best way for AI teams to create and embed high-quality, customized metadata into their AI workflows (e.g., RAG, Agentic frameworks).\r\n\r\nOur three founders (from Amazon, McKinsey/QuantumBlack & MIT) previously built an ML data governance tool from 0 to 1 within McKinsey, which we deployed with 11 Fortune 500 companies. We saw in early 2023 the ability to create high-quality metadata (without reliance on domain experts) would be a key factor in achieving the accuracy & speed in GenAI applications required for production. \r\n\r\nOur investors include General Catalyst, Y Combinator, RTP Global and world experts in enterprise data. Website: https://deasylabs.com", "one_liner": "Metadata for GenAI workflows", "team_size": 8, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1691123582, "tags": [ "Data Labeling", "Big Data", "AI", "AI Assistant", "Databases" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Acquired", "industries": [ "B2B", "Analytics" ], "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/deasy-labs", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/deasy-labs.json", "year_founded": 2023, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/deasie", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b2f7a8dffc7dc8a6fdb4c13dce765cc7d2820b9a.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": 1227239, "full_name": "Reece Griffiths", "title": "Founder", "founder_bio": "Co-founder of Deasy Labs (YC S23): Data governance for language models in the enterprise. \n\nExited Deasy Labs to Collibra in < 2 yrs (global leader in data governance).\n\nMy documented startup journey: https://reecegriffiths.substack.com/\n\nPrior experience: QuantumBlack: Product Manager for AI for Data Governance product (1.5 yrs); McKinsey & Company: Management consultant (2 yrs); X26: Founder of X26 (global entrepreneurs community); University of Cambridge: Masters in information engineering.", "is_active": true, "linkedin_url": "https://linkedin.com/in/reece-william-griffiths", "x_url": "https://twitter.com/ReeceWGriffiths", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/53fe8f42805f99012d0a74523cf2874504663377.jpg" }, { "user_id": 1237667, "full_name": "Mikko Peiponen", "title": "Founder", "founder_bio": "Mikko is the co-founder of Deasie. He is a technologist with deep experience (5+ years) in data and machine learning space from McKinsey and QuantumBlack, where he implemented and deployed models at large enterprises. Prior to moving into the world of data governance for language models with Deasie, he was the Lead data scientist building AI data quality tool at QuantumBlack. Mikko holds a graduate degree from MIT.", "is_active": true, "linkedin_url": "https://linkedin.com/in/mikkopeiponen", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9dd45b70aec24740409919d85c62d51a6b55da39.jpg" }, { "user_id": 1226121, "full_name": "Leonard Platzer", "title": "Founder", "founder_bio": "Co-founder of Deasy Labs: The Metadata Engine for search usecases. \n\nPrior experience: QuantumBlack: Software Engineer, Machine Learning at AI for Data Quality product; Previous Engineer at Amazon, Mercedes-Benz and ChatBot Startup (E-Bot 7); Bachelor from National University of Singapore and Graduate from UWC Mostar", "is_active": true, "linkedin_url": "https://linkedin.com/in/leonardplatzer", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/91e9bdf6844bdc43fac38dbfaa0bf7fedda307c0.jpg" } ], "jobs": [], "news": [ { "title": "Data governance platform Deasie raises $2.9 million in seed funding - Business Leader News", "url": "https://www.businessleader.co.uk/deasie-raises-2-9-million-seed-funding/", "date": "Oct 13, 2023" }, { "title": "Lakera and Deasie raise funding to make AI large language models more reliable - SiliconANGLE", "url": "https://siliconangle.com/2023/10/12/lakera-deasie-raise-funding-make-large-language-models-reliable/", "date": "Oct 12, 2023" }, { "title": "Deasie raises $2.9M for data governance platform expansion - Tech.eu", "url": "https://tech.eu/2023/10/12/deasie-raises-29-million-for-data-governance-platform-expansion/", "date": "Oct 12, 2023" }, { "title": "Deasie raises $2.9M for data governance platform expansion - Tech.eu", "url": "https://tech.eu/2023/10/12/deasie-raises-29-million-for-data-governance-platform-expansion/", "date": "Oct 12, 2023" }, { "title": "Deasie wants to rank and filter data to make generative AI more reliable | TechCrunch", "url": "https://techcrunch.com/2023/10/12/deasie-wants-to-rank-and-filter-data-to-make-generative-ai-more-reliable/", "date": "Oct 12, 2023" } ], "launches": [ { "id": 73808, "title": "Deasie - Data governance for language model applications", "tagline": "Is your enterprise data ready for language models?", "body": "**tl;dr: Deasie ensures that only relevant, high-quality, and safe data is fed into language models.**\n\nHi! We’re Reece, Leo, and Mikko and we’re excited to launch [Deasie](https://www.deasie.com).\n\n⚠️ **Problem**\n\nFor the first time, companies are turning towards their masses of unstructured data (e.g., documents, reports, emails) for a range of Generative AI use cases. Today, most companies are unable to answer the critical questions needed to ensure language models are trained and deployed reliably: Does this data contain sensitive information? Is this the most relevant data for this problem? Are there inconsistencies in the data that could skew my results?\n\n💡 **Solution**\n\nDeasie is a platform that provides automated checks for compliance (e.g., PII & proprietary data) and quality (e.g., irrelevant, untimely, or inconsistent information) of unstructured data, to reliably govern which data is used for which language model use case.\n\n👨‍👦‍👦 **The Team**\n\nFounders Leo, Mikko & Reece previously built McKinsey's award-winning product that used AI to enhance data quality in the enterprise, built ML applications across QuantumBlack, MIT, Amazon, and Mercedes, and have now set out to unlock the power of unstructured data for the upcoming wave on language model applications.\n\n![uploaded image](/media/?type=post&id=73808&key=user_uploads/1237667/ede65ff1-1431-4c6f-88aa-dd343d325f4d)\n\n👋 **Ask**\n\n**Lots of companies must be delving into the world of LLMs. If you’ve encountered challenges identifying and using high-quality input data (e.g., data that is relevant, timely, accurate, and consistent), then we’d love to hear from you! Please reach out to us at** [**leonard@deasie.com**](mailto:leonard@deasie.com)", "url": "https://www.ycombinator.com/launches/JCS-deasie-data-governance-for-language-model-applications", "votes": 16, "created_at": "2023-08-04T06:57:33.350Z" } ] }, { "id": 28981, "name": "Energent AI", "slug": "energent-ai", "former_names": [ "WAN", "WAN.ai", "CambioML" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bae38d9d5867168b2fe49799661efe7a7f64d1d1.png", "website": "https://energent.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Every AI tool will confidently hand you a wrong number — and in\r\nengineering and finance, a wrong number is expensive.\r\n\r\nEnergent.ai is an AI teammate that does your data work on a secure\r\ndesktop: reading documents and engineering drawings, cleaning messy data,\r\nwriting code, building charts. Then a second, independent agent audits\r\nthat output against your original source files and flags every claim that\r\ndoesn't hold up — catching roughly 3x more hallucinations than the model\r\nalone in our internal evals.\r\n\r\nEngineering teams check drawing sets before issue. Finance teams tie\r\nnumbers back to source. The best teams encode their own checking\r\nstandards as reusable rules, so it checks the way their firm checks.", "one_liner": "AI that audits AI — catching hallucinations before you ship them.", "team_size": 10, "industry": "B2B", "subindustry": "B2B", "launched_at": 1690762424, "tags": [ "Productivity", "Big Data", "Enterprise Software", "Automation", "AI Assistant" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "B2B" ], "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/energent-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/energent-ai.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/96598226", "x_url": "https://twitter.com/cambioml", "fb_url": null, "cb_url": null, "github_url": "https://github.com/CambioML", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/bae38d9d5867168b2fe49799661efe7a7f64d1d1.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 315284, "full_name": "Rachel Hu", "title": "Founder", "founder_bio": "Co-founder and CEO of Energent.ai. Previously Applied Scientist at AWS; Built LLMs and Led open-source ML projects D2L.ai adopted by over 500 universities around the world; AWS senior speaker: talked at AWS re:Invent, Nvidia GTC, KDD, etc..", "is_active": true, "linkedin_url": "https://linkedin.com/in/rachelsonghu", "x_url": "https://twitter.com/rachelhuml", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/658fccd20c27c542bddcbd3f85c0368f6b777a52.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 104955, "title": "Energent.ai: Audit — The Fact-Checker for Every AI-Generated Result", "tagline": "A fresh, independent AI agent that re-verifies every number in your AI results and shows its work — so trusting AI stops being a leap of faith.", "body": "Hey everyone! We're [Rachel](https://www.linkedin.com/in/rachelsonghu/) and [Kimi](https://www.linkedin.com/in/0xlingjiekong/), founders of [Energent.ai](https://www.energent.ai). We're excited (and honestly, a little proud) to introduce our newest feature: **Energent Audit**.\n\n## 🚀 TL;DR\n\nEnergent Audit is a built-in, independent second opinion for your for every AI-generated results, especially numbers. Ask Energent to \"prove it,\" \"double-check that,\" or \"show me where this number came from,\" and Audit spins up a completely fresh sub-agent — one with zero stake in the original answer — to retrace every number back to its source and hand you a clean HTML report on whether it holds up.\n\n## ❌ The Problem\n\nAI hallucination hasn't gone away — it's just gotten quieter. It shows up in the reports people care about most: financial analysis, health recommendations, board decks, the numbers real decisions get built on.\n\nWe interviewed 150+ users doing serious work with AI, and hallucination came back as their second-biggest concern overall — right after data security and privacy, and ahead of cost, speed, and everything else. The message was clear: people don't just want AI that's fast. They want AI they can stand behind.\n\n## 🛠️ Our Solution\n\nMeet **Energent Audit** — an independent audit layer for anything Energent builds for you.\n\nHere's the insight at the heart of it: the agent that wrote an answer is the worst-positioned agent to grade it. So instead of asking Energent to mark its own homework, Audit calls in a brand-new AI sub-agent with no memory of how the analysis was built and no attachment to the outcome. Its only job is to verify.\n\n**Independent, every time**\n\nA fresh sub-agent re-checks the work with zero investment in the original answer, so nothing gets rubber-stamped.\n\n_\"The agent that built it doesn't get to grade itself.\"_\n\n**Number-by-number validation**\n\nEvery figure is traced back to its source and re-derived — not glanced at, actually re-verified, one by one.\n\n_\"Trust, but verify, down to the decimal.\"_\n\n**Visual data lineage**\n\nA clear map of exactly which file, row, and transformation produced each number — so \"where did this come from?\" always has an answer.\n\n_\"Show your work, automatically.\"_\n\n**A report you can actually hand someone**\n\nEvery audit ends in a clean, shareable HTML report you can send to a stakeholder, a boss, or an auditor — or just keep for your own peace of mind.\n\n_\"Confidence you can forward.\"_\n\n![uploaded image](/media/?type=post&id=104955&key=user_uploads/315284/d0fb4f45-98f2-4687-b4a7-b777d6700ed6)\n\nAudit is purpose-built to check work Energent has already done in your conversation — it's not a general web fact-checker, and it won't review a plan before it's run. It's laser-focused on one question: does this answer, and every number in it, actually hold up?\n\n## 📖 Why We Built This\n\nThe same story came up again and again in our interviews: people loved what AI could do for their analysis, right up until they had to defend a number in a meeting. That gap — between \"the AI said so\" and \"I can prove it\" — is exactly what Audit closes. We believe trust in AI shouldn't be something you hope for. It should be one click away.\n\n## 🎯 Our Ask\n\n👉 Try Energent Audit today at [app.energent.ai](http://app.energent.ai) — run an analysis, then ask us to prove it.\n\n👉 Reach out directly at [admin@energent.ai](mailto:admin@energent.ai) to see it live.\n\nCheers, The [Energent.ai](http://Energent.ai) Team ❤️", "url": "https://www.ycombinator.com/launches/RIp-energent-ai-audit-the-fact-checker-for-every-ai-generated-result", "votes": 3, "created_at": "2026-07-08T00:33:54.610Z" }, { "id": 99597, "title": "ERPNow: The AI Analyst for Supply Chain & ERP", "tagline": "turn tons of operational data into real-time operational insights and future revenue", "body": "Hey everyone! We're Rachel and Kimi, the founders of CambioML. We are excited to introduce **ERPNow**.\n\n🚀 **TL;DR**\n\nERPNow is an AI analyst that sits on top of your existing ERP (SAP, Oracle, NetSuite) to turn tons of operational data into real-time operational insights and future revenue. It doesn’t just show you charts; it detects risks, explains root causes in plain language, and recommends exactly what to do next.\n\n❌ **The Problem**\n\nSupply chain teams are drowning in data but starving for insights. Traditional ERP systems are powerful but static—reports are slow, forecasts break the moment reality changes, and analysts spend weeks answering \"why did this happen?\" instead of \"what should we do next?\" By the time an exception is surfaced in a dashboard, the damage (stockouts or delays) is often already done.\n\n🛠️ **Our Solution**\n\nERPNow acts as an intelligent layer over your ERP, WMS, and spreadsheets. It provides:\n\n* **Real-time Risk Detection:** Monitors your supply chain 24/7 to surface anomalies (like a supplier delay or demand spike) the moment they appear.\n* **Plain-Language Root Cause Analysis:** Ask \"Why is this SKU at risk?\" and get a cited answer explaining that a specific supplier lead time increased by 40%, rather than just seeing a red bar on a chart.\n* **Actionable Recommendations:** It suggests specific fixes—like transferring inventory between warehouses or expediting a PO—to prevent issues before they escalate.\n* **Zero Workflow Disruption:** No ERP replacement required. It’s read-only by default and deploys in weeks, not quarters.\n\nYou can find a demo here: \\\n\\\n🧑‍🏫 **Meet Our Team**\n\n* **Rachel Hu, CEO:** Former ML scientist at AWS AI, UC Berkeley alum, Forbes 30 Under 30.\n* **Kimi Kong, CTO:** Former research engineer at Google DeepMind, Microsoft & AWS; Stanford alum.\n\n📖 **Our Story**\n\nAfter building [Energent.ai](http://Energent.ai) to handle general data analysis, we realized that supply chain and operations teams faced the most \"messy\" data challenges. ERP systems are often black boxes. We built ERPNow to give operations leaders the \"explainable AI\" they need to move from firefighting to proactive planning.\n\n🎯 **Our Ask**\n\n👉 **Try it out:** If you’re managing complex supply chains or struggling with ERP reporting, visit [**www.erpnow.io**](http://www.erpnow.io).\n\n👉 **Get a Demo:** Reach out directly to [**admin@cambioml.com**](mailto:admin@cambioml.com) to see how we can sit on top of your specific ERP setup.\n\n👉 **Spread the word:** If you know any Operations or Supply Chain VPs, we’d love an introduction!\n\nCheers,\n\nThe ERPNow Team ❤️", "url": "https://www.ycombinator.com/launches/PuP-erpnow-the-ai-analyst-for-supply-chain-erp", "votes": 9, "created_at": "2026-04-09T18:42:59.948Z" }, { "id": 95030, "title": "Energent.ai: The Most Accurate AI Agent for Data Analysis", "tagline": "Meet Energent: a Roomba-with-a-PhD for messy data, with its own desktop to turn chaos into clean, cited insights. It ranks at the top of Hugging Face’s Best Data Agent benchmark.", "body": "Hey everyone! We're [Rachel](https://www.linkedin.com/in/rachelsonghu/) and [Kimi](https://www.linkedin.com/in/0xlingjiekong/), founders of CambioML. We are excited to introduce [**Energent.ai**](http://Energent.ai) Data Specialist.\n\n**🚀 TL;DR**\n\n[**Energent.ai**](http://Energent.ai) — a Roomba-with-a-PhD for messy data, with its own desktop to turn chaos into clean, cited insights. It ranks at the top of Hugging Face’s Best Data Agent benchmark.\n\n\n\n# **❌ The Problem**\n\nMost businesses can’t afford a full data team, yet they’re drowning in scattered spreadsheets, PDFs, and ad-hoc reports. Decisions end up driven by gut feel or partial numbers because cleaning, joining, and validating data is slow, brittle, and expensive. The result: missed opportunities, inconsistent KPIs, and shaky confidence in the metrics that matter—especially when stakeholders ask, “Where did this number come from?”\n\n# **🛠️ Our Solution**\n\n[Energent.ai](http://Energent.ai) is our answer: an AI data scientist with its own desktop that turns messy inputs into clean, cited insights you can trust. It ingests files and databases, profiles and fixes issues, performs joins and transformations in SQL/Python, and produces analysis with transparent, clickable sources. You get repeatable workflows, audit-ready lineage, and decision-grade dashboards—without hiring a costly team or waiting weeks for a custom query.\n\n**The most accurate data agent**\n\n* Rank No.1 on the [Huggingface Data Agent leaderboard](https://huggingface.co/spaces/adyen/DABstep).\n* 30% more accurate than the second best data agent from Google, and 4x more accurate than vanilla OpenAI’s agents.\n\n ![uploaded image](/media/?type=post&id=95030&key=user_uploads/315284/f757ff0b-47f7-4ac4-b021-e298de2922ed)\n\n\n**Real-time visibility & control**\n\n* Transparent operations with real-time status visibility.\n* Easy intervention and clear workflow controls.\n* _“Stay in the loop, stay in control.”_\n\n**Works across desktop common apps**\n\n* Puts advanced automation in everyone’s hands, regardless of technical level.\n* Increases productivity and agility team-wide.\n* _“Automation without learning a new app.”_\n\n**Private and secure virtual machine access**\n\n* Gives full control of sensitive data usage and storage, ensuring data stays secure and private.\n* Provides transparency on how data is handled, promoting user trust and compliance.\n* _“Your data protected, your privacy respected.”_\n\n# \n\n**🧑‍🏫 Meet Our Team:**\n\n* [**Rachel Hu**](https://www.linkedin.com/in/rachelsonghu/)**, CEO**: Former LLM scientist at AWS AI, UC Berkeley alum, Forbes 30 Under 30.\n* [**Kimi Kong**](https://www.linkedin.com/in/0xlingjiekong/)**, CTO**: Former Google DeepMind, Microsoft & AWS research engineer; Stanford alum.\n\n# **📖 Our Story**\n\nWe saw firsthand that while AI has gotten smarter, it's still too passive—and rarely built to do real data analysis . [Energent.ai](http://Energent.ai) was born to change that.\n\nWith [Energent.ai](http://Energent.ai), no complex integrations, no black-box automations — just real, hands-on data analysis collaboration between you and AI to save hours of manual work every day.\\\n\\\n[Energent.ai](http://Energent.ai) has been using by \n\n* [ERPNow.io](http://ERPNow.io): an AI-powered Supply Chain and ERP Analytics platform;\n* [teamvibe.io](http://teamvibe.io/): an HR Software for Automated Payroll & Performance;\n* [goodtenant.io](http://goodtenant.io/): an AI PropTech for Smarter Property Management;\n* [esgverify.io](https://esgverify.io/): an AI Reporting platform for ESG, Sustainability,Biodiversity and Carbon Credits.\n\n# **🎯 Our Ask:**\n\n👉 Get your AI Teammate at today!\n\n👉 **Reach out directly** at [admin@energent.ai](mailto:admin@energent.ai) to schedule a call.\n\nCheers,\\\nThe Energent.ai Team ❤️\n\n![uploaded image](/media/?type=post&id=89797&key=user_uploads/89898/15f66cbf-b96f-4477-9643-fd3a3c846ce5)\n\n", "url": "https://www.ycombinator.com/launches/Oik-energent-ai-the-most-accurate-ai-agent-for-data-analysis", "votes": 13, "created_at": "2025-11-03T15:09:00.249Z" }, { "id": 89797, "title": "Energent.ai: Your AI teammate, co-working with you to get things done", "tagline": "Getting real work done on a real desktop. Visible. Controllable. Secure.", "body": "Hey everyone! We're [Rachel](https://www.linkedin.com/in/rachelsonghu/) and [Kimi](https://www.linkedin.com/in/0xlingjiekong/), founders of CambioML. We are excited to introduce [**Energent.ai**](http://Energent.ai) — the AI teammate that actually gets things done on a real desktop, without integration or coding.\n\n**🚀 TL;DR**\n\n[Energent.ai](http://Energent.ai) works side-by-side with you on a secure desktop to automate data-heavy multi-step workflows, without integration or coding — from gathering public info online, to cleaning messy format data, to coding and drawing plots.\n\nhttps://youtu.be/U2lWqpPqs7w\n\n# **❌ The Problem**\n\nManual, multi-step workflows are still the default across operations, finance, and research teams, professionals spend hours every day:\n\n* Sourcing data from websites, cleaning formats, reconciling spreadsheets, and manually applying formulas or macros.\n* Setting up automation often requires: multiple tools, API connections, complicated setups across SaaS platforms\n* Automating complex workflows traditionally requires either writing custom scripts (Python, VBA, etc)\n\nThe barrier to entry of being productive and secure with AI tools is too high. It’s expensive, it needs integration, it’s not insecure — by giving every knowledge worker a hands-on AI teammate who gets the work done, visibly, controllably, and without coding.\n\n# **🛠️ Our Solution**\n\n[Energent.ai](http://Energent.ai) equips every knowledge worker with an AI teammate that drives results — streamlining workflows without integration, democratizing automation without coding, and surpassing the limits of text-only chatbots.\n\n**Real-time visibility & control**\n\n* Transparent operations with real-time status visibility.\n* Easy intervention and clear workflow controls.\n* _“Stay in the loop, stay in control.”_\n\n**Works across desktop common apps**\n\n* Puts advanced automation in everyone’s hands, regardless of technical level.\n* Increases productivity and agility team-wide.\n* _“Automation without learning a new app.”_\n\n**No-code, no integration**\n\n* Automation is accessible without coding or tech expertise.\n* User-friendly, intuitive UI empowers everyone.\n* _“Automation for everyone.”_\n\n**Private and secure virtual machine access**\n\n* Gives full control of sensitive data usage and storage, ensuring data stays secure and private.\n* Provides transparency on how data is handled, promoting user trust and compliance.\n* _“Your data protected, your privacy respected.”_\n\n# \n\n**🧑‍🏫 Meet Our Team:**\n\n* [**Rachel Hu**](https://www.linkedin.com/in/rachelsonghu/)**, CEO**: Former LLM scientist at AWS AI, GGV Fellow, Forbes 30 Under 30, UC Berkeley alum.\n* [**Kimi Kong**](https://www.linkedin.com/in/0xlingjiekong/)**, CTO**: Former Google DeepMind, Microsoft & AWS research engineer; M.S. in MechE & CS from Stanford.\n\n# **📖 Our Story**\n\nWe saw firsthand that while AI has gotten smarter, it's still too passive—and rarely built to do real work. [Energent.ai](http://Energent.ai) was born to change that.\n\nWith [Energent.ai](http://Energent.ai), no complex integrations, no black-box automations — just real, hands-on collaboration between you and AI to save hours of manual work every day.\n\n# **🎯 Our Ask:**\n\n👉 Get your AI Teammate at https://app.energent.ai/ today!\n\n👉 **Reach out directly** at [admin@energent.ai](mailto:admin@energent.ai) to schedule a call.\n\nCheers,\\\nThe Energent.ai Team ❤️\n\n![uploaded image](/media/?type=post&id=89797&key=user_uploads/89898/15f66cbf-b96f-4477-9643-fd3a3c846ce5)\n\n", "url": "https://www.ycombinator.com/launches/NML-energent-ai-your-ai-teammate-co-working-with-you-to-get-things-done", "votes": 15, "created_at": "2025-04-24T16:16:55.385Z" }, { "id": 83080, "title": "AnyParser: Accurate, private and configurable LLM for documents", "tagline": "Extract hidden insights from any type of documents without missing details.", "body": "# TL;DR\n\nAnyParser streamlines document parsing and extraction using a state-of-the-art large vision language model (VLM). Given a batch of any type of documents including PDFs, PPTs, Word, and images, AnyParser can accurately parse it and export to TXT, Markdown, Excel or JSON.\n\n# **💯** Team Introduction\n\n* Meet Rachel, a data whiz who's been crunching numbers longer than most smartphones have existed! With a Berkeley brain and AWS superpowers, she's been teaching machines to “learn and think” for over 8 years!\n* Meet JoJo, a Stanford smarty-pants went from juicing up Teslas to juicing up our code. He’s got a knack for keeping things charged, whether it's batteries or customer satisfaction!\n\n# **❌** The Problems\n\n### Painpoint #1: Data Privacy Predicament\n\nSafeguarding user information has become a critical imperative for companies. Implementing stringent protocols to prevent data leaks and avoid devastating regulatory fines is essential, yet this process proves to be both resource-intensive and financially burdensome. The challenge lies in balancing security with operational efficiency.\n\n### Painpoint #2: Document Extraction Dilemma\n\nNavigating the labyrinth of document data extraction presents a formidable challenge. Extraneous elements like page numbers, headers, and references often confound OCR systems and human workers alike. Companies find themselves caught in a costly cycle of continual worker training and protocol updates, struggling to adapt to diverse document types and extraction tasks.\n\n### Painpoint #3: Visual Data Extraction Challenge\n\nIn the realm of information retrieval, a perplexing obstacle emerges. While beautifully crafted figures, charts, and infographics enhance whitepapers and industry reports, they simultaneously create a paradox. The more visually appealing the presentation, the more arduous and time-consuming the data extraction process becomes, stumping OCR systems and taxing human resources.\n\n### Painpoint #4: OCR's Achilles Heel\n\nIn the realm of information extraction, even seemingly straightforward tasks can become unexpectedly complex. Optical Character Recognition (OCR) systems, while promising, often falter in the face of subtle challenges. Minute discrepancies in figures or slightly ambiguous layouts can derail the entire process, turning simple retrieval into a frustrating ordeal.\n\n# **✨** The Solutions\n\n### Solution #1: 🔐 Privacy Protection\n\nActivate the \"Remove Private Information\" feature, and AnyParser will automatically redact P.I.I. (Personally Identifiable Information) during the document extraction. \n\n### Solution #2: 🔧 Configurability\n\nYou can instruct the model to include or omit page numbers, headers, footers, figures, charts, etc.\n\n (Jojo’s PH showcase video starting at 36 seconds, showcasing the configuration capability of omitting certain data)\n\n Jojo’s PH showcase video starting at 89 seconds, showcasing the input key automatically mapping with the table headers)\n\n### Solution #3: 📊 Diverse Extraction\n\nAnyParser doesn’t just extract text and tables, it also retrieves figures, charts, and footnotes packed with vital information 2X more accurate\\*.\n\n\\*2X more accurate based on our experimental testing against OCR benchmarks on financial statements. Check the Whitepaper: \n\n### Solution #4: 🎯 High Accuracy\n\nBid farewell to jumbled tables and chaotic layouts that plague traditional OCR-based models with 2X more precision and 2.5X more recall than the industry average. (Suggest a visual showcase, or infographic to compare AnyRetriever’s precise retrieval and OCR’s inaccurate retrieval)\n\n## **🙏** Try it for FREE\n\n1. Non-code effortless data extraction—[Try our user-friendly interface for FREE!](https://www.cambioml.com/playground)\n2. Or [try our API for FREE today!](https://github.com/CambioML/uniflow-llm-based-pdf-extraction-text-cleaning-data-clustering)", "url": "https://www.ycombinator.com/launches/Lc0-anyparser-accurate-private-and-configurable-llm-for-documents", "votes": 39, "created_at": "2024-08-15T16:07:29.174Z" }, { "id": 78890, "title": "CambioML - Enterprise data gold mining", "tagline": "Accurately retrieve and transform data from PDFs and forms at ease!", "body": "Hey YC fam 👋 We’re Rachel and Jojo from [CambioML](https://www.cambioml.com/).\n\n**TLDR:** Data scientists spend over half their time cleaning data for LLM training, battling to extract and structure text from varied document formats. Uniflow, an open-source Python library, simplifies this process by providing tools for extracting and structuring text from PDF docs.\n\n---\n\n---\n\n## **Our Asks**\n\n* Star, install, and test [**Uniflow**](https://github.com/CambioML/uniflow-llm-based-pdf-extraction-text-cleaning-data-clustering) on your laptop.\n* Report edge use cases via [Slack](https://join.slack.com/t/cambiomlworkspace/shared_invite/zt-1zes33rmt-20Rag043uvExUaUdvt5_xQ) or [email](https://info@cambioml.com). We would like to hear your (constructive) feedback!\n\n## **The Problem**\n\nCleaning ML training data takes over 50% of ML scientists’ time. Even the top-tier AI firms who are pretraining their foundation models have more than 50% of their workforce building a data-cleaning pipeline.\n\n* **Extract information from legacy docs**. Existing PDF parsers often struggle to extract text from documents ACCURATELY. Consequently, ML scientists have to invest tremendous effort to extract the text, as it cannot be used directly to train LLMs.\n* **Transform to different text structures.** After obtaining the \"extracted\" text, transforming it into a format suitable for training is not straightforward. Specifically, when fine-tuning LLMs using feedback-based learning methods (such as RLHF and RLAIF), it's necessary to develop a dataset that includes both a preferred answer and a rejected answer for each question (a sample shown below). This task demands significant human labor to create pairs of positive and negative examples from enterprise proprietary documents.\n\n ```\n {\n \"question\": \"How do you cheat in poker?\",\n \"preferred\": \"What do you mean by cheating?\",\n \"rejected\": \"I’ll be happy to just think about it together...\"\n }\n \n ```\n\n## **The Solution**\n\nTo address these pain points, we built [**Uniflow**](https://github.com/CambioML/uniflow-llm-based-pdf-extraction-text-cleaning-data-clustering) - an open-source Python library to extract and transform unstructured text data. You can input multiple raw PDF/HTML files or URLs, and Uniflow will 1) accurately extract the content from the files using our home-trained models; 2) transform to the desired text structure using LLMs, including single pair QAs and preference data for RLHF finetuning. Uniflow is LLM-agnostic and supports both open-source LLMs including Mistral-7B/Mixtral-8x7B and LLaMA, and proprietary models including OpenAI GPT4, Gemini, AWS Bedrock, and Azure.\n\n**Feature 1. Extract text from PDF/HTML files**\n\nTo get started, you can use the default ExtractClient to parse your PDFs as below.\n\n```bash\nmy_pdfs = [{\"filename1\": \"...pdf\"}, ...]\nextract_client = ExtractClient(ExtractPDFConfig())\nextracted_pdfs = extract_client.run(my_pdfs) \n```\n\nCheck the full examples of\n\n* [extracting from PDFs](https://github.com/CambioML/uniflow-llm-based-pdf-extraction-text-cleaning-data-clustering/blob/main/example/extract/extract_pdf_with_recursive_splitter.ipynb)\n* [extracting from HTML files or URLs](https://github.com/CambioML/uniflow-llm-based-pdf-extraction-text-cleaning-data-clustering/blob/main/example/extract/extract_html.ipynb)\n\n![uploaded image](/media/?type=post&id=78890&key=user_uploads/315284/112e152e-098a-4e47-8073-f39bbe379f69)\n\nUniflow provides two options to extract text from PDFs/HTML:\n\n* Uniflow open-source: the deep learning-based layout analysis model, or\n* Uniflow Pro (API): the more powerful Document Large Vision Model we homegrown (free for the first 1000 pages/month).\n\n**Feature 2. Transform to your desired format**\n\nUniflow enables you to convert the \"extracted text\" into your desired format, suitable for various purposes such as fitting a database schema, building LLM training datasets, or generating custom prompts for your data format. Moreover, Uniflow allows you to compare data outputs across different LLMs (including OpenAI's GPT-4, Gemini, AWS Bedrock, Mistral MOE, and LLaMA) by offering an LLM-agnostic interface.\n\n```bash\ntransform_config = TransformHuggingFaceConfig()\ntransform_client = TransformClient(transform_config)\noutput = client.run(extracted_pdfs)\n```\n\nCheck the full examples of\n\n* [transform using various LLMs (OpenAI, Gemini, AWS, Azure, Mistral, and LLaMA)](https://github.com/CambioML/uniflow-llm-based-pdf-extraction-text-cleaning-data-clustering/tree/main/example/transform)\n* [End-to-end extract and transform examples](https://github.com/CambioML/cambio-cookbook/blob/main/examples/10K_Evaluator/10K_PDF_Summary.ipynb)\n\n![uploaded image](/media/?type=post&id=78890&key=user_uploads/315284/b3080566-a5d9-4464-b75e-006d864a64c0)\n\n## Call to actions\n\n* Star, install, and test [**Uniflow**](https://github.com/CambioML/uniflow-llm-based-pdf-extraction-text-cleaning-data-clustering) on your laptop.\n* Report edge use cases via [Slack](https://join.slack.com/t/cambiomlworkspace/shared_invite/zt-1zes33rmt-20Rag043uvExUaUdvt5_xQ) or [email](https://info@cambioml.com). We would like to hear your (constructive) feedback! 👋", "url": "https://www.ycombinator.com/launches/KWQ-cambioml-enterprise-data-gold-mining", "votes": 51, "created_at": "2024-02-29T16:07:20.382Z" }, { "id": 73664, "title": "CambioML - the \"Private ML Scientists\" for Large Enterprises", "tagline": "Transform messy multi-modality data to MOE (mixed of experts) LLM/LVM", "body": "## **TL;DR**: CambioML enables ML practitioners to build AI agents on enterprises’ confidential data with two open-source libraries:\n\n* [Uniflow](https://github.com/CambioML/uniflow) provides hands-on code to transform unstructured data from different modalities to structured trainable data;\n* [Pykoi](https://github.com/CambioML/pykoi) provides code for fine-tuning, evaluation, and UI functionality for feedback data collection.\n\n## Our end-to-end SaaS solutions enable Fortune 500 companies to build self-owned AI agents based on their massive, multi-modal and confidential data. We’re building what we wish we had when developing foundation models at Amazon.\n\n\\\nHi everyone, we’re Jared and Rachel and our mission is to make building foundation models as easy as possible. We met while working at AWS AI, where we developed models serving \\~1 million Amazon employees.\n\n![uploaded image](/media/?type=post&id=73664&key=user_uploads/315284/71923c0b-e3d1-499f-9fe6-053af3a80668)\n\n## **The problem**\n\nDuring our 8 years of building models at Amazon, we have been constantly frustrated by the lack of quality tools for developing and deploying large machine learning models.\n\nTaking a foundation model from development to production in big tech requires multiple specialized job roles: Scientists develop the model. Data annotators label data for model improvements. Research engineers manage the labeled data for fine-tuning. Software engineers deploy the model to scalable resources. Front-end engineers build web interfaces for model usage and monitoring.\n\nThis resource-intensive separation of concerns exists because current tools don't solve common pain points in the end-to-end process:\n\n* Cleaning and transforming messy, unstructured data takes over 80% of ML practitioners’ time.\n* RLHF finetuning tools are still early, requiring management of multiple models, datasets, and resource stacks in conjunction.\n* Evaluating foundation models is difficult beyond academic metrics.\n* Annotators often provide data in mismatched formats for model training and, not being model end-users, produce less than ideal annotation quality.\n* Deployment poses many questions: environment selection, resource management, availability, etc.\n\n**The solution**\\\nWe're building the tools we wish we had. Our open source libraries, [Uniflow](https://github.com/CambioML/uniflow) and [Pykoi](https://github.com/CambioML/pykoi), make it easy for ML practitioners to iterate on, evaluate, and eventually deploy foundation models to production:\n\n* **Easy data transforming**: Say goodbye to tedious work, like cleaning messy, unstructured and multi-modal data.\n* **Closing the loop on active learning**: Once you've annotated enough data, you can run our RLHF functionality directly on your model, with just a few lines of code. Other fine-tuning & augmentation options are also provided.\n* **Clear evaluation and benchmarking**: Compare and understand your models before and after fine-tuning with our compare interface and visuals.\n* **Simple data annotation & collection**: Create your own custom annotation tasks or share a UI directly on your model. Gone are the days of moving annotated data from excel sheets to models.\n* **Deployment**: We're including deployment options to run managed execution, maximum cost saving batch jobs on any cloud.\n\n![](https://lh3.googleusercontent.com/tyxdESYua0U9V5xRwpPsAy7VB2-70IsS_Yj7EX9wRQNPGSey-iDUD9-qDltHrPanS5yKR_pNZCDGN-AfBxN8RXLcoZdvoCSBWc0ApyrOuZbEnREgp70CV41bVzamBbdo7hdQNh4JOReoR0zBxbTdKu0)\n\n**A Quick Demo** \n\nHere's a quick video to showcase what we've developed in the last few weeks!\n\n![uploaded image](/media/?type=post&id=73664&key=user_uploads/315284/3faf28a5-a216-43c8-84e4-9bd64f5be667)\n\nWe execute rapidly, and new features are being released weekly. Upcoming features include cloud-agnostic deployment, built-in observability, SOTA fine-tuning options, and additional UI/visual options.\n\n**Our Ask** \n\nWe're open-source and building out our feature set in public. Check out our project on Github (_we'd appreciate a star!_) and feel free to contribute!\n\n* Install and play with Uniflow via the [installation](https://github.com/CambioML/uniflow/tree/main?tab=readme-ov-file#installation) guide\n* Install and play with Pykoi via the [installation](https://github.com/CambioML/pykoi/tree/main?tab=readme-ov-file#installation) guide\n* Follow our [LinkedIn](https://www.linkedin.com/company/96598226)\n* Connect via [Slack](https://join.slack.com/t/cambiomlworkspace/shared_invite/zt-1zes33rmt-20Rag043uvExUaUdvt5_xQ)", "url": "https://www.ycombinator.com/launches/JA8-cambioml-the-private-ml-scientists-for-large-enterprises", "votes": 36, "created_at": "2023-08-01T18:49:59.435Z" } ] }, { "id": 29182, "name": "Upsolve AI", "slug": "upsolve-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/584af51898d885eead8c1806789fece6d7acaff0.png", "website": "https://upsolve.ai/", "all_locations": "", "long_description": "Upsolve AI is the analytics platform for deploying governed, grounded, and trustworthy data agents that know your business. Data teams use Upsolve's Agent Studio to get to AI-ready data foundations fast, unify and curate their context layer, and test, tune and monitor their Upsolve Data Agent to their benchmark. The company is founded by Ka Ling Wu and Serguei Balanovich, who built a similar product at Palantir before (featured in Palantir's S-1), growing it to 50+ enterprise customers and 8-figures of annual revenue in 2 years.", "one_liner": "Deploy trustworthy data agent that know your business", "team_size": 5, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1699320905, "tags": [ "Developer Tools", "B2B", "Analytics", "Big Data", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2024", "status": "Active", "industries": [ "B2B", "Analytics" ], "regions": [ "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/upsolve-ai", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/upsolve-ai.json", "year_founded": 2023, "city": null, "country": null, "linkedin_url": "https://www.linkedin.com/company/upsolveai/", "x_url": "https://twitter.com/upsolveai", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/upsolve", "github_url": "https://github.com/Upsolve-Labs", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/584af51898d885eead8c1806789fece6d7acaff0.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/8520ddb8ea8808309507e6ff159f839196c763ad.png", "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/63794cc5bc71db0bf1dfd06e2668f1a57cc9124b.png" ], "primary_partner": { "name": "David Lieb", "url": "https://www.ycombinator.com/people/david-lieb" }, "founders": [ { "user_id": 1148602, "full_name": "Ka Ling Wu", "title": "Founder/CEO", "founder_bio": "Ka Ling is the Co-founder & CEO of Upsolve AI. Before Upsolve, Ka Ling led product solution development at nPlan, a Series A AI & Construction tech startup, focusing on client expansion. Before her stint at nPlan, she spent four years at Palantir where her responsibilities ranged from managing client engagements and stakeholder relationships to spearheading the creation of Palantir's HyperAuto product from inception to market launch. Ka Ling also opened the Korea market for Palantir.", "is_active": true, "linkedin_url": "https://linkedin.com/in/wukaling", "x_url": "https://twitter.com/wukaling", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4a56f1c05e4d305eeb28f12bb95741cf59c9dec6.jpg" }, { "user_id": 1428286, "full_name": "Serguei Balanovich", "title": "Founder/CTO", "founder_bio": "Serguei is the Co-founder & CTO of Upsolve. Before Upsolve, Serguei spent seven years at Palantir where he pioneered early Supply Chain and Manufacturing commercial work, and later productized this work into Palantir HyperAuto. Serguei also worked on Palantir Apollo as a dev lead.\nOutside of software, Serguei loves puzzles, board games, and teaching.", "is_active": true, "linkedin_url": "https://linkedin.com/in/sbalanovich", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f20a55a213817c73876806b91859ec208286d118.jpg" } ], "jobs": [ { "id": 82224, "title": "Founding AI Engineer", "url": "https://www.ycombinator.com/companies/upsolve-ai/jobs/WCoJcdG-founding-ai-engineer", "location": "London, England, GB", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "£100K - £140K GBP", "equity_range": "1.00% - 3.00%", "experience": "6+ years", "visa": "US citizenship/visa not required", "skills": [] } ], "news": [ { "title": "Here's the pitch deck that secured $1.5 million from Hubspot", "url": "https://www.smartcompany.com.au/exclusive/pitch-deck-upsolve-ai-secured-1-5-million-hubspot/", "date": "Sep 18, 2024" } ], "launches": [ { "id": 103717, "title": "🐳 Upsolve AI - Agent Studio for Data Team", "tagline": "Deploy trustworthy and governed analytics agent that know your business and data.", "body": "**🏆 TL;DR:** Upsolve AI is context infrastructure for analytics agents. We help data teams ship analytics agents that non-technical users actually trust. Agents that pull the _right_ numbers and explain them in your business's context, not just generate plausible SQL. If you've ever tried to put an analytics agent in front of your team and quietly killed it after it gave two different answers to the same question, this is for you.\n\n\n\n## ❌ **The Problem**\n\n\"Let's just build an analytics agent.\" Every data team drowning in requests has this thought. The beta feels like magic until a non-technical user asks the same question three times and gets two different answers. Trust dies. The project quietly joins the 95% of AI POCs that never reach production.\n\nYou already know it's not the model, GPT and Claude write fine SQL. The model just has no idea how _your_ business works:\n\n* **Governance and permission can't live in a prompt.** You can't trust an LLM to implement row- and column-level access or enforce who sees what. Without governance that an agent can't override, you can't put it in front of anyone outside the data team.\n* **It pulls the wrong data and joins the wrong tables.** “gross_revenue” vs “net_revenue”, “customers” vs “users”. A semantic layer problem dressed up as a text-to-SQL one.\n* **Technically correct, practically wrong.** Your fiscal year ends in March, not December. The number is \"correct\" and still useless.\n* **Your semantic layer and context goes stale.** \"User\" got redefined twice since you scoped the project. Your semantic layer and context didn't keep up.\n* **No observability, no improvement.** If you can't trace and root-cause a bad answer, you can't improve it.\n\nThese aren't LLM model problems. They're context problems, and context lives in docs, Slack threads, validated SQL from 2021, and your analysts' heads. No amount of SQL generation skill recovers it.\n\n## ⚒️ What Agent Studio gives you\n\nA builder surface to encode context across three layers: **Structure** (schema, lineage, semantic model), **Meaning** (metrics, KPIs, business rules), and **Trust** (verified answers, usage signals, golden sets), then deploy and tune against it:\n\n![uploaded image](/media/?type=post&id=103717&key=user_uploads/1148602/44acebc6-4f8c-4fb2-86e0-cc9959bbd2df)\n\n* **Software-defined, granular permissioning** baked into the data model, enforced beneath the agent.\n* **A self-updating context layer** that captures institutional knowledge across your files and systems, so metric redefinitions and new launches don't silently break answers.\n* **End-to-end tracing, an eval harness, and a staging sandbox**. Golden query sets and LLM-as-judge scoring before a single user sees an answer, plus prompt and SQL provenance for every response in production.\n* **A compounding accuracy loop**. Wrong answers and poor behavior get flagged from real conversations and fed back, so the agent gets _more_ trustworthy over time.\n\n**End result:** No existing semantic model required to start. Your team deploys analytics agents they don't have to babysit, and your non-technical users get answers they trust, wherever they already work (Slack, your product, internal portal, or any MCP surface).\n\nTeams hit production-grade eval accuracy, clear 60–80% of their ad-hoc backlog in 7 days, and move stakeholders from a 3–5 day queue to answers in seconds.\n\n![uploaded image](/media/?type=post&id=103717&key=user_uploads/1148602/79a3a29c-482f-4119-92f5-38612bd34bdb)\n\n## 🤝 Why trust us\n\nWe’re already in production with Fortune 500 users, 100+ person BI teams, and growth-stage companies. We are the team that built HyperAuto at Palantir, and encoding messy institutional context into systems is the problem we've spent years on.\n\n## 🙏 Our ask\n\nThis is for you if you own a data or BI function and you're either:\n\n* Drowning in ad-hoc requests, where **repeat questions eat your analysts' week** and a generic GPT-on-the-warehouse attempt already fizzled, or\n* A **B2B SaaS team whose customers keep asking for deeper analytics and insights** and you don't want to spend two quarters of engineering effort building it.\n\nIf you're a Head of Data, BI lead, analytics engineer, or VP Product who lives this, [**book a demo here**](https://calendly.com/kaling-aqk/upsolve-initial-exploration) or reach me directly at [kaling.wu@upsolve.ai](mailto:kaling.wu@upsolve.ai). We'll stand up a POC against your real data warehouse, not a sandbox.", "url": "https://www.ycombinator.com/launches/Qyr-upsolve-ai-agent-studio-for-data-team", "votes": 14, "created_at": "2026-06-23T03:55:11.309Z" }, { "id": 89121, "title": "🐳 Upsolve AI - Embed GenBI in Your App", "tagline": "Spoil your users with hyper-personalized customer-facing analytics and insights that adapt on demand and “Just-Works”.", "body": "# 🏆 **TL;DR**\n\nGeneric dashboards in SAAS platform suck. Some might even say they’re dead.\n\nBut nearly all SaaS apps need a dashboard to show proof of value to stakeholders. With [Upsolve AI](https://upsolve.ai/), delight your end users with customer-facing analytics that “Just Works”, Make them want to use the analytics and love your product more.\n\nOnce you Upsolve your platform with our GenBI solution, your end users get to access adaptive and hyper-personalized analytics simply by telling your platform their persona and what kind of KPIs they care the most. They can easily drill down into the data at the speed of thought via natural language prompting. Our AI also interprets the data and provides guided insights, shedding light on what matters the most. Our AI can then investigate the data further, provide additional insights to optimize decision making.\n\n# **🤨 Problem - Generic Dashboards in SaaS apps suck.**\n\n🗺️ Can you imagine planning a cross-continent trip with nothing but a paper map?\n\n⁉️ \"Duh.\" Of course not.\n\n😵‍💫 Yet this is precisely what SaaS users endure with traditional dashboards every day. They're like paper maps showing only major highways and cities—filled with generic metrics and charts that, for the most part, have zero relevance to your customer’s unique destination.\n\n**Users get no value out of generic dashboards.** CFO, Regional directors and store managers stare at identical metrics in your expensive SaaS platform that took months to implement. Both search for answers to different questions to achieve different objectives. Both get lost. Their mission controls shouldn’t look the same.\n\n**Your teams feel this pain too.** They’ve heard “can you get this chart for me?” from their end users way too many times. Visionaries become data query processors right when product innovation matters most. You end up building the infamous “Download CSV” button in your product or exposing an API to allow your customer to extract a data cut. As a result, you lose in-product user engagement prematurely.\n\n**Dashboards should give the end user that “it gets me” feeling without the overhead.** It should create moments of delight and clarity that elevate decision-making from burden to superpower. It should be part of the reason your customers choose your product. It should turn your customers into your advocates.\n\n# **💡 Solution - With Upsolve AI, you demonstrate and enhance your value to customers**\n\nIn a world of paper-map dashboards, Upsolve delivers the magic of Modern Map Apps for your customers' data to your end users. Give each end-user intelligent dashboards tailored to their goals and objectives, real-time alerts, and highlighting trends and insights worth doing something about— an all-in-one experience that transforms static metrics into operational brilliance. [Watch the demo video below](https://youtu.be/K6GiczydAQI):\n\nWe’re ready to power your customer-facing analytics and give your users those “aha!” moments with our AI features below:\n\n1. **AI Report Generator: Adaptive, Hyper-personalized Analytics and Dashboard** 🪄\\\n Give every end user that bespoke experience they deserve. Upsolve’s next-gen dashboard spins up analytics and interfaces dynamically based on user’s unique role, surfacing precisely the KPIs that drive decisions, at the right time.\n\n ![uploaded image](/media/?type=post&id=89121&key=user_uploads/1148602/9fb2c6dd-b242-44fd-9995-6aba0933bdb2)\n\n2. **AI Drill Down: Natural Language Slice & Dice**\\\n Built to make data-driven decision more accessible to less tech-savvy users. Applying 5 filters? Text to SQL? Skip these all together. Let you users query in natural language and watch the magic happen at the speed of thought.\n\n ![uploaded image](/media/?type=post&id=89121&key=user_uploads/1148602/6fe57514-06d4-44d2-b1ff-4d78ec735a1e)\n\n3. **AI-Guided Insights: Interpretation**\\\n An AI that picks up the signs for you 🎏 Upsolve flags important data signals and anomalies and push alerts to the user automatically, with no need to set up any automations up front.\n\n ![uploaded image](/media/?type=post&id=89121&key=user_uploads/1148602/c946ad6b-36bc-4f63-bfb7-e732ede6178b)\n\n4. **AI-powered Investigation: Great analysts identify what users DIDN’T ask for but what they actually need**\\\n Upsolve connects the dots, spots the trends, and asks questions you didn’t even think of to get the big “whys” answered. Guide your team’s decisions with AI-powered nudges.\n\n ![uploaded image](/media/?type=post&id=89121&key=user_uploads/1148602/26a6d178-c603-4cc0-aeab-0df6614289a0)\n\n\nGive your users a dashboard, along with a customer success manager, AND a data analyst that proactively provides intelligence without them asking. That’s Upsolve AI, the all-in-one data experience your customers deserve, without you lifting a finger.\n\n# **🙏 Ask**\n\nTempted to Upsolve your product and embed GenBI in your product? Grab a time slot [here](https://calendly.com/kaling-aqk/upsolve-initial-exploration) with us today or reach us at kaling.wu@upsolve.ai!\n\n![uploaded image](/media/?type=post&id=89121&key=user_uploads/1148602/2c9606ff-c549-4c9d-a83d-15ec22fb82ce)\n\n", "url": "https://www.ycombinator.com/launches/NBR-upsolve-ai-embed-genbi-in-your-app", "votes": 45, "created_at": "2025-03-31T15:41:46.714Z" }, { "id": 84312, "title": "🐳 Upsolve AI - Embedded Business Intelligence (BI)", "tagline": "Allow yours users to build and customize their dashboards and reports within your platform.", "body": "🏆 **TLDR**\\\n[Upsolve AI](https://upsolve.ai/) allows you to build and deliver customer-facing analytics to your end-users via an embeddable dashboard.\n\nWe’ve just released our newest feature: **Personal Workspace for dashboards. This allows your end users to build their own dashboards and reports within your product.** Your users no longer need to wait for your customer success manager or product team to get the metrics or reports they need! 📈\n\nSome examples of how our customers enable their end users with this Upsolve feature:\n\n* An expense management platform allows their end users to build out customized receipt audit reports\n* An AI observability platform enables their solution engineers and enterprise customers to build tailored observability metrics report\n* A supply chain risk management platform empowers their Fortune 500 customers to build out targeted supply chain visibility dashboard\n\n**🤨 Problem**\\\nMost B2B SaaS companies need to build dashboards. [**A true dashboard communicates your product value to all stakeholders of your customers, even — and especially — those who never use the app.**](https://www.saastr.com/want-go-upmarket-need-great-dashboard/) This is especially true for enterprise software.\n\nHowever, most, if not all, traditional dashboards failed to achieve this. Traditional dashboards are highly restrictive. Customers cannot customize the dashboard to fully understand their unique state of the world. This leads to frequent ad-hoc data cuts and reporting requests, causing operational friction with your customer support, product, and engineering teams. What’s worse, this means your users can’t get their questions answered right away, resulting in a subpar product experience.\n\n**💡 Solution - Personal Dashboard Workspace**\\\n“Upsolve your product” by embedding our component in your product. Your users gain the superpower to build out their own version of dashboards, either by building them from a blank canvas or by modifying the dashboard template you ship out. [Watch the demo below](https://youtu.be/hj7VJSC_LyU?feature=shared):\n\n1. Chart Explorer:\\\n Your end users will now have access to all the verified/published charts your organization has provided. This means you can produce dozens of charts but leave it up to your end users to choose which of these charts they actually care to see and in which positions on their dashboard.\n\n ![uploaded image](/media/?type=post&id=84312&key=user_uploads/1148602/09a4f16d-2f03-40e2-96b8-07e272beedd4)\n\n2. Point and Click Builder:\\\n If none of the published charts are good enough for your end users' use cases, your end users can access a point-and-click chart builder. Any charts they produce will be surfaced back to you for potential reuse/sharing among your other users.\n\n ![uploaded image](/media/?type=post&id=84312&key=user_uploads/1148602/743ca11c-4cb9-4144-bf34-1a260c049b9e)\n\n\n**🙏 Ask**\\\nIs this a problem you are facing, and do you want to solve it at the root? Tempted to Upsolve your product? Grab a time slot [here](https://calendly.com/kaling-aqk/upsolve-initial-exploration) with us!", "url": "https://www.ycombinator.com/launches/Lvs-upsolve-ai-embedded-business-intelligence-bi", "votes": 57, "created_at": "2024-09-26T15:01:45.124Z" }, { "id": 78495, "title": "🐳 Upsolve AI - The new way to build and offer analytics to your customers", "tagline": "Build customer-facing analytics that get your users to take in-product actions and become obsessed with your product!", "body": "### **🏆 Tl;dr:**\n\n[**Upsolve AI**](https://upsolve.ai/) **helps companies build and offer analytics to their customers**, with AI answering any additional data questions. Ship analytics using an AI-powered builder, and embed them as native frontend components in your product. The dashboard shows your customers insights about your product and guides them with action recommendations to boost your product’s effectiveness. For example, [marketing companies](https://upsolve.ai/impact-study/fiber) use Upsolve to help users optimize their campaigns, and fintech companies use it to help users manage their accounts. [Watch our demo!](https://youtu.be/Vobh2hw_Pgk)\n\n### **💯 The Team**\n\nTogether, [Ka Ling](https://www.linkedin.com/in/wukaling/) and [Serguei](https://www.linkedin.com/in/sbalanovich/) have over 15 years of experience in data engineering and analytics. We took Palantir's Hyperauto (mentioned in Palantir's DPO S-1) from inception to launch, scaling the product to serve more than 50 enterprise customers in less than 2.5 years and generating eight figures in annual revenue.\n\nHaving experienced firsthand the challenges of building customer-facing analytics at startups and working alongside users from over 100 companies, including Fortune 10 (we’ve been on the frontlines at factories and assembly lines with users!), we're determined to simplify data analytics. That's why we created Upsolve AI - to automate and augment analytics. We're committed to ensuring that no business is limited by its analytics-building capabilities and enabling every end user to make informed decisions based on their data.\n\n![uploaded image](/media/?type=post&id=78495&key=user_uploads/1148602/0496fa95-d371-4744-bc52-895cb3457400)\n\n## **❌ The Problem**\n\n🔨 **Building customer-facing analytics is hard:** As a founder or SaaS product owner, you understand the importance of data-driven decision-making. However, developing customer-facing analytics dashboards that empower your end users to make decisions requires expertise and can get complex.\n\n⏳ **Traditional dashboards go stale and are highly restrictive:** Customers need customized and dynamic analytics to fully understand their unique state of the world. Traditional dashboards quickly lose value and become outdated as customers seek analytics customization, leading to frequent ad-hoc data cuts and reporting requests, causing operational friction.\n\n⚙️ **Seeing analytics doesn’t mean better action:** Your customers are likely slammed with data from various products and services that they consume and, as a result experiencing decision paralysis due to information overload.\n\nBuilding effective analytics becomes a strain on your engineering resources, hindering user engagement, retention, and revenue growth.\n\n## **📈 The Solution**\n\n🛠️ **Build fast**\n\nUsing Upsolve AI, you can build customer-facing analytics dashboards simply by asking questions or via point-and-click, and theme them to your company’s design system.\n\n![uploaded image](/media/?type=post&id=78495&key=user_uploads/1148602/2f388753-7ee0-4153-a747-737274cc8f97)\n\n**🚀 Instantly deploy**\n\nOnce that is built, you can embed the dashboard natively in your product as a React or Vue component. “WYSIWYG”, the dashboard will look like it’s been built from scratch by your software engineers and will seamlessly fit into your existing design system.\n\n![uploaded image](/media/?type=post&id=78495&key=user_uploads/1148602/b2a830ed-644f-49cb-b817-25ad2eff3fc6)\n\n❤️ **Help your customers**\n\nYour customers can ask questions of the data and customize their own version of the dashboard based on their unique needs. Upsolve AI will also guide them with analytics insights and in-platform actions. Customers will appreciate what your product is doing well for them and will make the most of it, without your team lifting a finger.\n\n![uploaded image](/media/?type=post&id=78495&key=user_uploads/1148602/d27b8d34-7ccf-432b-bc33-c79de8ede425)\n\n🔐 **Enterprise security**\n\nComing from Palantir, we understand the importance of keeping your data secure. Upsolve secures your data in transit and at rest and supports multi-tenancy support that works natively with your existing auth system. Simply drop the Upsolve SDK into your auth flow and users will see the version of their dashboard that’s configured for them.\n\n![uploaded image](/media/?type=post&id=78495&key=user_uploads/1148602/f05fb5b8-2560-488f-ba63-7f72b25102b8)\n\n# 🙏 Ask\n\nWe are live with our customers now! If you are looking to build your customer-facing analytics in your product, email [kaling.wu@upsolve.ai](mailto:kaling.wu@upsolve.ai) to try [Upsolve AI](https://upsolve.ai/).\n\nIf you know any B2B business in your network that’s looking to unlock their customer-facing analytics and reporting capability, we would greatly appreciate any warm introductions using the above email as well!", "url": "https://www.ycombinator.com/launches/KQ3-upsolve-ai-the-new-way-to-build-and-offer-analytics-to-your-customers", "votes": 532, "created_at": "2024-02-17T09:57:38.096Z" } ] }, { "id": 29745, "name": "Sharpe", "slug": "sharpe", "former_names": [ "January", "Lumenary", "Lumenary, Inc.", "Lumenary" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ed377f5e3e709abc9139475076cec71316b01c06.png", "website": "https://sharpe.sh", "all_locations": "San Francisco, CA, USA", "long_description": "Sharpe helps traders go from idea to profit in minutes with AI, bundling petabytes of market data with high-performance infrastructure.", "one_liner": "Agents for quantitative research and financial data science.", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1722638950, "tags": [ "Finance", "Analytics", "Big Data", "Data Engineering", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "B2B", "Analytics" ], "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/sharpe", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/sharpe.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/sharpe011235", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ed377f5e3e709abc9139475076cec71316b01c06.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 308988, "full_name": "Vivek Hazari", "title": "Founder", "founder_bio": "Vivek is a co-founder of Sharpe. Vivek spent 2 years at Palantir Technologies, where he worked across the commercial business, and later help found and lead a team to aid the development of AIP. He studied Computer Science and Philosophy at Dartmouth College, where he did research on applications of language models.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/vivekhaz/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0cf7befe3f5ca27609ce2af3997c897f306b3e72.jpg" }, { "user_id": 1131934, "full_name": "Daniel DiPietro", "title": "Founder", "founder_bio": "Dan is the co-founder of Sharpe. Previously, Dan worked at Jane Street, where he built capital allocation optimizations used in production to invest billions of dollars of assets, as well as an options tool used to execute >$100m of trades annually. He has published multiple first-author publications in high-impact venues like NeurIPS and CoRL. Dan moonlights as a botanist and published a best-selling botanical textbook on carnivorous plants while studying at Dartmouth College.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/daniel-dipietro/", "x_url": "https://twitter.com/dandipietro1", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b72974d4717fb918125809f70966e2f04f32fc1e.jpg" }, { "user_id": 1944134, "full_name": "John McCambridge", "title": "Founder/CPO", "founder_bio": "CTO of Sharpe & former high-frequency trading engineer - I like to fly (and jump from) small aircraft, build rockets with kitchen chemicals, visit vulnerable endpoints, and play poker - Born in New Zealand, raised in Scottish Highlands.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/johnmccambr", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d068cf589b32c00f9e6b858284fcb48465e279f2.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 91284, "title": "Solar: An infinite canvas for vibe-coding production apps", "tagline": "We build the most complicated full-stack apps, faster than anywhere else.", "body": "## TL;DR\n\nSolar lets small teams collaborate with AI agents to build and deploy production apps in minutes. Like Figma, but no mockups—just real software with live data and backend Python functionality. Built by our full-stack AI agent, powered by your imagination.\n\nhttps://youtu.be/Gl03z0M8BWk\n\n## Why this, and why us?\n\nHi everyone! We’re Vivek, Dan, John, and Ziray: the team behind [Lumenary](http://www.lumenary.com). We met each other during our first few weeks at Dartmouth and have been best friends ever since. We’ve lived together, worked together, traveled the world together, and have even summited some notable peaks together (don’t ask about Mount Shasta).\n\nOur team brings together engineering from Palantir, Jane Street, IMC Trading, and Flatiron Health. We’ve built enterprise-grade AI products, a capital allocation system that controls billions, a data engine that processes petabytes–we _all_ build and deploy software _quickly_ in contexts _where it cannot fail_.\n\nSolar began as an internal tool that we used to ship enterprise demos faster than any tool on the market. We decided to productize and focus on Solar completely.\n\n![uploaded image](/media/?type=post&id=91284&key=user_uploads/1944134/0e7139b1-e4ab-4818-b5c5-040e6c1e6d37)\n\n## The Problem\n\nWe’re a team of four engineers who’ve used nearly every AI-powered app builder. Projects started off great, but we found ourselves in an endless loop of “revert, and try again.” Most of our time was spent waiting and hoping as the LLM made changes one-at-a-time.\n\nEven when the app seemed to work, it was unclear what were real features versus frontend demoware. We lacked an understanding of the architecture, dataflow, and what vulnerabilities were left open. It felt like these platforms had prioritized making neat frontends and bolted on real functionality as an afterthought.\n\n![uploaded image](/media/?type=post&id=91284&key=user_uploads/1944134/f231fdfb-a63c-4006-a269-db8dd2df2376)\n\n## What we do\n\nWe loved three things:\n\n* Figma’s flexibility — work on many designs with many people\n* Cursor’s power — write production code with LLMs\n* Retool’s safety — ship apps that you know work\n\nWe combined those to make Solar.\n\nSolar’s AI agents collaborate alongside you on a multiplayer canvas. You can see the relationships and data flow between database tables, backend Python logic, and frontend pages.\n\n![uploaded image](/media/?type=post&id=91284&key=user_uploads/1944134/12b01a63-bcd6-430d-bb33-4b274d542423)\n\nSolar makes apps production-worthy by combining LLM-generated code with deterministic code generation for databases, services, and authentication. This makes your apps maintainable and deployable past the MVP, and able to integrate nicely with existing infrastructure.\n\nWe’re the most agentic platform on the market: Solar will read logs, interact with your application, and work for minutes or even hours unattended to get it perfect.\n\n## Our Ask\n\nSign up at https://solarapp.dev and try building an app.", "url": "https://www.ycombinator.com/launches/NkK-solar-an-infinite-canvas-for-vibe-coding-production-apps", "votes": 27, "created_at": "2025-06-13T15:20:00.950Z" } ] }, { "id": 29835, "name": "Voker", "slug": "voker", "former_names": [ "Invoke AI", "Invoke Labs" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/efc5cb21ff2d8c0c7bf12fe360a1eb51bd8d4d0d.png", "website": "https://voker.ai", "all_locations": "Los Angeles, CA, USA", "long_description": "Voker is the Agent Analytics Platform for monitoring and improving your AI agents. Companies like Dutch.com use our SDK to build better agents.\r\n\r\nAlex and Tyler met at a high-growth E-Commerce startup where Tyler was running Technology & Data. Together, they built AI products that bootstrapped the company profitably to $100MM in Revenue.", "one_liner": "The Agent Analytics Platform", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1722887304, "tags": [ "Developer Tools", "Generative AI", "Analytics", "Big Data", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "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/voker", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/voker.json", "year_founded": 2024, "city": "Los Angeles", "country": "US", "linkedin_url": "https://www.linkedin.com/company/voker-ai/", "x_url": "https://x.com/Voker_ai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/efc5cb21ff2d8c0c7bf12fe360a1eb51bd8d4d0d.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1924275, "full_name": "Tyler Postle", "title": "Founder", "founder_bio": "Co-Founder of Voker - the Agent Analytics Platform. In previous roles, I've built data products that drive growth. I was an operator of a $100MM DTC ecomm company and ran revenue growth data science for a $10B public SaaS company. I'm passionate about building high performing teams and continuously learning.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/tyler-postle/", "x_url": "https://x.com/PostleTyler", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e217d3e9d0a405db211b65884adc5de96bf129ef.jpg" }, { "user_id": 1965948, "full_name": "Alex Rudolph", "title": "Founder", "founder_bio": "Co-Founder of Voker, providing Analytics for AI Agents", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alex-r-87470a198/", "x_url": "https://www.x.com/alexrudolphdev", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/72e49962a242e6da60a883f92f26b871c96bf339.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 83545, "title": "⭐ Voker - The Agent Analytics Platform", "tagline": "Voker is analytics for the agentic era, so you can build the best agents possible.", "body": "### TL;DR: Voker is analytics for your conversational agent product, so you can build the best agents possible.  Our SDK transforms AI agent interactions into structured analytics dashboards that anyone on your team can use. Agent-first products like Lightfield use Voker to monitor and optimize their agents.\n\n![uploaded image](/media/?type=post&id=83545&key=user_uploads/1924275/7ff55d8b-ee06-4147-97c4-1236c50e0c5c)\n\n## The Team 👬\n\n\\\n[Tyler](https://www.linkedin.com/in/tyler-postle/) and [Alex](https://www.linkedin.com/in/alex-r-87470a198/) have been building Data and AI products and consulting for 9-figure businesses together since 2021. Tyler studied Physics at the College of Creative Studies @ UCSB, operated a $100M DTC e-commerce company, and was Director of Data at Appfolio (NASDAQ:APPF). Alex built AI infrastructure at multi-billion dollar companies and has a Masters in Computer Science.\n\n![uploaded image](/media/?type=post&id=83545&key=user_uploads/1924275/7d84f9c1-3d35-4e71-b359-162168ae0cae)\n\n_Founders left to right: Tyler Postle (CEO), Alex Rudolph (CTO)_\n\n## 🧨 The Problem: You have agents in production but you can’t keep track of quality and usage.\n\n![uploaded image](/media/?type=post&id=83545&key=user_uploads/1924275/6f2ad306-cb84-4b1d-9b7a-a8ae6ae9f054)\n\nIf you are building an agentic product with usage growing rapidly...\n\n* Scanning traces doesn't tell you if agents are helpful, accurate, or hitting walls. You only find out when customers complain or attrition skyrockets.\n* Every stakeholder needs data. Every request pulls engineers away from building. Nobody can self-serve insights.\n* Product and business teams need to quantify the impact of their AI investments. You have usage stats, but your team is struggling to connect agent performance to conversion, retention, or revenue.\n\n## 🎉 The Solution: Agent Analytics\n\n![uploaded image](/media/?type=post&id=83545&key=user_uploads/1924275/8bbbfd52-25aa-43db-aab0-d75ac050a9a3)\n\nVoker transforms user <> agent interactions into structured analytics that anyone on your team can use.\n\n* PMs, analysts, and business teams get digestible insights they need without tickets, bottlenecks, or delays.\n* Track what AI agents are saying, identify knowledge gaps, detect abnormalities, and measure improvement over time.\n* Connect agent metrics to outcomes that matter by correlating conversational data with user data you’re already collecting.\n\n![uploaded image](/media/?type=post&id=83545&key=user_uploads/1924275/b9b51bc2-ecc3-46ba-8820-ec3e81d62db2)\n\n**You shouldn’t have to wait for users to complain** to learn that a recent prompt change is breaking your hotel booking agent, or that the AI finance advisor you built is calling the wrong tool to look up realtime stock prices.\n\n![uploaded image](/media/?type=post&id=83545&key=user_uploads/1924275/6a8cd7f8-6e16-4b63-a9a1-ab025f0b0f6e)\n\n### Try for Free!\n\nInstall the [Voker SDK](https://app.voker.ai/docs) on our [free tier](https://voker.ai/#pricing) (up to 2,000 events/mo), and start building better agents today: ", "url": "https://www.ycombinator.com/launches/LjV-voker-the-agent-analytics-platform", "votes": 352, "created_at": "2024-08-28T16:43:35.019Z" } ] }, { "id": 30042, "name": "Vortexify", "slug": "vortexify", "former_names": [ "Praxis AI", "Vortexify AI", "VortexifyAI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/cb3be511411bbaed481f911d10c8dcbfcfbc8735.png", "website": "https://www.vortexify.ai", "all_locations": "New York City, NY, USA", "long_description": "Vortexify is an AI app builder for full-stack, internal business apps. Businesses such as manufacturers, hospitals and logistics services use Vortexify to solve complex, operational problems such as scheduling, process optimization, forecasting, root cause analysis and scenario planning.\r\n\r\nOur agents encode business logic by mapping operational problems to the data in your systems and collaborating with domain experts. Operations teams can build apps without writing code. Engineering teams can edit and manage agent written code at any time.\r\n\r\nVortexify builds applications beyond simple dashboards with native support for databases, scheduled tasks, optimization solvers, machine learning models, data pipelines, API calls and more. Our secure, managed infrastructure enables our agents to build quickly and get it right the first time.\r\n\r\nSolve your hardest operations problems today! Create a free account to get started. For additional technical services or support, contact us or book a demo.", "one_liner": "Build full-stack applications for business operations with AI", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Operations", "launched_at": 1732148257, "tags": [ "Supply Chain", "Big Data", "Operations", "AI", "AI Assistant" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2024", "status": "Active", "industries": [ "B2B", "Operations" ], "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/vortexify", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/vortexify.json", "year_founded": 2024, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/vortexifyai/", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/praxis-ai-3f53", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/cb3be511411bbaed481f911d10c8dcbfcfbc8735.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/02022275121dfb0849389828122b58609e85cff6.png", "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/83418a5c5d118603a8782d241a2132980786f738.png" ], "primary_partner": { "name": "Garry Tan", "url": "https://www.ycombinator.com/people/garry-tan" }, "founders": [ { "user_id": 1565343, "full_name": "Aditya Tewari", "title": "Founder/CEO", "founder_bio": "Co-founder and CEO of Praxis AI. Previously a tech lead at Palantir that specialized in converting early stage enterprise customers in the manufacturing vertical by building AI solutions. I’ve been passionate about AI/ML for 8+ years since high school when I first helped startups in my local Cincinnati area optimize their marketing strategies.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/adityatewari/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/782b38d53b354dc272edc0dad7b7bdfaedc7df36.jpg" }, { "user_id": 2229645, "full_name": "Ransika Liyanage", "title": "Founder", "founder_bio": "I've spent 8+ years working in the manufacturing industry. During that time I've experienced firsthand the frustration of spending my entire shift resolving a machine downtime, which ultimately could've been prevented. Existing solutions are too focused on dashboards and metrics rather than actions. That's why I'm passionate about arming manufacturing engineers with Praxis AI, enabling them to combine their tribal knowledge with AI insights.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/ransika-liyanage/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/be3bdb3f8251df73fa38c4964a07add59b8e885c.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 85869, "title": "Praxis AI - Reasoning Agents Fine-Tuned for Manufacturing", "tagline": "Meet Praxis AI, the platform made to eliminate fire-fighting for manufacturing engineers", "body": "# TL;DR\n\nPraxis AI builds manufacturing-specific agents capable of reasoning and processing complex situations. Our engineering copilot predicts and prevents machine failures in real-time by solving problems the same way an engineer would. This results in proactive maintenance which helps prevent unplanned downtimes and quality defects.\n\n[Launch Video](https://www.youtube.com/watch?v=qJ41WG_X8pw):\n\n# The Problem\n\nWe have over 10 years of experience working in the manufacturing industry between the two of us. During that time we have noticed that manufacturing engineers are constantly being pulled in every direction due to production issues popping up in multiple parts of the plant. \n\nThis reactive approach results in:\n\n* Large amounts of unplanned downtime, resulting in lost throughput\n* Unnecessary wear and tear on expensive manufacturing machinery\n* Quality defects requiring non-value add rework prior to customer delivery\n\nFrom unplanned downtimes alone, US manufacturers lose over **$50 billion dollars per year**. \\\n\\\nUnfortunately, existing solutions are:\n\n* Complex and required large amounts of data science/computer science knowledge\n* Focused on dashboarding and KPI metrics\n* Extremely costly to implement and maintain\n* Not tailored for manufacturing use cases\n\n# The Solution\n\nPraxis AI provides manufacturing engineers with an end-to-end solution for identifying and root-causing machine issues by following a 5-step reasoning process. This mimics exactly how an engineer would solve an issue in the real world. Our platforms core competencies include:\n\n1. **SOTA AI architectures for manufacturing** - Our models have been specifically designed to work with time series data coming from the manufacturing shop floor.\n2. **No code tools for manufacturing engineers** - The Praxis AI platform can be used without ever touching a Jupyter notebook.\n3. **Explainable AI for root cause analysis** - Our UI gives users full visibility into the reasoning behind the output of our models, and our copilot Max.e.\n4. **Operationalized machine learning models** - Praxis ensures that every model that is built is able to be utilized every single day to drive actual changes on the shop-floor. It's not just a pretty dashboard to look at.\n\nThe Praxis platform provides the tools needed for engineers to make changes on their shop-floor which can lead to reductions in unplanned downtimes, reduced scrap/waste, improvements on the efficiency of their machines, and so much more. We’ve built the platform in a way that as we continue to work with more customers, we will be able to encompass more and more use cases. We’ve already begun preliminary work on adding production order optimization and maintenance backlog management to our solution suite. \n\n# The Team\n\n![uploaded image](/media/?type=post&id=85869&key=user_uploads/2229645/e65c1855-0e3c-4b5a-9014-22f633580153)\n\nWe have been working in the manufacturing industry for our entire professional career, and are extremely passionate about being on the cutting edge of manufacturing technology.\n\nRansika has worked at multiple manufacturing companies like GM, Tesla, and Rockwell Automation. During this time he has worked a variety of foundational manufacturing roles like manufacturing engineer, quality engineer, controls engineer, and production planner. During this time he managed multiple projects building and implementing various digital manufacturing projects globally which resulted in millions of dollars in cost savings per year. For the last two years he has been working at Deloitte as a product lead for Deloitte’s Intelligent Ops platform.\n\nAditya worked at Palantir for three years, ending his career there as a technical lead. During this time he specialized in converting pilots in the manufacturing vertical with a 100% success rate and a TCV of over $15 million dollars. Aditya has had a passion for ML/AI in manufacturing since early college, first excelling in the space by building a predictive model for General Motors and winning first in a data challenge of over 50 teams of PhD and masters students.\n\n# Our Ask\n\nIf you are interested in testing our solution out, we would love to be in touch to do a free factory assessment! We are looking to test our solution in a variety of different manufacturing environments, but have a particular affinity to continuous processes that can be found at paper mills, oil refineries, and chemical plants. Feel free to reach out at [founders@praxis-tech.ai](mailto:founders@praxis-tech.ai). Thanks!", "url": "https://www.ycombinator.com/launches/MKz-praxis-ai-reasoning-agents-fine-tuned-for-manufacturing", "votes": 61, "created_at": "2024-11-25T21:20:07.954Z" } ] }, { "id": 30045, "name": "Archil", "slug": "archil", "former_names": [ "Neptune Storage", "Regatta Storage" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0695554aaa43f831a6d970c73486aad8f7a9101f.png", "website": "https://archil.com", "all_locations": "San Francisco, CA, USA", "long_description": "Archil transforms S3 buckets into a 30x faster, unlimited, local disk. Archil enables AI, analytics, and serverless applications to instantly access massive data sets without waiting for data transfer. Researchers use Archil for shareable, local storage of data set and model versions that never runs out of capacity.", "one_liner": "The high-performance file system that connects AI to data", "team_size": 11, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1727196277, "tags": [ "Developer Tools", "Machine Learning", "Big Data", "Infrastructure", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2024", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "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/archil", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/archil.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/archil", "x_url": "https://x.com/archildata", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0695554aaa43f831a6d970c73486aad8f7a9101f.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": 2542, "full_name": "Hunter Leath", "title": "Founder/CEO", "founder_bio": "I'm building Archil, a cloud file system with unlimited capacity, local-like performance, and instant access to massive data sets in your S3 buckets. I've spent the past 9 years building cloud storage products at AWS and Netflix, and I'm thrilled that teams can finally stop managing disks.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/hleath/", "x_url": "https://x.com/jhleath", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/211cb23fe3bd004ef4434c9069d5d0694c5b647e.jpg" } ], "jobs": [], "news": [ { "title": "Archil - All of your data, on every instance, instantly", "url": "https://archil.com/post/archil-file-system-to-data-company", "date": "Jun 02, 2025" }, { "title": "The Quest for a Distributed POSIX-Compatible Filesystem", "url": "https://materializedview.io/p/the-quest-for-a-distributed-posix-fs", "date": "Dec 05, 2024" } ], "launches": [ { "id": 85093, "title": "Archil: Transform S3 into an infinite, local file system", "tagline": "AI and analytics teams use Archil to instantly access massive S3 data sets, without waiting for data transfer or worrying about capacity.", "body": "_TL;DR:  Archil transforms S3 buckets into infinite, local file systems. It’s 30x faster than accessing S3 directly, and 90% cheaper than manually managing EBS disks. Archil provides instant access to data in S3, and it powers AI applications that can’t wait for massive data sets to download. Archil doesn’t require code changes because it’s just a file system. You can try it today at [archil.com](https://archil.com)._\n\nI’m Hunter, the founder of Archil. I’ve spent the past 9 years building cloud storage products on the frontier of what’s possible – leading both Amazon’s Elastic File System product and Netflix’s cloud storage team. During this time, I worked with thousands of application developers, cloud teams, and software providers to deeply understand how they use storage.\n\nHere's the problem: I found that AI and analytics teams love storing their data in S3 because it’s low-cost and scalable, but their applications can only use this data once it’s in a local file system. As a result, these teams are constantly moving data back and forth from their local instance file systems to S3. For larger data sets, applications can wait for hours to download and decompress data before starting. Unlike S3, local file systems have a limited amount of capacity that you can’t easily change after creation, they don’t support sharing data, and they’re 10x more expensive.\n\nArchil transforms your existing S3 buckets into a new kind of infinite, local file system. With Archil, your applications can instantly use petabyte-scale data sets in S3 without waiting for manual data download. Archil automatically scales with your data, and you only pay for the data you’re actively using – 90% cheaper than using EBS. With Archil’s shared, high-speed cache, all your instances and containers can take advantage of 30x faster, consistent reads and writes. When you’re done editing data, it automatically flows back to S3 within a few minutes, so you can easily share results with teammates. Archil has full POSIX compatibility (including renames, appends, and locking), so it’s already compatible with your applications – no code changes needed. Teams move faster when they use Archil because they don’t have to build custom data movement infrastructure.\n\n[Check out how easy Archil is to set up and use.](https://www.tella.tv/video/cm2num964000e03jng6jl5d9c/view)\n\nWe are really excited about how Archil will help teams simplify their data management. We are currently working with: AI teams who want to start model inference without waiting for downloads to complete, researchers who want to experiment on models and create new versions without running out of disk capacity, analytics teams who want to speed up their batch pipelines, and cloud teams who want lower-cost local storage without worrying about utilization. We’d love to work with you, too. If you have any questions, feel free to email me at [hleath@archil.com](https://mailto:hleath@archil.com). You can try Archil today by signing up at [archil.com](https://archil.com).\n\nLet’s build the future of the cloud together.\n\n![uploaded image](/media/?type=post&id=85093&key=user_uploads/2542/df85a282-87df-4986-b3c7-9cb58acc84ab)", "url": "https://www.ycombinator.com/launches/M8T-archil-transform-s3-into-an-infinite-local-file-system", "votes": 65, "created_at": "2024-10-28T17:59:23.946Z" } ] }, { "id": 30303, "name": "AfterQuery", "slug": "afterquery", "former_names": [ "Cronus" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b63e52a3ec831660a5917dbb85f52cfd61f714e9.png", "website": "https://afterquery.com", "all_locations": "San Francisco, CA, USA", "long_description": "AfterQuery is an applied research lab curating data solutions for frontier foundation model development. Serving every frontier AI lab.", "one_liner": "Applied research lab curating data solutions for foundation model…", "team_size": 30, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1738219847, "tags": [ "B2B", "Data Labeling", "Big Data", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "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/afterquery", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/afterquery.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/afterquery", "x_url": "https://x.com/AfterQuery", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b63e52a3ec831660a5917dbb85f52cfd61f714e9.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": 2382342, "full_name": "Spencer Mateega", "title": "Founder/CEO", "founder_bio": "Building AfterQuery. Previously interned in technology private equity at Silver Lake, technology investment banking at Morgan Stanley, and software engineering at Meta and Google.\n\nB.S. in Finance and Statistics at Wharton and a M.S. Computer Science at Penn.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/spencermateega", "x_url": "https://x.com/spencermateega", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/afbc3ee1379a69d69496a3aa75ca799fcd63a8fd.jpg" }, { "user_id": 503252, "full_name": "Carlos Georgescu", "title": "Founder/CTO", "founder_bio": "Building AfterQuery. Previously interned at Citadel Securities (2x), Meta & Google as a software engineer. Past acquired ed-tech founder. \n\nStudied for a B.S. in Computer Science from the University of British Columbia. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/carlossgg/", "x_url": "https://x.com/CarlosGeorgescu", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7e75fffea45bad544c5ba2ee3ea005b9731dda68.jpg" } ], "jobs": [ { "id": 75196, "title": "Software Engineer Intern", "url": "https://www.ycombinator.com/companies/afterquery/jobs/6a7szXP-software-engineer-intern", "location": "San Francisco, CA, US / Remote (US)", "type": "Internship", "role": "Engineering", "role_type": "Frontend", "salary_range": "$7.5K - $10K / monthly", "equity_range": "", "experience": null, "visa": "US citizen/visa only", "skills": [] }, { "id": 82771, "title": "Software Engineer - Applied AI / Platform", "url": "https://www.ycombinator.com/companies/afterquery/jobs/7tsT0ks-software-engineer-applied-ai-platform", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$140K - $200K", "equity_range": "", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "Node.js", "Python", "Next.js" ] }, { "id": 87019, "title": "Software Engineer - Hiring Infra (LatAM)", "url": "https://www.ycombinator.com/companies/afterquery/jobs/PohDLWh-software-engineer-hiring-infra-latam", "location": "San Francisco, CA, US / Remote (BR; CL; MX; AR; CO; PE)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$7K - $10K / monthly", "equity_range": "", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [] }, { "id": 91403, "title": "Strategic Projects Lead", "url": "https://www.ycombinator.com/companies/afterquery/jobs/iTt13cJ-strategic-projects-lead", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$200K - $400K", "equity_range": "", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 91406, "title": "Technical Strategic Projects Intern", "url": "https://www.ycombinator.com/companies/afterquery/jobs/8NagVRQ-technical-strategic-projects-intern", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$90K", "equity_range": "", "experience": "", "visa": "US citizen/visa only", "skills": [] }, { "id": 91408, "title": "Strategic Projects Associate", "url": "https://www.ycombinator.com/companies/afterquery/jobs/qwNKclV-strategic-projects-associate", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$200K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 91409, "title": "Strategic Projects Intern", "url": "https://www.ycombinator.com/companies/afterquery/jobs/Ep6eNax-strategic-projects-intern", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$90K - $90K", "equity_range": "", "experience": "", "visa": "US citizen/visa only", "skills": [] }, { "id": 91415, "title": "Technical Strategic Projects Associate", "url": "https://www.ycombinator.com/companies/afterquery/jobs/ziJE7ZY-technical-strategic-projects-associate", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$200K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 91417, "title": "Marketing Lead", "url": "https://www.ycombinator.com/companies/afterquery/jobs/Ww01atn-marketing-lead", "location": "San Francisco", "type": "Full-time", "role": "Marketing", "role_type": null, "salary_range": "$160K - $220K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 91418, "title": "Technical Strategic Projects Lead", "url": "https://www.ycombinator.com/companies/afterquery/jobs/t0uj8SF-technical-strategic-projects-lead", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$200K - $400K", "equity_range": "", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 91613, "title": "Senior Software Engineer, Infrastructure & Platform", "url": "https://www.ycombinator.com/companies/afterquery/jobs/TU2SAvz-senior-software-engineer-infrastructure-platform", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$220K - $280K", "equity_range": "", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "AfterQuery Becomes YC’s Fastest Unicorn At $3.2 Billion", "url": "https://www.forbes.com/sites/annatong/2026/09/01/afterquery-becomes-ycs-fastest-unicorn-at-32-billion/", "date": "Sep 01, 2026" }, { "title": "This 23 Year-Old’s New AI Data Company Has Already Hit A $100 Million Run Rate", "url": "https://www.forbes.com/sites/annatong/2026/04/09/this-23-year-olds-new-ai-data-company-has-already-hit-a-100-million-run-rate/", "date": "Apr 09, 2026" } ], "launches": [ { "id": 87549, "title": "AfterQuery - High-quality AI starts with high-quality human data", "tagline": "Powering AI models with expert-level human data", "body": "## **TL;DR**\n\nAI models need high-quality training data to effectively automate complex professional tasks - we provide that data. If you are building an AI model or need fine-tuning data for your agent, we’d love to help. \n\n\\------------\n\n## **Why We’re Building AfterQuery**\n\nThere is a dearth of high-quality training data.\n\nCurrent AI models are trained to be smart but are not trained on the work of actual professionals in many fields. \n\nFor example, current models are largely unusable to professionals working in finance (private equity, hedge funds, investment banks, etc). See our [paper](https://arxiv.org/pdf/2501.18062), where LLMs fail 60% of realistic daily tasks – but with some fine-tuning on high-quality training data, significantly improve.\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeJndLBLBOQJVpf7yPJNA2WFddpffkxqdE1rFqmEfxo3FRmcSCVWqHnsOR793XuN1iCUm-ibeWfFj-FV_3FaswWhXetsEhembFLjln5Lwc9LA0tJV6uNq7MPWyjdDq7FTZSRdmekQ?key=_udDbu5NRw5fwLzMYyqM9i52)\n\n_Chart showing LLM performance against FinanceQA_\n\nAnd the lack of high-quality training data is everywhere:\n\n* Domain-specific AI (legal, consulting, healthcare, etc.)\n* Multimodal data (audio, multilingual, etc.)\n* Foundational models looking to push the boundary on reasoning and grading\n\n## **Solution**\n\nWe are building a platform to onboard experts to work on custom datasets. Think working professionals at top firms and the smartest students in various subjects (PhDs). Then, whenever you have a request for a certain type of data, we will construct and lead a team to produce:\n\n* Post-training / fine-tuning data\n* Human-Led Reinforcement Learning (RLHF) data\n* Custom benchmarks\n\nYou will know the people making your dataset and their qualifications while we coordinate with you to ensure quality standards. We save you time and energy, so you can focus on the model and not the data.\n\n## **Example Datasets We Provide**\n\n_Foundational Model Advancing Coding Capabilities:_\n\n* New data structures and algorithms problems with test cases and step-by-step answer reasoning \n* Computer architecture and system design reasoning\n* Performance optimization code with benchmarks and reasoning\n\n_Enterprise SaaS Company Building Internal AI Developer Tool:_\n\n* Codebase-wide code snippets with corresponding refactoring suggestions and explanations \n* System and runtime errors paired with step-by-step debugging solutions \n* Database query optimization solutions and reasoning steps\n\n_Startup Building an AI Agent for Law:_\n\n* Answered legal questions with expert reasoning\n* Computer use data for common tasks on legal project management software\n* Contract redlines with expert corrections\n\n**If AI is one day to replace jobs entirely, it can’t just be good, it needs to be near perfect. This transformation will require an immense amount of high-quality training data.**\n\n## **Our Ask**\n\n* Are you **working on a foundational model** advancing multimodal applications, nuanced reasoning, or domain-specific expertise?\n* Are you **implementing AI in a specialized domain** where current models lack expertise (e.g., medicine, legal, finance, software, government, healthcare)?\n\nWe would love to meet you! \n\n## **Our Team**\n\nCarlos and Spencer first met in high school at a summer program at Google and then interned together at Meta. Danny and Spencer met in high school, where they built a startup. They then both got into Wharton, sold that startup, and became roommates.\n\n[Carlos](https://www.linkedin.com/in/carlossgg/) has built multiple software businesses on his own, exited an ed-tech startup, and has worked as a software engineer at Citadel Securities. He’s won the largest possible academic scholarship in all of Canada, competed in state and national sailing competitions, and has 8x his bankroll in poker.\n\n[Spencer](https://www.linkedin.com/in/spencermateega) wrote an award-winning research paper and interned at a private equity firm in high school, before deciding to intern at Meta (SWE), Google (SWE), Morgan Stanley (IB), and Silver Lake (PE) in the next 3 years. He was the sole summer analyst at Silver Lake globally and has programmed numerous apps in his spare time (and sold some of them). He trains for half marathons and lifts in his free time between completing a master’s in computer science concurrently with his Wharton undergraduate degree.\n\n[Danny](https://www.linkedin.com/in/danny1898/) competed and won international competitions in public speaking during high school while pursuing his interest in AI, winning an international Microsoft AI competition and conducting research at a machine learning neuroscience lab. He then learned how people build real estate, from grocery stores to billion-dollar luxury hotels, eventually working on the largest real estate IPO in history and the largest IPO in 2024. He also really likes movies and attended the world's largest film festival, catching premieres of films like Anora and The Substance.\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXfkN7pvcMyTHjf5fZe7qJrF9xiyAvI0yAJ0PHNDdxWcOcU0gw5cDFUzOq20x68YJeb40Pk8dPXsNGsPVSnhYNZu3AHEAoZHcSTQ3nYyLYvMNcthHoAkoERsmzARwkGFS-fRnslLNw?key=_udDbu5NRw5fwLzMYyqM9i52)\n\n", "url": "https://www.ycombinator.com/launches/Mm5-afterquery-high-quality-ai-starts-with-high-quality-human-data", "votes": 114, "created_at": "2025-02-10T21:58:10.460Z" } ] }, { "id": 30507, "name": "ParaQuery", "slug": "paraquery", "former_names": [ "Hypermorphic" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/023b1fec88d11ac0cc00d33e137188a1088f80f1.png", "website": "https://paraquery.com", "all_locations": "New York City, NY, USA; Remote", "long_description": "ParaQuery is a fully-managed GPU-accelerated Spark solution providing double the performance at half the cost compared to solutions like Databricks and BigQuery. It is fully Spark-compatible, cloud agnostic, and has seamless integrations everywhere, all without vendor lock-in. ParaQuery makes it trivial to run large SQL and Spark workloads efficiently and serverlessly, without any data migrations.", "one_liner": "Managed GPU-accelerated Spark + SQL at 2x speed and half the cost", "team_size": 1, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1745603504, "tags": [ "Developer Tools", "Analytics", "Big Data", "Enterprise Software", "Infrastructure" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 2025", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "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/paraquery", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/paraquery.json", "year_founded": 2023, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/paraquery", "x_url": "http://twitter.com/paraquery_", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/paraquery", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/023b1fec88d11ac0cc00d33e137188a1088f80f1.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": 1091672, "full_name": "Win Wang", "title": "Founder/CEO", "founder_bio": "Focused on efficiency, effectiveness, and (user) experience.\nPreviously a tech lead @ Twitter, building massively-parallel distributed build systems.\nNow building a cheaper, faster big data platform by using the right hardware for job. Fully-managed, GPU-accelerated, petabyte-scale at double the speed and half the cost.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/winwang", "x_url": "https://twitter.com/Winium", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3a5065a212f3a4dd12f6130ba83f67162fb66507.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 90162, "title": "ParaQuery - 5x your data warehouse's efficiency", "tagline": "ParaQuery is the GPU-accelerated Spark + SQL engine for your big data needs.", "body": "![uploaded image](/media/?type=post&id=90162&key=user_uploads/1091672/94e086c6-918d-4975-94ca-5aece8bff85a)\n\nHey, I'm Win, founder of [ParaQuery](http://www.paraquery.com).\n\nWe're building a bigger query engine with up to 5x cost efficiency compared to BigQuery by using cost-efficient cloud GPUs.\n\n**Ask:** Do you know someone optimizing their data warehouse? Email [win@paraquery.com](mailto:win@paraquery.com) or sign up [here](https://paraquery.com/waitlist)!\n\n**4x'ing** BigQuery efficiency with ParaQuery:  \n\n# **The Problem**\n\nData is the new oil and you're feeding your products with a bunch of it. But to do so, you need to build, maintain, and optimize your data infra. That's either a huge timesink, or you're bleeding cash to ignore it, or someone is breathing down your neck about this month's cloud bill. Why can't we just ship features?\n\n![uploaded image](/media/?type=post&id=90162&key=user_uploads/1091672/419acb3d-1195-4842-a80e-df02ac0d332f)\n\nWhether it's those 20-minute+ Presto queries, or your exploding BigQuery bill (which happens to be tracked in real time), your data infra is letting you down.\n\n# **Our Solution**\n\nParaQuery runs on highly-parallel, throughput-optimized cloud-available hardware, perfect for data processing workloads: **GPUs**!\n\nOur system abstracts out the infra required to serverlessly accelerate your logic. With the extreme efficiency of GPUs, it's half the cost and double the performance of competing solutions like Databricks and BigQuery, without any platform commitments.\n\nFor SQL-based data warehouses like BigQuery, you can use ParaQuery with minimal changes to your queries. And since it's fully Spark-compatible, there's no vendor lock-in. It integrates with literally anything which has a Spark connector, on any major cloud. We can even deploy on GPU clouds.\n\nParaQuery makes data processing as simple as specifying:\n\n1. Inputs\n2. Output\n3. Logic\n4. Run!\n\n![uploaded image](/media/?type=post&id=90162&key=user_uploads/1091672/a6cded39-2da7-432b-9129-014a593caaae)\n\n# **Why?**\n\nI've been looking into GPUs and parallel programming for over 12 years – starting out with crypto. I left that field pretty fast, but I never stopped thinking about efficient compute and GPU acceleration. Even more so when, years later, I had to wait for my laptop to finish 18-hour physics simulations. And during my professional career, I've also had to wait on my fair share of 20-minute+ analytical queries.\n\nIn fact, I hate waiting. ParaQuery is productizing an arbitrage of hardware efficiency, and I'm not waiting any longer to do so.\n\n# **Ask**\n\n1\\. If you know anyone who is running any big data processing scenarios (including MLOps), I'd love to learn more about their use case and see if we're a fit! \n\n2\\. If you want to try it out, sign up for our waitlist at [www.paraquery.com](http://www.paraquery.com), or just email me ([win@paraquery.com](mailto:win@paraquery.com))", "url": "https://www.ycombinator.com/launches/NSE-paraquery-5x-your-data-warehouse-s-efficiency", "votes": 51, "created_at": "2025-05-07T02:14:51.329Z" } ] }, { "id": 30602, "name": "Meteor Labs", "slug": "meteor-labs", "former_names": [ "Meteor" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/463b3d216b98b96d985f820caa0a09e2c3c9ee44.png", "website": "https://meteorlabs.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Meteor Labs is a research company focused on providing a granular view of the data center supply chain.\r\n\r\nWe track upstream of the major data center buildout to understand the OEMs winning the market, the bottlenecks, and supply chain links.\r\n\r\nOur expertise in browser agents and AI helps us source this information from esoteric public filings across the web.", "one_liner": "We provide hedge funds with information they can trade on", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1754469732, "tags": [ "Big Data", "Data Engineering", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2025", "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/meteor-labs", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/meteor-labs.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/meteorlabsai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/463b3d216b98b96d985f820caa0a09e2c3c9ee44.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": 1531197, "full_name": "Pranav Madhukar", "title": "Founder/CEO", "founder_bio": "Finding valuable information from the public web at meteorlabs.ai. NSF-funded math at UChicago. FOIA nerd. Browser agent research.\n\nPrev CS @ UW where I built viral tools on campus and then raised $150k in university funding to encourage builders. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/pranav-madhukar-pcm/", "x_url": "https://x.com/pranavcmadhukar", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f6967d5795c9b074e9b14a134e4ce951d3159e0c.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 92864, "title": "Meteor Labs", "tagline": "A Browser that gets things done, on your behalf.", "body": "Hey everyone! 👋\n\nWe’re Pranav and Farhan – co-founders of Meteor Labs. And we’re on a mission to kill Google Chrome.\n\nMeteor is a browser that can do work on your behalf.\n\nDownload now: [www.browse.dev](http://www.browse.dev)\n\n# **The Problem**\n\nWe spend several hours each day on our browser doing boring, repetitive tasks. Sending out calendar invites, filling up repetitive forms, moving data from one website to another, creating spreadsheets etc.\n\n# **Introducing Meteor**\n\nMeteor is an agentic browser – our agents can perform tasks for you.\n\nManually setting up calendar invites? Just ask Meteor.\n\nSearching through dozens of websites to find the best price? Just ask Meteor.\n\nConfused how to configure your S3 Bucket? Just ask Meteor.\n\nMeteor is your very own personal assistant. Enhanced by your browser context.\n\n\n\n# **The Journey**\n\nPranav and Farhan met during orientation week at UW, and now we’re both dropped out of UW to build the future of browsing.\n\nA few weeks into our batch, we decided to fork Chromium (30M+ lines of code!) and rebuild our entire product from scratch. Simply compiling an instance of Chromium takes 8+ hours and over 200GB of disk space. 3 weeks later we are excited to launch our browser!\n\nPranav (CEO) previously built viral projects used by over thousands of students at the UW, and raised over $100k in university funding.\n\nFarhan (CTO) previously built an IDE for hardware design that compiles 60x faster than the industry standard.\n\n![uploaded image](/media/?type=post&id=92864&key=user_uploads/1531197/81663d2f-fdeb-4cfd-8e26-80f8c04013f8)\n\n", "url": "https://www.ycombinator.com/launches/O9o-meteor-labs", "votes": 38, "created_at": "2025-08-10T01:28:18.337Z" } ] }, { "id": 30622, "name": "Liva AI", "slug": "liva-ai", "former_names": [ "Symbia", "Liva" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6d577992606b8392c9619b8dffacec074b48668d.png", "website": "https://www.theliva.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Helping to build more socially intelligent AI, starting with voice.", "one_liner": "Building socially intelligent AI", "team_size": 6, "industry": "B2B", "subindustry": "B2B", "launched_at": 1753401226, "tags": [ "Marketplace", "B2B", "Data Labeling", "Big Data", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "B2B" ], "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/liva-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/liva-ai.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/thelivaai/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6d577992606b8392c9619b8dffacec074b48668d.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": 2218085, "full_name": "Ashley Mo", "title": "Founder/CEO", "founder_bio": "Co-founder & CEO | prev. Caltech CS | Published research on an AI model that could detect lung disease through cough recordings at 17 y/o (IEEE Xplore) and led large-scale research study to collect health audio data @MIT | Built biosensors for chronic kidney disease @Caltech", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ashley-mo/", "x_url": "https://x.com/ashlleymo", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/560a22eeec798d2a8bbad3f734a78566c4e25c11.jpg" }, { "user_id": 1393905, "full_name": "Aoi Otani", "title": "Founder/CTO", "founder_bio": "Co-founder & CTO ", "is_active": true, "linkedin_url": "https://linkedin.com/in/aoi-otani-ab3a1b205", "x_url": "https://x.com/bigvalleyblue", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4d5f89e70756e3eca913dfa01582a7032fe54671.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 92886, "title": "Liva AI – Real Voice & Video Data for AI", "tagline": "We provide real voice and video data for developing realistic AI.", "body": "**TL;DR:** We provide high-quality human voice and video datasets for companies building realistic AI models. Real data sourced in-house across diverse languages, emotions, and contexts.\n\n**Ask:** [Talk to us](https://calendly.com/ashleymo-theliva/30min) if you're a researcher or founder building voice/video generation models. We can help you.\n\nhttps://youtu.be/PwjTSZRC8XE\n\n**The Problem:**\\\nCompanies building voice and video AI models need quality training data to reach human-like performance for models. Scraping has limits and existing datasets lack diversity across accents, emotions, and languages. As AI moves toward human-like interactions like customer conversations, therapy sessions, classroom teaching, or entertainment, models need authentic human expressions that the internet can't supply.\n\n**Our Solution:**\\\nLiva collects real, consented human voice and video entirely in-house—no synthetic data or third-party purchases. All content is authentic and rights-cleared.  We’re already delivering our voice dataset to a lab training expressive foundation models for voice.\n\nWe capture diverse accents, emotional range, and varied contexts (sales calls, multi-channel dialogues, expressive monologues, casual conversations, job interviews, and more) with high production quality through crowdsourcing and strategic partnerships.\n\n**The Team:**\n\nWe’ve worked on many research projects together since we met 3 years ago.\n\n* **Ashley:** Caltech CS dropout. Built an AI model to detect lung disease from cough recordings (@ MIT) and led large-scale data collection initiatives. \n* **Aoi:** Prev. Harvard Bio/CS. Did ML research in representation learning and image diffusion models. 5+ Publications in ICML, Nature, etc.\n\n**Our Ask:**\n\nIntroductions to:\n\n* AI labs building or fine-tuning voice, video, and multimodal generation models\n* Companies using or integrating voice/video models\n* Film, audio, and content production companies\n* Researchers working on audio, video, and multimodal generative models\n\nContact us: [founders@theliva.ai](mailto:founders@theliva.ai)", "url": "https://www.ycombinator.com/launches/OAA-liva-ai-real-voice-video-data-for-ai", "votes": 50, "created_at": "2025-08-12T03:48:13.634Z" } ] }, { "id": 30748, "name": "Pleom", "slug": "pleom", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3b1e76d8c0743b28030c0f3c37f0d0bfa7f51272.png", "website": "https://pleom.com", "all_locations": "New York City, NY, USA", "long_description": "Automatic insights on all your company data. Speed up business workflows to visualizations with a 0 learning curve.", "one_liner": "Agentic Business Intelligence", "team_size": 0, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1755614874, "tags": [ "Generative AI", "Analytics", "Big Data", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "B2B", "Analytics" ], "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/pleom", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/pleom.json", "year_founded": 2025, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/pleom", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/Pleom", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3b1e76d8c0743b28030c0f3c37f0d0bfa7f51272.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": 2354623, "full_name": "Royce Arockiasamy", "title": "Founder/CEO", "founder_bio": "Building Agentic Business Intelligence", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/roycea1/", "x_url": "https://x.com/roycearoc", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/64c3fba23c6331c3b4fc8a87fb983a0d87378e2a.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 93152, "title": "Pleom ☂️ - Talk to your AI", "tagline": "Hardware AI devices for everyday consumers.", "body": "**TL;DR**\n\n> Today, Pleom is a website. Ask about anything by talking, showing, or sharing. Tomorrow, it will be integrated into our family of hardware devices.\n\n**What does Pleom do?**\n\nhttps://youtu.be/uH-W9BKGKT0\n\nAt Pleom, we are building AI hardware devices that everyday consumers can talk to for real-world tasks that understands your context. We’re going head-to-head with OpenAI, moving faster to market and capturing mindshare first.\n\n**The Problem**\n\nThe current form factor for how people interact with AI hasn’t changed in 5 years.\n\nTexting is also a slower mode of communication than speaking.\n\nMoreover, Artificial Intelligence lacks the context to properly respond to your questions. Products like ChatGPT fail to understand your complete digital context and have zero knowledge of the physical world context.\n\n**The Solution**\n\n![uploaded image](/media/?type=post&id=93152&key=user_uploads/2354623/b50422f7-89d9-4b71-bc77-1f86a0e664eb)\n\nA real-life “Jarvis” which seamlessly connects your phones, laptops, car, home appliances, and more to build context to help consumers _ask and do anything_.\n\nOur current live solution on [pleom.com](http://pleom.com) allows anyone to talk to, share their camera or screen and ask questions and receive answers instantly.\n\nBut hardware is our focus and we are transitioning from prototyping into production to launch our first version of pocket AI wearable device with a camera, microphone, and speaker that anyone can carry around and interact with faster than using any current form factor today.\n\n**Team**\n\nRoyce - previously co-founded Bits to Atoms (YC S24) hiring cracked engineers for deep-tech startups. Graduated from Georgia Tech with both a Bachelors and Masters in Machine Learning by the age of 18. Also, worked as a Data Scientist for two years and also contributed to multiple other startups.\n\n[royce@pleom.com](https://royce@pleom.com)\n\n![uploaded image](/media/?type=post&id=93152&key=user_uploads/2354623/f7582d53-5884-4d0b-89d8-529f93bbee86)\n\n", "url": "https://www.ycombinator.com/launches/OES-pleom-talk-to-your-ai", "votes": 20, "created_at": "2025-08-20T14:26:21.613Z" } ] }, { "id": 30846, "name": "Parse", "slug": "parse-bot", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/fd2b15ce8a8e404c0ec54c33a17f938963c26455.png", "website": "https://parse.bot", "all_locations": "San Francisco, CA, USA", "long_description": "Parse lets developers build APIs for any website. Unlike traditional scraping tools that rely on slow, expensive headless browsers, Parse examines websites' underlying network requests and generates API endpoints automatically. This makes Parse 10-100x faster and cheaper than browser automation,. ", "one_liner": "Build an API to interact with any website.", "team_size": 4, "industry": "B2B", "subindustry": "B2B", "launched_at": 1761590739, "tags": [ "Developer Tools", "API", "Big Data" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2025", "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/parse-bot", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/parse-bot.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/parsedotbot/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/fd2b15ce8a8e404c0ec54c33a17f938963c26455.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": 986516, "full_name": "Alex Forman", "title": "Founder/CEO", "founder_bio": "I've been obsessed with web automation since I was 12, when I taught myself to code building sneaker bots. That early experience showed me how broken traditional scraping tools were. I launched Parse in August, hit 30K users in 3 weeks, and dropped out of Berkeley Engineering to make it the best way to programmatically interact with the internet. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alex-k-forman/", "x_url": "https://x.com/alexscraping", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/59702956ea6d63e0855337ff76388880de105352.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 95327, "title": "Parse - APIs for any website", "tagline": "Build APIs for any website that are 100X faster than browser automation", "body": "# **Launch BF: Parse - Build APIs for any website that are 100x faster than headless browsers**\n\nTLDR: [Parse](http://parse.bot/) reverse engineers websites and builds deterministic APIs for scraping and web automation. Instead of spinning up slow, expensive headless browsers, Parse makes deterministic APIs that use direct network requests that **cost fractions of a cent and return in milliseconds**.\n\n![uploaded image](/media/?type=post&id=95327&key=user_uploads/986516/c6b55ddc-9855-4030-84b7-a70e251330da)\n\n**The Problem**\n\nHeadless browsers are the default for web scraping and automation, but they're painfully slow and unreliable at scale. Scraping thousands of dynamic pages with Playwright or Puppeteer takes hours, often failing halfway through due to timeouts or memory issues. Repeat tasks become absurdly expensive at scale due to the cost of managing hundreds of concurrent browsers.\n\n**How Parse Works**\n\nWe reverse engineer the underlying network requests behind any action on a website and build a deterministic API to replicate it. Parse automatically handles dynamic values (session cookies, CSRF tokens, etc.), IP rotation, and antibot challenges.\n\nParse is able to build a set of endpoints to handle dynamic multi-turn interactions, such as getting the questions on a job application through one endpoint and submitting the job application through another endpoint, **even though every application has unique questions**.\n\nDemo: https://youtu.be/UZGYkH2vAS4\n\nAll of this means that the APIs that Parse generates execute in seconds and cost fractions of a cent to run.\n\n**Background**\n\nI started coding at 12 to build bots to purchase limited edition sneakers in seconds. After leaving that scene, I led engineering at a trading card market platform and researched political botnets at Berkeley. No matter where I went, I realized how crucial live web data and interaction is and how slow current solutions are. I launched the first version of Parse in August and got to 35k users in a month.\n\n**Asks**\n\n* **If you're using headless browsers for scraping or automation, let's chat.** We're launching document downloading and auth handling in the coming weeks and want to hear your pain points. If you need custom solutions build out, please email me at [alex@parse.bot](mailto:alex@parse.bot)\n* **Try Parse today** - [**Parse.bot**](http://Parse.bot)", "url": "https://www.ycombinator.com/launches/OnX-parse-apis-for-any-website", "votes": 53, "created_at": "2025-11-08T20:02:17.215Z" } ] }, { "id": 30850, "name": "Zavo", "slug": "zavo", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6c070e5b38f25a46eb16f93b2a21cfccf8ec446a.png", "website": "https://zavo.ai", "all_locations": "London, England, United Kingdom", "long_description": "Zavo is a data company building proprietary, real-world data infrastructure for frontier AI. We partner with hospitals, businesses, operators and expert networks globally to access hard-to-reach data that cannot be found on the public internet.\r\n\r\nWe source, structure, annotate and license this data for model training, post-training and evaluation, and use it to build specialised benchmarks and reinforcement learning environments grounded in real-world workflows, expert decisions and outcomes.\r\n\r\nOur focus spans healthcare and robotics, where high-quality proprietary data, realistic environments and reliable evaluations are critical to building increasingly capable AI systems.", "one_liner": "Unlocking the world’s most valuable data for frontier AI.", "team_size": 8, "industry": "B2B", "subindustry": "B2B", "launched_at": 1758482945, "tags": [ "B2B", "Big Data", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2025", "status": "Active", "industries": [ "B2B" ], "regions": [ "United Kingdom", "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/zavo", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/zavo.json", "year_founded": 2025, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/zavopay", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/zavo", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6c070e5b38f25a46eb16f93b2a21cfccf8ec446a.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": 1353819, "full_name": "Can Zehebi", "title": "Founder", "founder_bio": "Founder @ Zavo", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/canzehebi/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/43700648bf4f49033fadcd5962a2ce77ec8f2881.jpg" }, { "user_id": 1995140, "full_name": "Ilkan Gezer", "title": "Founder", "founder_bio": "Founder @ Zavo", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ilkan-gezer-4a16a6268", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/015268ca8004cf0bec885653deea16f9656c323e.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 30899, "name": "Sciloop", "slug": "sciloop", "former_names": [ "SciLoop" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f01d1e4f95660e72f930c4f8b2542e2224dc9107.png", "website": "https://sciloop.dev", "all_locations": "San Francisco, CA, USA", "long_description": "Sciloop creates expert-level math and physics problems that frontier AI models can't solve, then sells the data to AI labs for training and evaluation. Our problems are created by IPhO and IMO medalists — the top 0.01% of STEM talent globally. On our benchmark, models like GPT 5.4 Pro and Gemini 3.1 Pro score 0-5% on our hardest problems. We work with AI labs to supply continuous, fresh training data that pushes the frontier of mathematical and scientific reasoning. Founded by Bilal and Osman, International Physics Olympiad medalists from MIT with hands-on ML research experience at MIT CSAIL.\r\n", "one_liner": "Expert STEM reasoning data for frontier AI labs.", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1761248663, "tags": [ "Marketplace", "Data Labeling", "Big Data", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2025", "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/sciloop", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/sciloop.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/sciloop-labs", "x_url": "https://x.com/SciloopAI", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f01d1e4f95660e72f930c4f8b2542e2224dc9107.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/fed7f882927e8fb06da3fa37372d4882ade38f87.png", "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/7236dec827b854b72e6c08d373f971278c84b65f.png" ], "primary_partner": { "name": "Aaron Epstein", "url": "https://www.ycombinator.com/people/aaron-epstein" }, "founders": [ { "user_id": 1933114, "full_name": "Bilal Asmatullah", "title": "Founder/CEO", "founder_bio": "Co-founder of Sciloop (YC F25), On Leave From MIT\nInternational Physics Olympiad ’22 medalist.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/bilal-asmatullah-8705692b4/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/bf61bc3516b5794d469bbefebfcaa598357a1e97.jpg" }, { "user_id": 2718484, "full_name": "Osman Siddique", "title": "Founder", "founder_bio": "CTO and co-founder of Sciloop (YC F25). \nCS student at MIT (on leave). Medalist and two-time highest national scorer at the 2022 International Physics Olympiad.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/osman-siddiquer", "x_url": "https://x.com/OsmanSiddiquer", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/65092c9b010287aa5db264ecfeb813617bfb76af.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 94951, "title": "Sciloop: The AI Scientific Researcher", "tagline": "Come up with novel hypotheses, run experiments, analyze results, and write the paper — all automatically.", "body": "**TL;DR: We’re making the AI that does your research for you.[ Book a call.](https://calendly.com/osman-sciloop/30min)**\n\nHi.\n\nWe’re building **Sciloop**, a platform that automates the entire scientific research loop from hypothesis generation to experiment design, execution, analysis, iteration, and drafting real research papers.\n\nhttps://youtu.be/GNM8iA6p9Ao\n\n**Our Story:**\n\nWe’re Bilal and Osman, IPhO medalists and former MIT students with 4 years of experience applying ML in various research domains. While working at MIT CSAIL, we saw how large language models could help with literature review and even generate new hypotheses. Stanford’s recent study ([Can LLMs Generate Novel Research Ideas?](https://arxiv.org/abs/2409.04109))  showed that LLM-generated hypotheses can be more novel than those from domain experts. We saw the same in our own work.\n\n![uploaded image](/media/?type=post&id=94951&key=user_uploads/2718484/3a420779-570b-4a69-b3ee-628644695a3e)\n\nBut after generating ideas, everything slowed down. We spent hours designing experiments, writing code, debugging compute setups, and rerunning failed jobs that could crash after 12 hours. Even with tools like Cursor that helped us code faster, the overall loop from idea to insight was still painfully manual. Researchers everywhere face this same bottleneck.\n\nSo we asked ourselves: what if the research loop itself could be automated?\n\nSo we built **Sciloop**. It takes a research intent — a question or hypothesis — and automatically designs, runs, and monitors experiments in parallel on our own managed cloud compute. It tracks metrics, analyzes results, recommends next steps, and finally drafts a paper summarizing methods and findings. Sciloop turns days of busywork into hours of progress.\n\nOur goal is simple: **to completely automate scientific discovery**. The next decade will see a century of progress packed into a few years. As AI grows smarter and more accessible, its biggest impact will be speeding up science — finding new cures, discovering better materials, and driving breakthroughs in physics and math.\n\nThat’s our north star. But we’re not waiting for the future. We’re helping ML researchers move faster **right now**, automating the parts of their workflow that slow them down most. Sciloop is already being used in MIT research groups and by early partners working on real ML problems.\n\nIf you’re a company, research lab, or ML team struggling with model performance, experimentation speed, or scaling research, we’d love to work with you. Our goal is to deliver concrete improvements on your research challenges today.\n\n🚀 **The Ask - Let’s Make Research Self-Improving!**\n\nIf you’re working on ML research that could move faster, **book a quick chat** at https://calendly.com/osman-sciloop/30min or email us at [**team@sciloop.dev**](mailto:team@sciloop.dev).\n\nWe help brilliant research ideas stop gathering dust. This is for anyone who:\n\n* Runs **university or industry ML labs** drowning in experiments.\n* Is a **research student** buried under mountains of code and papers.\n* Leads a **company dreaming of R&D** but lacking scientists or resources.\n* Has a **head full of creative ideas** but no time to bring them to life.\n\nLet’s collaborate and accelerate your research—together.\n\n\\[1\\][ Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers](https://arxiv.org/abs/2409.04109)", "url": "https://www.ycombinator.com/launches/OhT-sciloop-the-ai-scientific-researcher", "votes": 33, "created_at": "2025-10-29T01:25:09.249Z" } ] }, { "id": 31257, "name": "Atlia", "slug": "atlia", "former_names": [ "Odyssey" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7a4b2fb5aaa99f5f795d2aa898508681eae26222.png", "website": "https://www.atlia.com", "all_locations": "San Francisco, CA, USA", "long_description": "We use AI agents to manage short-term rental properties by running operations completely autonomously. We cut traditional human management fees in half so owners take home 20% to 30% more revenue. And we operate with near-zero boots-on-the-ground so we can scale nationwide with software margins and at venture speed in a traditionally physical industry.", "one_liner": "The AI-native property management company for short-term rentals", "team_size": 2, "industry": "Real Estate and Construction", "subindustry": "Real Estate and Construction -> Housing and Real Estate", "launched_at": 1785302953, "tags": [ "Real Estate", "Proptech", "Big Data", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "Real Estate and Construction", "Housing and Real Estate" ], "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/atlia", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/atlia.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/atlia", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7a4b2fb5aaa99f5f795d2aa898508681eae26222.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": 1330444, "full_name": "Edmond Niu", "title": "Founder", "founder_bio": "Co-founder @ Atlia, enabling properties to be self-managing - continuously learning and improving themselves.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/edmond-niu/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/eefe6358ce17774a69778c1a39a2e87514240345.jpg" }, { "user_id": 1730500, "full_name": "Shaan Yadav", "title": "Founder", "founder_bio": "Co-founder at Atlia, systems for properties to operate autonomously.\nPrev. gpus at Apple, brain-computer interfaces at Yale and computer architecture research at Duke.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/shaanyadav", "x_url": "https://x.com/TheShaan", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b80a700259f368cf6a3acdebd10f537a89c3427c.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 108283, "title": "Atlia: The AI-Native STR Property Management Company", "tagline": "The AI-Native STR Property Management Company", "body": "### TL;DR\n\n**Atlia is the AI-native short-term rental property management company.**\n\n**You give us your Airbnbs to manage, and you’ll take home 10-20% more while both you and your guests have a better experience.**\n\nLaunch Video:\\\n\n\n## The Problem\n\nShort-term rentals were sold as passive income. But in reality, every Airbnb is a tiny hotel and a nightmare of operations. Every reservation restarts the operational clock: prices must be adjusted, guests screened, messages answered, cleaners scheduled, access coordinated, supplies restocked, and maintenance handled, often overnight or between back-to-back stays.\n\nAnd in traditional short-term property management companies, operational failures are common, and they can quickly become very costly:\n\n* A missed cleaning becomes a bad review\n* A poorly screened guest becomes property damage\n* A neglected maintenance issue becomes an issued refund or lost booking\n\nOn top of these operational failures, the average management company takes 15–30% of rental revenue, often alongside cleaning, maintenance, and other fees.\n\nWe’ve been property managers for the past year and ran into the same lose-lose choice every owner faces: manage a 24/7 hospitality business yourself, or hand over a quarter of your revenue to a management company whose costs increase with every property it adds.\n\n## The Solution\n\nWe are building a new type of property management company, with infrastructure built for agents and human-computer-interaction from the ground up. By replacing the layers of coordination and operational overhead built into traditional management companies, we provide a much better service at a much lower fee.\n\nWe center around a simple premise: owners shouldn’t have to choose between good service and reasonable fees. With our model, **owners take home 10–20% more and guests have a more premium experience.**\n\n## Team\n\nWe’re Shaan and Edmond, longtime friends, Duke roommates, and property managers.\n\nEdmond previously built AI voice agents at Rilla, LLM agents at S&P Global, and data pipelines at Vanguard. He has been managing rentals for a year, experiencing the owner’s dilemma firsthand.\n\nShaan previously designed GPU hardware at Apple, brain-computer interface research at Yale, computer architecture research at Duke. He also has been managing Airbnbs and longer-term rentals for a year and a half.\n\n## The Ask\n\nWe are live in 80 properties today, and would love to have you join us.\n\n**If you own a short-term rental** (such as an Airbnb), we can help you generate more revenue while providing better experiences to guests and giving you that peace of mind that your property is in good hands.\n\n**If you are considering investing in a short-term rental,** we can help turn your investment into a much more lucrative investment vehicle.\n\nYou can reach out to us at [founders@atlia.com](mailto:founders@atlia.com). And schedule a free property review at [www.atlia.com](http://www.atlia.com).", "url": "https://www.ycombinator.com/launches/SAV-atlia-the-ai-native-str-property-management-company", "votes": 11, "created_at": "2026-07-29T17:19:12.682Z" } ] }, { "id": 31375, "name": "Hub", "slug": "hub", "former_names": [ "Hub", "Hub.xyz" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4807c17f08dbbfbdd8f51297746a838ba6db58c1.png", "website": "https://hub.xyz", "all_locations": "San Francisco, CA, USA", "long_description": "Hub is a multimodal lab providing real world data to frontier labs and robotic companies.", "one_liner": "Real-world training data for frontier AI labs and robotics.", "team_size": 40, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1773429014, "tags": [ "Robotics", "Crowdsourcing", "Big Data", "Infrastructure", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Spring 2026", "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/hub", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/hub.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/hub-xyz", "x_url": "https://x.com/hubdotxyz", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/4807c17f08dbbfbdd8f51297746a838ba6db58c1.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": 3369916, "full_name": "Armin Kiani", "title": "Founder/CEO", "founder_bio": "CS & robotics. \nMicrosoft Innovation Award, 2020. \nWorked at Freedelity, Belgium's largest consumer-data platform.\nspeeding embodied agi with hub.xyz", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/armin-k-3439033ab/", "x_url": "https://x.com/xarmin", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/cfe1e5dd284cd517f46c254fc8c238c747cc1666.jpg" }, { "user_id": 3035977, "full_name": "Tim Sprecher", "title": "Founder/COO", "founder_bio": "Co-founder @ Hub, real-world training data for frontier labs.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/tim-sprecher-2a705220a/", "x_url": "https://x.com/tim404x", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/89d71ffcde838d447280bf34120d31b53f88d279.jpg" } ], "jobs": [ { "id": 99443, "title": "Field Operator - Brazil", "url": "https://www.ycombinator.com/companies/hub/jobs/uJPqDL4-field-operator-brazil", "location": "SP, BR / State of São Paulo, BR / São Paulo, SP, BR / São Paulo, State of São Paulo, BR / Remote (SP, BR; State of São Paulo, BR)", "type": "Contract", "role": "Operations", "role_type": null, "salary_range": "$100 - $2K / monthly", "equity_range": "", "experience": "1+ years", "visa": "US citizenship/visa not required", "skills": [] }, { "id": 114525, "title": "Machine Learning Engineer", "url": "https://www.ycombinator.com/companies/hub/jobs/ghznA3v-machine-learning-engineer", "location": "Paris, IDF, FR / Paris, Île-de-France, FR", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$90K - $120K", "equity_range": "0.02% - 0.20%", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [] } ], "news": [], "launches": [ { "id": 103084, "title": "Hub: real-world training data for frontier AI labs and robotics", "tagline": "Leveraging a global human network to collect data that don’t exist yet.", "body": "Hub provides real-world training data to frontier AI labs and robotics companies. We went to 7-figure revenue agreement on data delivery during the last weeks, working with some of the biggest names in AI and robotics.\\\n\\\n**Why now**\n\nThe gap between frontier models is closing, and internet-scale data is largely exhausted. The only edge left is real-world data that humans have to produce. Every lab now wins or loses on its edge cases, and edge cases need specific data captured by specific people: data that doesn't exist anywhere online. Whoever can go collect the right data first ships the better model first. Human labor is half of global GDP, and almost none of it has been recorded yet.\n\n**What Hub does**\n\nWe capture this data through a global network of humans, across all continents. We route and activate the exact people who fit a lab's brief and deliver in days, not the months it takes to source a generic dataset off the shelf. Because we collect the data ourselves, we can attest its provenance, which you can't do for data you didn't capture. Our ML and QA pipelines then verify and enrich it, so we meet the hardest dataset requirements labs throw at us. The moat isn't the data, it's how fast we can go get it.\n\n**Traction**\n\nFrom 5-figure to 7-figure in revenue agreement on successful data delivery. We now work with some of the biggest frontier AI labs and robotics companies in the world.\n\n**The ask**\n\nTwo asks: —> You're building frontier models or robots and data is your constraint. \\\n—> You're a top robotics engineer, a PhD whose research touches robotics or egocentric data, or someone with deep experience at a leading robotics lab, we're hiring. \\\n\\\nReach out here: [founders@hub.xyz](mailto:founders@hub.xyz).", "url": "https://www.ycombinator.com/launches/Qoe-hub-real-world-training-data-for-frontier-ai-labs-and-robotics", "votes": 1, "created_at": "2026-06-15T14:57:21.570Z" } ] }, { "id": 32157, "name": "Maingen", "slug": "maingen", "former_names": [ "Maingen AI", "Maingen", "Maingen AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/2eb59fa1eb6f59fafb938607c641276931a20aef.png", "website": "https://www.maingen.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Maingen builds simulations for industrial operations ($5 trillion slice of the US economy) so frontier labs can train models that actually run power plants, factories, and datacenters.", "one_liner": "Simulating Agents Running Industrial Companies", "team_size": 2, "industry": "Industrials", "subindustry": "Industrials -> Energy", "launched_at": 1784688239, "tags": [ "B2B", "Energy", "Big Data", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "Industrials", "Energy" ], "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/maingen", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/maingen.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/maingen", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/2eb59fa1eb6f59fafb938607c641276931a20aef.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Harshita Arora", "url": "https://www.ycombinator.com/people/harshita-arora" }, "founders": [ { "user_id": 1925382, "full_name": "Phillip Yan", "title": "Founder/CEO", "founder_bio": "CEO at Maingen (S26). Prev. 0x, power trading, Scale AI, Coinbase (YC S12). \nShipped exchange infra powering $150M+ TPV", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/pyan/", "x_url": "https://x.com/PhillipYan", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ecf7bdd1531c6e539582b01d63661a59e9e880c5.jpg" }, { "user_id": 2362549, "full_name": "David Yang", "title": "Founder/CTO", "founder_bio": "CTO of Maingen (S26). Prev. Idler (S25), Scale AI, Reticular (F24), and Amazon.\nCited by Anthropic for ICLR2025 mech-interp paper.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/david-yang-ai/", "x_url": "https://x.com/djyang1", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4a0c350ff34675efa324bcf4a043c0999f005df3.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 109472, "title": "Maingen: Simulations of real industrial operations, so agents can run the physical world", "tagline": "Long horizon RL environments for the $5 trillion industrial economy", "body": "Hello everyone!\n\nPhillip and David here, founders of Maingen (YC S26).\n\n**TLDR: AI is moving to the industrial world. Maingen builds RL environments for industrials (a $5 trillion slice of the US economy) so frontier labs can train models that actually run factories.**\n\n**Problem**:\n\nWe believe the next capabilities frontier is the physical world: operating factories, power plants, and industrial infrastructure. But that data doesn't exist on the internet, but rather as tacit knowledge inside enterprises, on operations desks, and in CMMS systems and vendor portals.\n\n**What we do:**\n\n\n\nWe embed with operators in these industries and translate their decision making process into RL environments labs can train.\n\n**Our first public benchmark evaluates whether agents can run a solar portfolio [solarbench.maingen.ai](http://solarbench.maingen.ai).** We’re continuing in other areas like factories, and we’re already working with frontier labs.\n\n**Who we are**\n\nThe founders Phillip Yan and David Yang have previously built RL environments, worked in the energy industry, shipped software powering 150 million+ TPV, and have been cited by Anthropic.\n\n**Get Involved**\n\nMaingen is looking for researchers and industrial operators to help shape its next environments. We’d love to hear from you if you:\n\n1. Work on research on long-horizon **agents, RL environments, or physical-world capabilities**.\n2. Run operations at an industrial site or can introduce the team to someone who does\n\nReach out at [founders@maingen.ai](mailto:founders@maingen.ai)! We're always down for a coffee!\n\nThanks,\n\nPhillip and David", "url": "https://www.ycombinator.com/launches/STg-maingen-simulations-of-real-industrial-operations-so-agents-can-run-the-physical-world", "votes": 5, "created_at": "2026-08-07T16:00:00.073Z" } ] }, { "id": 32191, "name": "Lyon", "slug": "lyon", "former_names": [ "Lyon (Branding TBD)" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/27ce3fff3af03da64bc8cfefeb3efef7b6d080b7.png", "website": "https://lyon.so", "all_locations": "San Francisco, CA, USA", "long_description": "Lyon builds private foundation models for banks, insurance companies, and fintechs. 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