[ { "id": 24893, "name": "Exa", "slug": "exa", "former_names": [ "Exa Search", "Metaphor" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9a54e5c11b9f296fb3be7980c9aaaf6ddd2f0c8c.png", "website": "https://exa.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Exa is a search engine built for AIs. We enable developers to filter the internet's content with complex queries in order to bring the world's knowledge into their AI apps.", "one_liner": "Web search rebuilt for LLMs", "team_size": 75, "industry": "B2B", "subindustry": "B2B", "launched_at": 1628280127, "tags": [ "Developer Tools", "Search", "AI", "APIs" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "B2B" ], "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/exa", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/exa.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/exa-ai/", "x_url": "https://twitter.com/ExaAILabs", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/9a54e5c11b9f296fb3be7980c9aaaf6ddd2f0c8c.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/4ec722b1e7f5be34ef6b1ecef005e23c18d8f229.png" ], "primary_partner": { "name": "Nicolas Dessaigne", "url": "https://www.ycombinator.com/people/nicolas-dessaigne" }, "founders": [ { "user_id": 779813, "full_name": "William Bryk", "title": "Founder/CEO", "founder_bio": "I grew up in NYC, then studied CS and physics at Harvard, where I did some ML research and led the robotics club. Then I was a software engineer at ML startup Cresta, while also writing a book about the history of civilization in my spare time. When I realized the future of civilization depends almost entirely on the quality of information we consume, I left to start Exa.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/william-bryk", "x_url": "https://twitter.com/WilliamBryk", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7eebec32bed52e52dc4a67b4ee21d53a67ccff2f.jpg" }, { "user_id": 269220, "full_name": "Jeffrey Wang", "title": "Founder", "founder_bio": "2023-now: Exa.ai\n2019-2022: Plaid\n2015-2019: Harvard CS+Philosophy\n\n- Obsessed with new AI tools, productivity\n- Surf, read, cook for friends, when I have time", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/wangzjeff/", "x_url": "https://x.com/wangzjeff", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/41fd29bfb7236e6c9b5a8efc4a73549f661d9149.jpg" } ], "jobs": [], "news": [ { "title": "Andreessen-Backed AI Search Startup Exa Valued at $2.2 Billion - Bloomberg", "url": "https://www.bloomberg.com/news/articles/2026-05-20/andreessen-backed-ai-search-startup-exa-valued-at-2-2-billion", "date": "May 20, 2026" }, { "title": "Exa: Organizing the World’s Knowledge", "url": "https://www.youtube.com/watch?v=9ZQP7PfHnuI", "date": "Sep 03, 2025" }, { "title": "Benchmark Bets Big on Exa, Which Wants to Be Google for the AI Era - Bloomberg", "url": "https://www.bloomberg.com/news/articles/2025-09-03/benchmark-bets-big-on-exa-which-wants-to-be-google-for-the-ai-era", "date": "Sep 03, 2025" }, { "title": "Exa raises $17M from Lightspeed, Nvidia, Y Combinator to build a Google for AIs", "url": "https://techcrunch.com/2024/07/16/exa-raises-17m-lightspeed-nvidia-ycombinator-google-ai-models/", "date": "Jul 16, 2024" } ], "launches": [] }, { "id": 25058, "name": "Andi", "slug": "andi", "former_names": [ "LazyWeb", "Andi", "LazyWeb" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/8c2b55aa5e7689ba35628ff2ed6299db9328d83c.png", "website": "https://AndiAI.com", "all_locations": "San Francisco, CA, USA", "long_description": "Instead of just links, Andi AI gives you answers - like chatting with a smart friend. \r\n\r\nAndi is a new type of search engine that uses generative AI and language models combined with live data to generate factually correct answers to questions, and to explain and summarize information combined from the best sources. \r\n\r\nAndi works as a search chatbot that's ad-free and privacy-focused. Ask Andi to...\r\n\r\n✅ Answer questions\r\n🧠 Find, summarize and explain information\r\n🔍 Navigate and search the web\r\n\r\nSearch is broken because of SEO spam, ads, and surveillance capitalism. Andi cuts through the clutter to get you straight to the knowledge you need. It presents results visually in a way that's easy to understand with a simple chat interface, and protects you from ad tech and distraction.\r\n\r\nYou can try the search app at https://andisearch.com", "one_liner": "AI Search for the Next Generation", "team_size": 2, "industry": "Consumer", "subindustry": "Consumer -> Content", "launched_at": 1643049274, "tags": [ "Generative AI", "Consumer", "API", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Active", "industries": [ "Consumer", "Content" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": true, "demo_day_video_public": true, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/andi", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/andi.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/andisearch/", "x_url": "https://twitter.com/andi_search", "fb_url": "https://www.facebook.com/andisearch", "cb_url": "https://www.crunchbase.com/organization/andi", "github_url": "https://github.com/andisearch/", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/8c2b55aa5e7689ba35628ff2ed6299db9328d83c.png", "app_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/bf-public_profile/25114/app_video.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQC4NIECAOYE2QNZ5%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022701Z&X-Amz-Expires=60&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHEaCXVzLXdlc3QtMiJIMEYCIQDZr7mht6VpZroejzIM4ovdqqWjmh9u8AZ4wJ%2BcxZeHaQIhAN0TyP1C5dfPpTpdf3vcwz%2B044IBUbnJ55rbsOHYRWQWKuUDCDoQABoMMDA2MjAxODExMDcyIgxycpAO09Mq3A1cvyQqwgN39qg2Ox70YWArW8Z19bx1LbIV2O3f8r7xSc%2BDpl3XtmT02s9DHm%2FdyEMimOoBk5McFrR%2FThfgO9h1Zcmt1NqN1y6d1lWIsDAdluAWIqXM089dX1KVjFmsSwg8hWS2%2F4ysZFTLuD5VBUHeANJPyfmRKN9Kb4JepbPSE01eipdz4aqPxRqz%2FVUJEYLUTh5YJM6jJKNzl5l2mnWSSFiFdUfT5OS16LHDCYH4PGt9WRY2iseRxMu7FZcICWM0P8aN14WLxKsu1H0yVxk8LS%2BNbXezGhQnkMsa3bf7aMNU7x4TSs7KyM01QgiCZxC01YV43U4O8vFENPThr%2FHtKaAvtZ0%2BXSTwUE691O8mG15thNVkorInjIhilxdazy%2BzEYcGsAMNZtfx0Bd5Xn43Xf7b%2Fie4vv0d0RCHqbCGLmxRHj5VG3Pdta5Kju3%2Fpb8n%2FKmRjY%2FtuIfi9oTYAjc4PVyikHTm8UXURiHmaM0tqXZjxyh0iIponEKehSmtjmjudiXLWICFBoPhEXeaUeyK2AT6btvH4fclsQa0qRmkxdgguiWvrYxLKhNNfPLTc5QA0jlPSRoDJ%2F%2BxNHLFrGusOX7IwU6hfRMw6P3a1gY6pAETos2BWzWJY973oh8s1gy0zFzSYQP7N%2B39nIjIA7I0ymOVnRNJktoZRF6GV9DH3ymBPT2fnMgKvEaFAqVR%2F0WVwXz3ixgSwFGrvlwPr39cuFnKBEcdnivQJOXkexjqUxi9alVn76GV8ONTXD1ZupAQeLYI09gLaaAmJlyJPRAUCybD64XlECdPqS2TV2e%2FZlA5duJD1GekpDGthoc0y8PIc1ahwA%3D%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=a67d15d7135730927b7d931a8f1ccfefd8492a68b7015ded2e75f658912c9bce", "dday_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/video_event/56/1648752205787-vezlyf3wgxo-e16cfd4aded8c33e8b3d46c0291fbd14/569897ba97459676b43f1f4f8e770cf2.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQC4NIECABPQ2AHNW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022701Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=a484a6922b9874eabd1ab0a47fa0ceeb419acc924228635ff8e5ab99f6ef69f4", "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 673843, "full_name": "Angela Hoover", "title": "Founder", "founder_bio": "Angela (CEO) is from a totally non-traditional founder background. She's a community college dropout who went from construction laborer to working with Microsoft on Azure, before launching Angie's Travels, a travel influencer startup. She started Andi after going through YC Startup School as a 23-year-old non-technical female founder with the idea for a \"not searching\" engine for Gen-Z.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/angela-hoover/", "x_url": "https://x.com/angelahoover_", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3151a886c4f9b867f43adcbb67cc74d888598784.jpg" }, { "user_id": 19139, "full_name": "Jed White", "title": "Founder", "founder_bio": "Jed (CTO) has been programming since he was 8, and sold his first software aged 12. He studied computer science (USyd-part/USFCA), and was editor-in-chief of Australia's top computer magazine at 23, before making a popular start page with 3m users, and building a media search and personalization startup. He has 20+ years experience in search and AI.", "is_active": true, "linkedin_url": "http://linkedin.com/in/jedwhite", "x_url": "https://x.com/jedwhite", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ac775aafdd7712bff597911e958a1ce9d95f5d26.jpg" } ], "jobs": [], "news": [ { "title": "Bye, Google: These 3 AI search engines help you find stuff faster - Fast Company", "url": "https://fastcompany.com/91161996/best-ai-search-engines", "date": "Aug 14, 2024" }, { "title": "4 AI Search Engines I Use Every Day", "url": "https://www.howtogeek.com/4-ai-search-engines-i-use-every-day/", "date": "Jun 29, 2024" }, { "title": "google under fire", "url": "https://research.cbinsights.com/google-under-fire", "date": "May 14, 2024" }, { "title": "How I do it: Jed White, Andi AI search - Flashes & Flames", "url": "https://flashesandflames.com/2024/04/12/how-i-do-it-jed-white-andi-ai-search/", "date": "Apr 11, 2024" }, { "title": "Best AI Search Engines To Try Right Now", "url": "https://www.searchenginejournal.com/ai-search-engines/497061/", "date": "Nov 15, 2023" } ], "launches": [ { "id": 73788, "title": "⭐️ 🔍 Andi - Making search fun, factual and interesting", "tagline": "Andi uses generative AI and beautiful content to make search more interesting, engaging and useful", "body": "TL;DR: Andi is **search for the next generation** using generative AI - visual, conversational and factually accurate. Instead of just links, [**Andi**](https://andisearch.com/) gives you **answers** in a fun and friendly way - like chatting with a smart friend.\n\nHey YC fam, we’re Angela and Jed and we’re building [Andi](https://andisearch.com/)!\n\n![uploaded image](/media/?type=post&id=73788&key=user_uploads/673843/6e575206-0716-407f-ba62-8228d473165c)\n\n### **✖️** The Problem\n\nWe all know search is broken, but it also has massive potential. Today, it’s full of ads, misinformation and SEO spam. With AI-powered search and chatbots the problem will get even worse, because LLMs make things up and bury the sources, and a new era of sponsored answers threatens to destroy trust online.\n\nWhat makes the web great is the high quality content that many producers spend hours researching, writing and creating. Google sells ads on their content without giving fair attribution or promotion. Now LLM-based chatbots give even less credit, or make sources up. Users have to spend more time fact-checking than searching.\n\n### **✅** Our Solution\n\nWith Andi, we want to make search 100x better. We’re building **practical** and **helpful** AI that is largely invisible to the user. We set a high bar on answer speed and accuracy and only show generated answers that have very high confidence. We focus on getting the building blocks like factual summaries and explanations right. We highlight and present rich information direct from sites rather than little snippets.\n\nWe make searching the web fun, interesting, and easy to understand. Our goal is to save you 15 - 20 mins by providing direct answers to questions, but only when they are _right_. Andi is **ad free**, **private** and **anonymous**, so you can feel safe online.\n\n![uploaded image](/media/?type=post&id=73788&key=user_uploads/673843/ceba85a8-d52d-4461-a816-708dff488213)\n\n### **🙌** What’s New\n\n📷 **Stunning visual results**: Search is more interesting, fun and useful with visual results that highlight the beauty and richness of content online, and enhance search with the meaningful information they contain.\n\n🤓 **Change your view**: Change how you see results based on what you need or your mood - with an entertaining **Feed**, an info-packed **Grid**, or simple **List** views. Results take full-advantage of large screens.\n\n![uploaded image](/media/?type=post&id=73788&key=user_uploads/673843/f1fc2871-7d14-462a-a149-c36a90f56a23)\n\n⚡ **Summarization**: Andi uses AI to select the best and most useful content from websites to display on results cards, generating short meaningful summaries for each. A **Summarize** button on every result gives you a short 200 word summary of the full content.\n\n🤯 **Explanations**: Understand complex topics with an **Explain** button on each result card that explains the content like you're in school.\n\n🪄 **Generate**: Get factually grounded and accurate tutorials, overviews, reports or other written content using the **Generate Text** button.\n\n![uploaded image](/media/?type=post&id=73788&key=user_uploads/673843/1e476f6b-177c-427b-ba23-c41c8828ecd0)\n\n### 🙏🏼 Asks\n\nTry it now at [andisearch.com](https://andisearch.com).\n\nIf you like it, please share it with your friends! 🙏🏼\n\nIf you want to use it on mobile, download the Progressive Web App! Instructions [here](https://aureatelabs.com/blog/install-pwa-to-device/).\n\nPlease join our community on [Discord](https://discord.gg/andi) 💙", "url": "https://www.ycombinator.com/launches/JC8-andi-making-search-fun-factual-and-interesting", "votes": 366, "created_at": "2023-08-03T21:27:17.152Z" }, { "id": 64519, "title": "⭐️ 🔍 Andi - Search for the next generation", "tagline": "Andi is like a smart friend who answers complex questions simply, and protects you from spam, ads and tracking 🤖 ", "body": "### TL;DR Instead of a page of blue links, Andi gives you answers. It uses language models combined with live data to answer questions and summarize information based on the top sources.\n\nHey Fam, we're Angie and Jed and we're building [Andi](http://andisearch.com/). We're on a mission to unbreak the Internet and save the world from spam and ad tech.\n\n### **❌ The Problem**\n\nIt’s become harder to find what you need online. Search is broken because results are overwhelmed with ads, clickbait, and low-quality content marketing. Even when the information you need is in there somewhere, you have to trawl through pages of links to content often buried behind ads and paywalls.\n\n---\n\n## **✨ Our Solution**\n\nAndi is a new type of search engine with an intelligent AI assistant that answers complex questions directly. It has a simple chat interface with visual cards. It's like messaging a smart friend who acts as an intermediary to online knowledge, and keeps you safe and productive online.\n\n### ⚙️ **How it works**\n\nAndi combines large language models with live data, reasoning and common sense. It's like a search of vertical searches and APIs. We use AI to understand questions and predict the best places to find answers, applying blacklist and quality filters. We retrieve and use live content in real time with semantic search ranking, rather than returning outdated information from stale models or a static keyword index. \n\n## **💙 Community fave features**\n\n👉 Get direct answers to questions\n\n![uploaded image](/media/?type=post&id=64519&key=user_uploads/673843/4fe6c540-657b-4299-a362-a39ebd481748)\n\n👉 Visual results (a picture is worth 1,000 words!)\n\n![uploaded image](/media/?type=post&id=64519&key=user_uploads/673843/5723b727-30f2-4473-b9fb-46b3376c9cf8)\n\n👉 Reader mode: See content free from ads and clutter\n\n![uploaded image](/media/?type=post&id=64519&key=user_uploads/673843/2f8a8522-6c43-4880-a013-bfb0e7e6dcb1)\n\n### **🙏 Asks**\n\nTry Andi! Start searching [here](https://andisearch.com/). Since we're anonymous, we rely on our users giving us feedback when Andi gets answers wrong to improve. Type /feedback to tell us what you think. If you like it, [make it your default search engine](https://andisearch.com/default/) for a week or install the progressive web app!\n\nJoin us on [Discord](https://discord.gg/qcCcrbMuex), or DM us on [Twitter](https://twitter.com/andi_search)!\n\nIf you have 10 minutes to chat about Andi, please book a time with me [here](https://calendly.com/angela-2022/10-minute-meeting) 😊", "url": "https://www.ycombinator.com/launches/Gmd-andi-search-for-the-next-generation", "votes": 508, "created_at": "2022-07-11T17:35:27.336Z" } ] }, { "id": 25306, "name": "Capuchin", "slug": "capuchin", "former_names": [ "WorldQL" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ea9d3b72b1fa93a8d4352fa71bae133957dcb7a7.png", "website": "https://capuchin.ai/", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "", "one_liner": "Search and automation for engineer-to-order companies", "team_size": 5, "industry": "B2B", "subindustry": "B2B", "launched_at": 1648654841, "tags": [ "Manufacturing", "Defense", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Active", "industries": [ "B2B" ], "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/capuchin", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/capuchin.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ea9d3b72b1fa93a8d4352fa71bae133957dcb7a7.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": 859638, "full_name": "Jackson Roberts", "title": "Founder", "founder_bio": "Software engineer. My interests include distributed systems (both computer and IRL), novel business models, and discovering new problems. CU Boulder '21.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jackson-roberts/", "x_url": "https://twitter.com/jacksondotsh", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/80066dd0452d0cffbba91d2412900a4241ea8303.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 25969, "name": "Shaped", "slug": "shaped", "former_names": [ "shaped.ai Inc." ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/241b1f1931647920258ef5ae6571461ada0b62f1.png", "website": "https://shaped.ai", "all_locations": "New York City, NY, USA", "long_description": "Connect your data. Train your models. Query text, user or session context and retrieve relevant results in milliseconds. Explore our case studies to see how we’ve helped leading brands drive significant engagement and revenue: https://www.shaped.ai/case-study. ", "one_liner": "The real-time retrieval engine for search, feeds, and agents.", "team_size": 25, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1646119340, "tags": [ "Search", "AI", "Recommendation System" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "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/shaped", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/shaped.json", "year_founded": 2021, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/shapedai", "x_url": "https://twitter.com/shapedai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/241b1f1931647920258ef5ae6571461ada0b62f1.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Brad Flora", "url": "https://www.ycombinator.com/people/brad-flora" }, "founders": [ { "user_id": 812951, "full_name": "Tullie Murrell", "title": "Founder/CEO", "founder_bio": "CEO of Shaped. \nPreviously an AI researcher at Facebook AI Research (FAIR).\nCreator of PyTorchVideo & original core contributor to PyTorch Lightning.\nPassionate about anything AI, machine-learning and statistics.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/tullie/", "x_url": "https://twitter.com/tulliemurrell", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0b422fcadf175caaedc3de64e8d33a973558dd6e.jpg" } ], "jobs": [], "news": [ { "title": "Shaped Raises Series A, Launches Self-Serve Platform to Power the Future of AI-Driven Recommendations & Search | Shaped Blog", "url": "https://www.shaped.ai/blog/shaped-raises-series-a", "date": "Jul 17, 2024" } ], "launches": [ { "id": 97225, "title": "ShapedQL: The SQL engine for search, feeds, and AI agents", "tagline": "Connect your data. Train your models. Query text, user or session context and retrieve relevant results in milliseconds.", "body": "Hi everyone 👋\\\n\\\nI'm Tullie, the founder of [Shaped](https://www.shaped.ai/).\\\n\\\n**TL;DR**\\\n\\\nToday we’re launching ShapedQL, a new language purpose built for agents, feeds and search. We built ShapedQL because we realized that while retrieval has become easier (thanks to Vector DBs), ranking and relevance are still incredibly hard.\\\n\\\nLaunch video: \\\n\\\n**Problem ❌**\\\n\\\nMost engineering teams we talk to are stuck maintaining a \"Frankenstein\" stack. To build a \"For You\" feed or give an AI Agent personalized memory, they have to glue together a vector database, a feature store (like Redis), a reranking service, and thousands of lines of Python spaghetti code.\\\n\\\n**Solution ✅**\\\n\\\nWe built ShapedQL to turn that \"house of cards\" into a single interface.\n\n![uploaded image](/media/?type=post&id=97225&key=user_uploads/812951/eed67b43-7c3b-4b0f-819a-5bbd31db2732)\n\nShapedQL is a domain-specific SQL dialect that compiles down to a high-performance, multi-stage ranking pipeline. With a single query, you can define the four stages of modern relevance:\\\n**1. Retrieve:** Fetch candidates from multiple sources (Hybrid Search, Collaborative Filtering, Trending).\\\n**2. Filter:** Apply hard constraints (e.g., \"in stock\" or \"within 50 miles\").\\\n**3. Score:** Rank results using real-time ML models (optimizing for clicks, purchases, or watch time).\\\n**4. Reorder:** Enforce diversity so your users (or Agents) don't see the same 5 items repeatedly.\n\nWe're seeing teams reduce 2,000+ lines of maintenance code down to \\~30 lines of ShapedQL, while shipping features like \"Cart Upsell\" or \"Conversational Recommendations\" in days instead of months.\n\nIf you're not a fan of SQL we also have Python or Typescript SDKs, perfect for a real-time production integration.\n\n![uploaded image](/media/?type=post&id=97225&key=user_uploads/812951/d3cc12cc-3c74-448b-adf0-ae65d9359b79)\n\n**Ask 💬**\\\n\\\n**1.** We have a Product Hunt launch at this link: . Any support or and upvote would be greatly appreciated!\\\n\\\n**2.** Please try out our interactive demo at [playground.shaped.ai](https://playground.shaped.ai), we’d love to hear any feedback on the new language. Were you able to easily make a query? Do the docs make sense? What is it missing?\\\n\\\n**3.** If the problems above resonates and you’re struggling to get true relevance from your vector store or current retrieval system, please get in touch! Book on this calendar here: and mention you’re from YC. I’ll make sure to jump on the call to help you out myself.\n\n![uploaded image](/media/?type=post&id=97225&key=user_uploads/812951/bb70b2b7-780a-4da6-a13f-511015e09776)\n\n", "url": "https://www.ycombinator.com/launches/PI9-shapedql-the-sql-engine-for-search-feeds-and-ai-agents", "votes": 15, "created_at": "2026-01-28T00:44:48.253Z" }, { "id": 88281, "title": "Shaped — Value Modeling for Search and Recommendation Systems", "tagline": "A control panel for your business objectives", "body": "# **TL;DR**\n\nWe’ve built Shaped Value Modeling, a framework that enables real-time optimization of recommendation and search ranking models for multiple objectives. It allows teams to blend different objectives (e.g. relevance, conversions, diversity) and dynamically run AB tests that drive business outcomes (with built in analytics tooling). Our community has seen awesome results, so we’re excited to bring this framework to the masses!\n\nWatch our demo [here](https://youtu.be/gYLmtMR-N4I).\n\n**Key Features:**\n\n* **Multi-objective optimization:** Combine multiple models and signals (e.g., click prediction, purchase likelihood, content quality) into a single, customizable scoring function.\n* **Dynamic ranking logic:** Define and adjust ranking objectives as your business needs change using a simple python DSL.\n* **Real-time API:** Modify ranking scores on the fly with simple API calls, enabling rapid experimentation and A/B testing.\n* **Easy to integrate:** Connect and deploy rapidly with direct integration to your existing data sources.\n* **Explainability:** Gain insights into why certain items rank higher through interpretable scoring logic, and a built in analytics platform out of the box.\n\n![uploaded image](/media/?type=post&id=88281&key=user_uploads/857360/543b423e-521a-491d-808b-0bd914f06c23)\n\n**Why This Matters:**\n\n* Rapid iteration: Experiment with new ranking strategies and adapt to changing business needs without lengthy retraining cycles.\n* Improved control: Fine-tune your ranking algorithms with precision and transparency.\n* Better alignment: Optimize for multiple KPIs simultaneously, ensuring your recommendations drive both engagement and business value.\n\n**How it Works:**\n\n1.Train separate models for different objectives (e.g., LightGBM for click prediction, BERT4Rec for purchase likelihood).\n\n2\\. Define a \"value model\" that combines these models using mathematical expressions and custom logic. Let's say we have a lightgbm click-through-rate model called ctr_model and a transformerd based next purchase model called purchase_model, you can create an ensembled value model as follows:\n\n```\nvalue_model = 0.5 * ctr_model + 0.5 * purchase_model + log(item.quality_score)\n```\n\n3\\. Dynamically adjust the value_model via API calls to optimize for different goals or user segments:\n\n```\n{\n  \"user_id\": \"83NSLX\",\n  \"config\": {\n    \"value_model\": \"0.3 * ctr_model + 0.7 * purchase_model\"\n  }\n}\n```\n\n\\\nHow our customers are using value modeling:\n\n* **Marketplaces:** Dynamically mix personalized scores with a geo-location penalizer to connect users with nearby items.\n* **E-commerce:** Balance purchase intent, engagement, and product quality.\n* **Content platforms:** Implement time-decay, boost high-quality content, and personalize recommendations.\n* **Social media:** Dynamically switch between personalized and trending models.\n\n# **Our ask**\n\nIf you know decision-makers in any of these types of organisations, we’d be super grateful for an introduction at [daniel@shaped.ai](mailto:daniel@shaped.ai)\n\n1. Marketplaces\n2. E-commerce\n3. Content platforms\n4. Social Media\n\nFor more information see our website [here](https://www.shaped.ai/value-modelling) and developer docs [here](https://docs.shaped.ai/docs/model_creation_guides/value-modeling)\n\n[Get a demo](https://meetings.hubspot.com/matt2900/meet-with-shaped-?uuid=4076ed73-5725-4ee7-ac01-2e1869237a18) if you’re interested in testing value modeling!", "url": "https://www.ycombinator.com/launches/Mxt-shaped-value-modeling-for-search-and-recommendation-systems", "votes": 8, "created_at": "2025-03-05T15:46:26.414Z" } ] }, { "id": 26808, "name": "Phind", "slug": "phind", "former_names": [ "Hello Cognition", "Hello", "Phind", "phind.com" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/8151bfb74b7fe2072291d1efc343fcf50f977bec.png", "website": "https://phind.com", "all_locations": "San Francisco, CA, USA", "long_description": "Phind is an AI search engine. It uses multi-step reasoning to find the best answer for your question and generative UI to present it in the most beautiful and interactive way.", "one_liner": "AI search engine", "team_size": 4, "industry": "Consumer", "subindustry": "Consumer", "launched_at": 1654731943, "tags": [ "Generative AI", "Search", "AI", "AI Assistant" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Inactive", "industries": [ "Consumer" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/phind", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/phind.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/phindsearch", "x_url": "https://twitter.com/phindsearch", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/8151bfb74b7fe2072291d1efc343fcf50f977bec.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 984097, "full_name": "Michael Royzen", "title": "Founder", "founder_bio": "Founder & CEO at Standard Signal (YC P26), building the first hedge fund where AI runs everything from research to the actual trade execution end-to-end.\nPreviously co-founder & CEO at Phind (YC S22), where I built the first AI search engine and trained LLMs that beat GPT-4 on HumanEval (2024) and had the #1-ranked open-source coding model on Hugging Face for 6 months in 2023.\nUT Austin Turing Scholar '22\nPreviously an undergrad NLP researcher, as well as ML at Lyft, Cloudflare, and Microsoft.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/michaelroyzen/", "x_url": "https://twitter.com/michaelroyzen", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/706f8f788e198ff32880b7ca40d15459d1f60a6e.jpg" }, { "user_id": 1063355, "full_name": "Justin Wei", "title": "Founder", "founder_bio": "UT Austin Turing Scholar '22\nPreviously founder at Phind.com", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/justinawei/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a72df97582d39522da4f3340edcc02a577a2b421.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 27042, "name": "Artemis Search", "slug": "artemis-search", "former_names": [ "Artemis", "Artemis Labs", "Umbria (by Artemis Labs)" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/002e53ddce5bb4318d71a6b7c48c2417fb3ad159.png", "website": "https://search-artemis.com/", "all_locations": "San Francisco, CA, USA; Rochester, MN, USA; Remote", "long_description": "Artemis Search is a vector database search with a twist. We use special-purpose deep-learning models instead of using textual / semantic similarity to evaluate how good a search result is.\r\n\r\nThis enables us to actually reason how well search results match the intent of the search query, eliminating the problems that come from evaluating search results on how much they look like the search query.", "one_liner": "A search technology that actually finds what you’re looking for.", "team_size": 3, "industry": "B2B", "subindustry": "B2B", "launched_at": 1656613108, "tags": [ "Machine Learning", "SaaS", "B2B", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "B2B" ], "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/artemis-search", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/artemis-search.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/search-artemis", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/002e53ddce5bb4318d71a6b7c48c2417fb3ad159.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 982880, "full_name": "Austin McCoy", "title": "Founder", "founder_bio": "Austin is a Caltech CS graduate with an emphasis in applied mathematics and finance. Austin has an academic background developing and publishing computer graphics technology and machine learning models at Caltech's Aerospace Robotics and Control (ARCL) laboratory. Austin also has experience launching and selling several small technology businesses ($100k - $1M ARR) centered around developing and selling B2B and B2C software products.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/austin-mccoy-833600229/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7e2b5d37a856a3a6ac6422a57220edd8c664fcaa.jpg" }, { "user_id": 1018952, "full_name": "Manvir Singh", "title": "Founder", "founder_bio": "Manvir is a self-taught software engineer who specializes in building robust and intuitive technology accessible to clients from all backgrounds. He has coordinated with his co-founder, Austin, on previous ventures and looks forward to bringing his experience to bear with Artemis.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/manvir-singh-abb298247/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5c1de568e5bc43f567f2cd40177ec5a5ca6092d6.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 75378, "title": "Umbria: Get designer-grade docs fast", "tagline": "Join us for a 24-hour YouTube livestream!", "body": "### 🖥️ The Product\n\nUmbria is the fastest way to launch designer grade docs!\n\n**Here’s how it works:**\n\n1. Drag and drop components\n2. Add your text\n3. Publish\n\nSee our own site & docs built with Umbria here: [Umbriadocs.com](http://Umbriadocs.com)\n\n### 🚀 The Pitch\n\nYou can get started for free — or contact Kyle for a special deal! ([kyle@umbriadocs.com](mailto:kyle@umbriadocs.com))\n\n### 👀 24 Hour Docs Livestream\n\nWe’re going to build docs for 24 hours straight on a YouTube stream next Wednesday (the 11th)!\n\nWhy? Becuase we can. Reach out for a shoutout, we’ll feature your docs!\n\n![uploaded image](/media/?type=post&id=75378&key=user_uploads/1018852/37abfcf5-e64c-4b6b-b81f-aa9e7a213169)\n\n", "url": "https://www.ycombinator.com/launches/Jbm-umbria-get-designer-grade-docs-fast", "votes": 5, "created_at": "2023-10-04T20:27:51.119Z" }, { "id": 66800, "title": "Artemis: Data Science Project Management", "tagline": "Document, track and visualize every aspect of your workflow in one place", "body": "**tl;dr** Artemis is the best platform to document, track, and visualize complex data science projects. We help teams log when and how they use resources (scripts, models, databases, etc.) to create the through yet compensable documentation for their projects.\n\n![uploaded image](/media/?type=post&id=66800&key=user_uploads/1018852/4e301d6d-975c-4497-8692-91a0834cdaba)\n\n**❌ The Problem:** Data science teams employ a variety of resources often spread across platforms. The way these resources interact in a project is often poorly documented. Documentation systems that do exist rely on direct peer-to-peer collaboration and produce results that are difficult to understand.\n\n**✅ The Solution:** Artemis is a platform for organizations to document and map every time a resource is used and how it works so projects can be understood at a glance.\n\n🏆 **Two Steps To Success:**\n\n**First,** Artemis teams create archives to document every time they use a resource. The Archive helps users document their resource’s location(s), relationships to other resources, and functions in addition to recording how they’re employed in a specific project. \n\n![uploaded image](/media/?type=post&id=66800&key=user_uploads/1018852/718ed567-d130-450d-a425-e25dfdf7c8ee)\n\n**Second**, resource deployments archived with Artemis are automagically mapped out to create interactive visual networks. These networks offer a comprehensive, one of a kind perspective that informs leadership decisions and helps individuals understand their work and their peers’ work in context.\n\n![uploaded image](/media/?type=post&id=66800&key=user_uploads/1018852/188a66ae-e107-4524-b084-54d62a18eec3)\n\n**▶️  Price and Contact**\n\nArtemis free to try for 30 days and free to academic institutions forever. Enterprise seats cost $20/month.\n\nIf you’d like to try Artemis or are interested to learn more, please reach out: [austinmccoy@artemisar.com](mailto:austinmccoy@artemisar.com)", "url": "https://www.ycombinator.com/launches/HNQ-artemis-data-science-project-management", "votes": 13, "created_at": "2022-10-18T22:04:26.831Z" }, { "id": 65058, "title": "Artemis: Retool for desktop applications", "tagline": "Drag-and-drop to make interfaces for any program", "body": "Hello, we’re [Austin](https://www.linkedin.com/in/austin-mccoy-833600229/), [Manvir](https://www.linkedin.com/in/manvir-singh-abb298247/) and [Kyle](https://www.linkedin.com/in/kyle-h-202515237/) — we founded [Artemis](https://artemisdevtool.com/). \n\n**_tl;dr_** _It takes too long to give desktop internal tools usable GUIs. Artemis fixes that._\n\n### **Problem:** \n\nArtemis was born in a Caltech robotics lab when our founder Austin realized too much time was spent giving internal tools usable GUIs. Building front-ends for internal tools written in programming languages like Python, C, C++ and Matlab is a tedious process that makes it slow and difficult to deploy even the simplest projects.\n\nExisting solutions aimed at solving this problem like Java’s Netbeans or Python’s QT only aid the user in designing how their interface will look, leaving the task of coding each element’s functionality. The complexity of these tools also present a high barrier to entry – especially for users who are just trying to make simple interfaces. These users don’t need all the bells and whistles of QT, they just need a simple solution they can learn and employ quickly.\n\n### **Artemis Solution:**\n\nArtemis’ drag and drop interface lets developers rapidly design front-ends comprised of elements that function out-of-box. This prepackaged functionality means integration is a breeze.\n\nHere’s how it works:\n\n1) Create your interface in our drag-and-drop webtool:\n\n![uploaded image](/media/?type=post&id=65058&key=user_uploads/1018852/93a29830-bbc4-4a98-a054-995f401064a4)\n\n2) Integrate into codebase with three steps (minimal overhead)\n\n![uploaded image](https://bookface.ycombinator.com/media/?type=post&id=65058&key=user_uploads/982880/bf607bd0-7015-4777-b264-72c6128ada94)\n\n3) Launch your new interface (runs in browser to facilitate cross platform use, but still works offline) \n\n![uploaded image](/media/?type=post&id=65058&key=user_uploads/1018852/c497ce1e-e1f0-4d6b-ab4d-f25340a53401)\n\n### **Project history, use cases, and pricing:**\n\n**History:** We began life as an augmented reality development platform that functioned in almost the same manner. However after realizing how small that market is, we’ve pivoted to a broader one. That being said: we remain so cross platform you can even build frontends to be displayed in AR and VR.\n\n**Use Cases:** Artemis is a must-have for anybody who builds internal tools. \n\nSome examples include: Machine learning dashboards • GUIs for engineering tools • Control panels for machinery & IoT devices • GUIs for physicists’ models and calculators • And whatever else you can build!\n\n**Pricing:** $9 a month after 1 week free trial", "url": "https://www.ycombinator.com/launches/GvK-artemis-retool-for-desktop-applications", "votes": 21, "created_at": "2022-08-02T04:40:30.973Z" } ] }, { "id": 28759, "name": "Branch AI", "slug": "branch-ai", "former_names": [ "Branch AI", "Apiflow" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a6abe17a364c1113dea922a968293da0024ff5af.png", "website": "https://www.branch-ai.com", "all_locations": "Singapore, Singapore", "long_description": "Boost conversions & organic SEO traffic by adding AI to your search box, unlocking personalized answers, suggestions, comparisons & more\r\n\r\nSolutions\r\n- Add AI Overviews to Search (Boost Conversions) \r\n- Save Answers as Landing Pages (Boost Organic SEO)", "one_liner": "AI Overview Search, as a Service", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Retail", "launched_at": 1708944337, "tags": [ "SaaS", "B2B", "Retail", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Inactive", "industries": [ "B2B", "Retail" ], "regions": [ "Singapore", "Southeast Asia" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/branch-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/branch-ai.json", "year_founded": 2023, "city": "Singapore", "country": "SG", "linkedin_url": "https://www.linkedin.com/in/govindchandrasekhar/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/a6abe17a364c1113dea922a968293da0024ff5af.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Aaron Epstein", "url": "https://www.ycombinator.com/people/aaron-epstein" }, "founders": [ { "user_id": 31801, "full_name": "Govind Chandrasekhar", "title": "Founder", "founder_bio": "Co-founder @ Branch AI (YC S23) | Previously co-founder @ Semantics3 (YC W13, acquired by Hearst Magazines) | ex Machine Learning lead @ Twitter (recommendations)", "is_active": false, "linkedin_url": "https://linkedin.com/in/govindchandrasekhar", "x_url": "https://www.twitter.com/govind201", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/bb54033e2d29b77652f6c478eedc476808bf1918.jpg" }, { "user_id": 3501, "full_name": "Ishan Agrawal", "title": "Founder", "founder_bio": "Co-founder @ Branch AI (YC S23). \nPreviously Group CTO @ Funding Societies (350M raised, scaled the company from Seed to Series D)", "is_active": false, "linkedin_url": "https://linkedin.com/in/ishanagrawal", "x_url": "https://twitter.com/ishanagr", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c2b27b14924455009bb079d1a560d26ce1a81ac5.jpg" } ], "jobs": [], "news": [ { "title": "Former Funding Societies CTO Ishan Agrawal Launches YC-Backed Branch AI - Fintech Singapore", "url": "https://fintechnews.sg/89305/ai/former-funding-societies-cto-ishan-agrawal-launches-yc-backed-branch-ai/", "date": "Feb 26, 2024" }, { "title": "Ex-Funding Societies CTO launches YC-backed ecommerce AI startup", "url": "https://www.techinasia.com/exfunding-societies-cto-launches-ycbacked-ecommerce-ai-startup", "date": "Feb 26, 2024" } ], "launches": [ { "id": 78740, "title": "Branch AI - State-of-the-art Ecommerce search, instantly", "tagline": "Powered by AI that learns your catalog and tunes itself to drive conversions", "body": "### **The Problem**\n\n* [**Search queries are becoming more conversational**](https://blog.branch-ai.com/second-order-effects-of-ai-search-from-keywords-to-intent-63b3b9fa20e8) (ChatGPT effect). However, most Ecommerce retailers & brands still use keyword search tools resulting in lost revenue for tail queries.\n* Search providers give you tools to build search engines, **but leave relevance engineering up to you** (i.e. tuning of the search).\n* Tuning search to maximize conversions is [**complicated & expensive**](https://blog.branch-ai.com/what-it-takes-to-build-effective-search-8a2901a9a943) (involves stitching together embeddings, rankers, NLP models, heuristics & annotated feedback).\n* DIY embeddings and models are easy to experiment with. But building **production-grade deployments requires catalog-specific tuning**, which can unlock 20-30% better accuracy & conversions.\n\n### **The Solution**\n\n![uploaded image](/media/?type=post&id=78740&key=user_uploads/31801/a4c105e8-a6a6-41f0-977c-e09de9b09233)\n\n**Step 1 - Upload/Connect Catalog:** Upload a list of products or connect to an existing search database or platform (e.g. Elastic or Shopify).\n\n**Step 2 - LLM-powered Tuning**: This happens under the hood automatically. We use LLMs to train catalog-specific embeddings, rankers & query rewriters, bootstrapped from our proprietary Ecommerce-specific models.\n\n**Step 3 - AI APIs**: With zero effort, businesses get:\n\n1. _Search API_: Query → Ranked Products (for end-to-end query rewriting, hybrid retrieval & reranking)\n2. _Query Understanding API_: User Query → Catalog-Aware Query (for DIY query expansion/relaxation)\n3. _Embeddings API_: Query or Product → Catalog-specific Embedding (for DIY retrieval)\n4. _Reranking API_: Candidate Products → Re-ordered Products (for DIY precision improvements)\n\n**Step 4 - Preferences**: Users can control search behavior using:\n\n* _Merchandising Strategies:_ Execute smart strategies using simple instruction templates (e.g. “boost Apple Vision Pros to visitors looking for gifts”)\n* _Feedback:_ Demonstrate examples of preferred search results (models learn from these “few-shot”)\n* _Maintenance Jobs:_ LLMs periodically evaluate search results (e.g. for queries with zero results) and produce training data to fix gaps\n\n### **Our Ask**\n\n* If you want a better search with lower effort for your Ecommerce store, contact us via [email](mailto:govind@branch-ai.com) or [LinkedIn](https://www.linkedin.com/in/govindchandrasekhar/).\n* If you’d like a free evaluation of your site search, submit a request [on our website](https://www.branch-ai.com).\n\n### **About Us**\n\n* [Govind](https://www.linkedin.com/in/govindchandrasekhar/) was previously co-founder of [Semantics3](https://www.ycombinator.com/companies/semantics3) (YC W13), an Ecommerce catalog AI company, from 2011 to 2020.\n* Govind & [Praveen](https://www.linkedin.com/in/praveens07/) (founding engineer) built embeddings, rankers, and transformer-based sequence models at Twitter that directly drove 1MM+ DAUs.\n* [Ishan](https://www.linkedin.com/in/ishanagrawal/) was CTO at Funding Societies, the largest digital financing Fintech in Southeast Asia, where he built and scaled the technology and business from Seed to Series D.\n\n### **Our Vision**\n\nTo serve as an Ecommerce AI platform for brands and retailers. We already see that our models do better than mainstream alternatives at out-of-domain tasks like recommendations, assortment curation, insight extraction (from query text), marketing content generation (from query text), in-session personalization, and event-based ranking.", "url": "https://www.ycombinator.com/launches/KU0-branch-ai-state-of-the-art-ecommerce-search-instantly", "votes": 27, "created_at": "2024-02-26T15:56:38.525Z" } ] }, { "id": 29091, "name": "Webhound", "slug": "webhound", "former_names": [ "Slingshot", "Lavalamp", "Instaclass", "Remy" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/1c9d8480ce32c923dc77fca44069ab19c5dab6eb.png", "website": "https://webhound.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Webhound builds long-horizon AI research agents that scale quality with time and budget. Our agents can create either structured datasets or fully cited research reports.", "one_liner": "Long-horizon research agents", "team_size": 2, "industry": "Consumer", "subindustry": "Consumer", "launched_at": 1689818569, "tags": [ "Generative AI", "Consumer", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "Consumer" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/webhound", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/webhound.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/webhound", "x_url": "https://x.com/webhoundai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/1c9d8480ce32c923dc77fca44069ab19c5dab6eb.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/af083f86bdd2f140667a8be17ea63bf77b6ea035.png" ], "primary_partner": null, "founders": [ { "user_id": 1444428, "full_name": "Flynn Traeger", "title": "Founder", "founder_bio": "Software engineer by training but cut my teeth in headhunting so can't stop talking to people. Ex-Stanford rower. 2x All-American. Now I'm trying to make search better.", "is_active": false, "linkedin_url": "https://linkedin.com/in/flynn-traeger", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ecc1f2dff3dba0c41cd8d3b38dca353c6dc31bd4.jpg" }, { "user_id": 1446074, "full_name": "Bryn Hughes", "title": "Founder", "founder_bio": "Stanford CS 2023 grad with previous research in ASL translation with LLMs and model design.", "is_active": false, "linkedin_url": "https://linkedin.com/in/bryn-hughes-3968b7201", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f3d8a68d7607f020c95d70f2be99b53f17ccd1e4.jpg" }, { "user_id": 1444435, "full_name": "Moe Khalil", "title": "Founder", "founder_bio": "Product @ LiteLLM (YC W23). Previously built the first ever commercial deep research product @ Webhound (YC S23). Stanford CS '23", "is_active": false, "linkedin_url": "https://linkedin.com/in/moe-khalil-73a2371a1", "x_url": "https://x.com/Mokhalil01", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/71908f66e9da71876cf3cca281a7afd005398c55.jpg" }, { "user_id": 870360, "full_name": "Porter Weisberg", "title": "Founder", "founder_bio": "Previously a C.S. major and all-academic rower at Stanford, and has been building startups since.", "is_active": false, "linkedin_url": "https://linkedin.com/in/porter-weisberg", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/48f21488c1a9ade1b22a7d7a2c52afb3703b1f4b.jpg" } ], "jobs": [], "news": [ { "title": "Our favorite startups from YC's Summer 2023 Demo Day, Day 1 | TechCrunch", "url": "https://techcrunch.com/2023/09/06/ycombinator-demo-day-one-summer-2023/", "date": "Sep 06, 2023" } ], "launches": [ { "id": 99089, "title": "Webhound Agent: A full research assistant with a workspace", "tagline": "An AI research assistant powered by the best deep research money can buy", "body": "\n\n**The problem:** Most deep research products decide for you when to stop. And since they're subscription-based, the less work they do per query, the more they profit.\n\n**What we built:** Webhound is a research agent where you set a dollar budget and it keeps searching until it's used. $5 or $100 — every dollar goes into deeper search, more sources, and real verification. The output is either a cited research report or a structured dataset with source URLs per cell.\n\n**What's new:** Today we're launching Webhound Agent — a full research assistant with a workspace.\n\n* **Start research from chat.** Describe what you need, the agent proposes a session with an editable budget and model. You confirm before anything runs.\n* **Multi-step pipelines.** Chain steps together — \"research X, then extract Y, then write a recommendation\" — and they run automatically in sequence.\n* **Analyze your existing work.** Ask the agent about any report or dataset. It reads the full document and answers with citations.\n* **Organize everything in folders.** Nested workspace with drag-and-drop, working directories, and folder-scoped context.\n* **The agent remembers you.** It learns your research interests, budget preferences, and working patterns across conversations.\n\n**How it works under the hood:** Research sessions run a plan-execute-verify loop. The planner decides what to investigate, the executor searches the web and writes a cited report, and the verifier checks every claim against its sources. When the verifier finds gaps, it sends the work back. This cycle repeats until the budget runs out.\n\n**Pricing:** Pay-as-you-go. No subscriptions. $5 free credits to start. See real example reports at different budgets: \n\n![uploaded image](/media/?type=post&id=99089&key=user_uploads/1444435/efda94f3-8921-4230-98e9-e26f86b3514c)\n\n", "url": "https://www.ycombinator.com/launches/PmD-webhound-agent-a-full-research-assistant-with-a-workspace", "votes": 7, "created_at": "2026-03-25T17:52:50.836Z" }, { "id": 97405, "title": "Webhound Reports: Deep research that scales with your budget", "tagline": "Long-running deep research for the highest quality reports", "body": "Hey everyone, Moe here.\n\nWe're building Webhound — AI research agents where you set a budget and the agent keeps researching until it runs out. More budget = more sources, more depth.\n\n**How we got here:** We started with a product (now called Datasets) that builds structured tables from the web. It worked, but we hit a wall: as datasets got bigger, costs exploded and context became unmanageable. And for open-ended datasets, we never had a clean answer for \"when should the agent stop?\"\n\nSo we rebuilt everything around two ideas: costs that stay linear with time, and giving users direct control over depth.\n\nThe new architecture is a **Planner-Executor-Verifier loop**. Executors get fresh context each run. Planner and Verifier work from summaries and can search over what's accumulated. Runs until budget's out.\n\nWe tested it on traditional deep research as a sanity check. At $3-5, it beat the top result on DeepResearch Bench. At $15, it way outperformed it, even running on older models (gemini-2.5-flash). So we shipped it as Reports and put the same architecture into Datasets.\n\n**We now have two products:**\n\n* **Reports** = cited research docs (competitive analysis, market research, system design). Always uses the full budget—more budget means more sources and deeper coverage.\n* **Datasets** = build structured tables from the web, or upload your own and enrich with new columns. We can access LinkedIn, YouTube, Reddit, and most other sites. Examples: lead lists with company info, competitor feature matrices, lists of VCs filtered by thesis, GTM leads with emails. Stops when complete, or at budget, whichever comes first.\n\n**Asks:**\n\n* **Try it** – [**webhound.ai**](http://webhound.ai), $5 free. Honest feedback welcome.\n* **Spread the word** – if it's useful, share it with anyone you know doing a lot of research (investors, analysts, consultants)\n\n– Moe\n\n![uploaded image](/media/?type=post&id=97405&key=user_uploads/1444435/fceaddd4-53f4-438f-930d-a4bf18bf97ce)\n\n(Demo Video for Reports: [**https://www.youtube.com/watch?v=eEGJBudvmsE**](https://www.youtube.com/watch?v=eEGJBudvmsE))", "url": "https://www.ycombinator.com/launches/PL3-webhound-reports-deep-research-that-scales-with-your-budget", "votes": 3, "created_at": "2026-02-02T18:32:28.191Z" }, { "id": 92035, "title": "Webhound: An AI Agent That Builds Datasets From the Web", "tagline": "Webhound lets you automate weeks of manual research work by running in the background while you tackle other priorities.", "body": "Hey everyone,\n\nWe’re [Moe](https://www.linkedin.com/in/moe-khalil-73a2371a1/) and [Theo](https://www.linkedin.com/in/theodor-schmidt/), and we’re building [Webhound](https://webhound.ai).\n\n# TL;DR\n\nWebhound is an AI research agent that builds custom datasets from web research. Instead of spending weeks manually gathering data, simply describe what you need and Webhound automatically finds, extracts, and organizes it into structured datasets you can export.\n\nSome examples are:\n\n* “Find 100 AI/ML YouTubers with 50K-100K subscribers, their email addresses, and latest video view counts.”\n* “Track pricing, features, and customer reviews from both Reddit and Youtube for 30 CRM competitors including Salesforce, HubSpot, and Pipedrive.”\n* “Collect 500 vacation rental listings in Bali with photos, pricing, amenities, and guest reviews from multiple platforms to analyze market trends.”\n* “Identify 200 early-stage e-commerce startups on Shopify or WooCommerce, along with founder names, contact emails, and product categories to build a lead list.”\n\nhttps://www.youtube.com/watch?v=cG1SiKznpHs&pp=0gcJCcEJAYcqIYzv\n\n# **❌ The Problem**\n\nData collection is painfully manual and slow. Need to research 100 competitors? That means visiting 100 different websites, copying information into spreadsheets, and repeating this process for every data point.\n\nThis creates two major problems:\n\n* **High effort:** Hours of tedious manual work, clicking through sites, and organizing data\n* **Slow speed:** What should be quick research turns into weeks of work\n\n# **👷 Our Solution**\n\nWebhound makes data collection effortless and fast.\n\n**Effortless:** Simply describe what you need in plain English. Webhound handles everything in the background while you focus on other work.\n\n**Fast:** A fleet of parallel search agents collect data simultaneously across multiple sources, turning weeks of manual work into hours.\n\nExport clean, structured datasets as CSV, Excel, or JSON.\n\n![uploaded image](/media/?type=post&id=92035&key=user_uploads/1444435/a0229371-c973-42ca-8383-bf35d6bb99c7)\n\n# **🙏 Our Ask**\n\nWant to try Webhound? Access is currently limited to 5 datasets per week and one at a time, but if you're interested in higher limits to automate your research workflow, please reach out to us at [**team@webhound.ai**](mailto:team@webhound.ai).\n\n# 👬 **The Team**\n\nMoe and Theo have been friends for 6 years and were roommates in college (in the room where Evan Spiegel founded Snapchat).\n\nSince graduating, Moe’s built a bunch of AI search tools, including Instaclass (turn any topic into a full search-backed course) and Remy (think Perplexity, but for video).\n\nTheo went deep on data, building and scaling Quicklime, where he helped film studios and streaming services collect the info they needed to make smart bets.", "url": "https://www.ycombinator.com/launches/NwR-webhound-an-ai-agent-that-builds-datasets-from-the-web", "votes": 20, "created_at": "2025-07-10T00:48:30.310Z" }, { "id": 85709, "title": "Remy: The chatbot that's seen every video on YouTube", "tagline": "Remy searches over billions of videos to build you custom playlists on any topic, finding exactly what you want to watch - and nothing you don't.", "body": "![](https://www.ycombinator.com/media/?type=post&id=85709&key=user_uploads/1444428/e4dd006c-a312-4c90-9340-6b403f9bd17a)\n\nThe internet is filled with incredible video content, but finding the exact moments you're looking for can feel impossible. \n\nCurrently, typical video search falls into one of two categories:\n\n**1. Searching short-form video content**\n\n* Search TikTok or YouTube Shorts for clips made by creators.\n* Settle for recommended clips that don't quite match what you need.\n* Get filtered back into _the algorithm_.\n\n**2. Manually looking through long-form videos**\n\n* Open ten tabs with the top results on YouTube to look through them all.\n* Waste time scrubbing through long videos looking for the moments you want.\n* Miss valuable insights buried in obscure content.\n* Give up searching altogether.\n\n### We built Remy to solve this.\n\nInstead of treating videos as single units, [Remy](https://www.useremy.com/) understands what's happening inside every second of every video. It's like having a brilliant research assistant who's watched everything on the internet and can quickly find the exact moments you care about.\n\nThink of all the knowledge trapped in hour-long podcasts, conference talks, interviews, and tutorials. Remy sets it free by building you personalized playlists that connects the dots across thousands of sources—turning an overwhelming sea of content into a clear story that matters to you.\n\n\n\n## **🔎 How Remy works:**\n\n1. Tell Remy what you're curious about\n2. Remy searches through the internet's video content\n3. Remy curates a playlist of perfectly clipped moments for you to watch\n\n## **✨ Perfect for:**\n\n* 📚 Learning new skills: **\"Gordon Ramsay explaining expert knife techniques\"**\n* 🎯 Product research: **\"Professional skiers talking about best skis of 2025\"**\n* 🔍 Deep dives: **\"070 Shake talking about the music scene in New Jersey\"**\n* 📰 News & updates: **\"What does Sam Altman think about what's happening with AI regulation?\"**\n\n## **🤨 What makes Remy different:**\n\n* Real-time search across billions of videos\n* Smart clipping that captures full context\n* Conversational interface for natural discovery\n* Playlists that make sense for your search, not just random clips\n\nAs your searches evolve, Remy understands the context and narrows down results in real time, finding clips that otherwise might be buried deep within hours-long videos. Have questions about a clip it found? Just ask in the chat and get answers rooted in the content Remy found for you.\n\n### Give Remy a try today to discover videos you never knew existed.\n\n", "url": "https://www.ycombinator.com/launches/MIP-remy-the-chatbot-that-s-seen-every-video-on-youtube", "votes": 49, "created_at": "2024-11-19T20:47:39.072Z" }, { "id": 80492, "title": "Instaclass: Make a masterclass on literally anything", "tagline": "Instaclass lets you make a masterclass on anything with the best content on the internet", "body": "[Instaclass](https://myinstaclass.com/) lets you make a masterclass on anything you want, using the best content that's already on the internet.\n\nWe’ve spent the last year building and launching in the AI content consumption space, from games to podcasts to news to videos. What we’ve learned is that we don’t need AI to create content for us. Everything you could possibly need or want to consume has probably already been created; _it’s just hard to find, and you don’t always know what to look for_. Instead of using AI to create content, we believe AI should play a role in curating human-made content.\n\nOn the back of all of this work and these insights, we have built [Instaclass](https://myinstaclass.com/).\n\n![uploaded image](/media/?type=post&id=80492&key=user_uploads/1444428/f9dbfab3-e0c1-4518-ae87-ba05f3caf9b5)\n\n[Instaclass](https://myinstaclass.com/) lets you build classes on anything you want, no matter how niche, doing the hunter-gatherer portion of research for you so you don't need to spend a day trawling through the internet looking for good content to learn with.\n\nWe use Instaclass daily to learn about [_brewing better coffee_](https://myinstaclass.com/home/0510f5fd-420c-4023-b394-39c3c6074c1e/f4157ffb-def7-44b8-bb6a-80c933ed3054)_, _[_the Fallout universe and lore_](https://myinstaclass.com/home/0c315ab0-a562-46d5-94e5-8013f3be40d3/3b0fdf63-c6a6-4feb-a883-b8f207409810)_, _[_the French Revolution_](https://myinstaclass.com/home/0cf5ac0a-025b-455b-9e17-344d221cec2f/347c6edc-6b41-43c8-8a0b-2ec32471f6a8)_, and _[_cooking tips from Gordon Ramsay_](https://myinstaclass.com/home/0510f5fd-420c-4023-b394-39c3c6074c1e/acbcc328-a9a1-4158-a892-2a04b8d5c830)_._ We’ve joked that this would have been really helpful in the early days of each of our previous pivots - if you know us, then you’d know this one on [_how to build video games_](https://myinstaclass.com/home/1d5a2b90-86c1-471a-933c-6903d1f93206/443c077a-34bc-400a-abc3-6284d6a3e1e7) would have been worth its weight in gold.\n\n_Users have done everything from learning _[_how to give a sales pitch for an interview_](https://myinstaclass.com/home/8ee4cb8a-0ab4-4d88-83c2-50d582fe1adb/9be936bd-c058-4ad8-be2d-f292bd1059e9)_ to _[_how to be nice to your boyfriend_](https://myinstaclass.com/home/1db8a552-515d-4029-9eba-68c807329668/299d2703-4b49-4743-a83a-d9168cbc3874)_._\n\n[Instaclass](https://myinstaclass.com/) pulls in information from across the web on a particular topic and puts it in a course format so you can go in with no idea about a topic and come out decently well-informed.\n\nTry it out, and let us know what you think! We are limiting free credits at the moment, so if you run out, join the waitlist, and we’ll email you when more become available. Looking forward to seeing what you all make!", "url": "https://www.ycombinator.com/launches/KwG-instaclass-make-a-masterclass-on-literally-anything", "votes": 172, "created_at": "2024-04-29T17:51:32.573Z" } ] }, { "id": 29118, "name": "Structured Labs", "slug": "structured-labs", "former_names": [ "Structured", "Structured Labs", "Preswald", "Structured Labs", "Wadium", "Waldium" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e8dd08fd61491d9efffabb3dff787320791e2421.png", "website": "https://www.structuredlabs.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Waldium is an AI-driven platform for researching, writing, and publishing technical knowledge in formats that both humans and AIs can understand. We help developers create technical guides, resources and tutorials — structured, durable knowledge that AIs can learn from, cite, and build upon.", "one_liner": "The publishing layer for the agentic internet", "team_size": 5, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1686619959, "tags": [ "Developer Tools", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "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/structured-labs", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/structured-labs.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/structuredlabs/", "x_url": "https://x.com/waldium", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/structured-labs", "github_url": "https://github.com/StructuredLabs", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e8dd08fd61491d9efffabb3dff787320791e2421.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": 1194855, "full_name": "Amrutha Gujjar", "title": "Founder", "founder_bio": "Structured", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/amruthagujjar/", "x_url": "https://twitter.com/amruthagujjar", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/94a9bf7fb1763cd598927323dbc16a60f0d7ad98.jpg" }, { "user_id": 281458, "full_name": "Shivam Singhal", "title": "Founder", "founder_bio": "data infrastructure", "is_active": false, "linkedin_url": "https://linkedin.com/in/shivam-singhal", "x_url": "https://twitter.com/singhals_", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4d9c91a2c049a5a37324846f26886d63ddc5837d.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 83717, "title": "📈 Structured - Single source of truth for key business metrics", "tagline": "Unify your growth and data teams with consistent metrics, automated documentation, and real-time alerts, all synced directly from your data warehouse and dbt.", "body": "### **The Problem: Inconsistent Metrics, Misaligned Decisions**\n\nIn the high-pressure world of growth and revenue, teams live or die by their data. Yet, too often, the very metrics that should guide decisions are inconsistent, poorly documented, or misunderstood across different teams. \n\nImagine this: Your team is preparing for an important strategy meeting. They need to assess the impact of a new onboarding feature on user activation. Marketing has one definition of \"activation,\" product has another, and sales uses a completely different set of metrics. Each team pulls data from the same warehouse – but interprets it differently, leading to conflicting reports and a confused leadership team. The result? This misalignment creates chaos, leading to conflicting reports, wasted resources, and missed revenue. Hours are wasted on reconciling data discrepancies instead of focusing on strategy, and missed opportunities to act on accurate insights.\n\nBut what if you could ensure that every team in your organization was always on the same page, with a single, accurate source of truth for all key metrics? What if your data was automatically documented, changes tracked, and alerts sent out when something significant changes? Enter Structured.\n\n![uploaded image](/media/?type=post&id=83717&key=user_uploads/1194855/8a5a8ddb-49b1-4d41-82f6-12ec24c16154)\n\n### **The Solution: Structured Brings Clarity and Consistency**\n\nStructured is designed to solve this exact problem. By integrating directly with your data warehouse (e.g., Snowflake, BigQuery) and your transformation tools (e.g., dbt), Structured automatically catalogs your data’s metadata and ensures that all metrics are consistently defined and documented across your organization.\n\n1. **Connect Your Data Warehouse and dbt:**\n * Structured connects directly to your data warehouse—whether it’s Snowflake, BigQuery, Databricks, or Redshift—and integrates with dbt, the tool your data engineers already use to model and transform data.\n * The moment you connect, Structured starts cataloging your data, tracking every table, column, and transformation automatically.\n2. **Auto-Generated Data Catalog and Documentation:**\n * Structured automatically generates a comprehensive data catalog, documenting every metric, entity, and relationship. AND—this documentation is instantly synced to Notion, a tool your business teams already know and love.\n * Now, whether you’re a data engineer or a revops analyst, you can easily access and understand how metrics are defined and where the data comes from. No more guessing, no more miscommunication—just clarity\n3. **Summaries of Changes and Version History:**\n * Structured tracks every change made to your data models and metrics, automatically generating summaries that are easy to understand, even for non-technical users.\n * Every change is logged with version history, so you can see exactly how metrics have evolved over time. This means no more confusion over which version of a report to trust—everyone is working from the latest, most accurate data.\n4. **Alerting on Metric Changes:**\n * Structured’s AI doesn’t just catalog your data—it monitors it over time. If a critical metric changes—say, your churn rate spikes or a key transformation in dbt is modified—Structured sends out alerts to the relevant teams to see what’s up\n * These alerts are designed to be easily understood by business users so everyone knows what’s happening and why it matters. This means faster, more informed decisions and no surprises in your strategy meetings.\n\n![uploaded image](/media/?type=post&id=83717&key=user_uploads/1194855/aeb49c87-18e9-4860-9bcb-04a54f96569b)\n\n### **ROI: Real Results from Day One**\n\nStructured delivers measurable value by:\n\n* **50% Faster Reporting:** Teams can generate accurate reports in half the time, thanks to consistent, pre-documented metrics that are automatically synced to tools like Notion.\n* **20% Reduction in Data Errors:** Structured reduces the risk of data discrepancies with automated metadata management and version tracking, ensuring that your reports are always based on the most accurate and up-to-date information.\n* **30% Lower Operational Costs:** By automating the documentation and alignment of data, Structured reduces the manual effort required from your data teams, freeing them to focus on more strategic tasks.\n\n![uploaded image](/media/?type=post&id=83717&key=user_uploads/1194855/9751d79d-6c81-46c3-adb8-c9156653f0d6)\n\n### **Get Started Today!**\n\nStructured is here to help your teams make better decisions, faster. Visit [structuredlabs.com](http://structuredlabs.com) or reach out to Amrutha Gujjar: [amrutha@structuredlabs.com](mailto:amrutha@structuredlabs.com) to schedule a demo and see how Structured can help!", "url": "https://www.ycombinator.com/launches/LmH-structured-single-source-of-truth-for-key-business-metrics", "votes": 125, "created_at": "2024-09-03T20:34:12.116Z" }, { "id": 73623, "title": "⭐ Structured - Semantic layer for reliable business metrics", "tagline": "Consistent metrics, trusted data, faster decisions, and clear growth outcomes.", "body": "Hello YC! We are Amrutha Gujjar and Shivam Singhal, the founders of Structured.\n\nWe’re excited to introduce **Structured**—a platform that helps businesses easily define, manage, and scale their most critical business metrics using a robust semantic layer.\n\n### **The Problem**\n\nIn today’s data-driven world, businesses are increasingly relying on metrics to guide decision-making. However, we’ve seen countless organizations struggle with the inconsistency and complexity of defining and managing these metrics. Whether it’s “Revenue,” “Customer Acquisition Cost,” or “Churn Rate,” different teams within the same company often use different definitions, leading to misaligned decisions, wasted resources, and a lack of trust in the data.\n\nTraditional BI tools fall short when it comes to ensuring that metrics are computed consistently across an organization. This problem is particularly acute in companies with large, complex datasets stored in cloud data warehouses like Google BigQuery. Maintaining consistency and governance over these metrics typically requires significant manual effort, deep technical expertise, and can lead to errors that compromise decision-making.\n\n### **Scenario**\n\nAs the VP of Data at a rapidly scaling SaaS company, you're responsible for ensuring that the metrics used across the organization are accurate, consistent, and aligned. But despite your best efforts, discrepancies in critical metrics like Customer Acquisition Cost (CAC) and Monthly Recurring Revenue (MRR) continue to create challenges.\n\nFor example, your Marketing team reports a CAC of $130, calculated using data from HubSpot and Salesforce, where they focus on ad spend and campaign costs. They’re eager to demonstrate the efficiency of their campaigns, so they exclude overhead costs to present a leaner figure.\n\nOn the other hand, your Finance team calculates CAC at $210, pulling from NetSuite and incorporating a broader range of costs, including overhead and allocated expenses. Their approach is to capture the full financial impact, ensuring that nothing is overlooked.\n\nMeanwhile, the Product team is responsible for tracking MRR, using data from Stripe. However, they include one-off setup fees in their MRR calculations, inflating the recurring revenue figure. When they report MRR as $1.2 million, you know the true recurring revenue is closer to $1 million when those fees are excluded.\n\nThese conflicting numbers lead to confusion and frustration at the executive level. The CEO asks for the real CAC and MRR to inform key strategic decisions, but the data from different teams doesn’t match up. You find yourself mediating between departments, trying to align definitions, and reconciling these differences. Your data team spends hours pulling reports, adjusting metrics, and validating numbers, just to arrive at a single source of truth.\n\nThis process isn’t just time-consuming; it’s draining your team’s resources and slowing down the decision-making process. The ongoing discrepancies and the manual effort required to resolve them create friction across departments, eroding trust in the data and undermining the confidence of the executive team in making data-driven decisions.\n\n### **The Solution**\n\n**Structured** is a powerful semantic layer that integrates directly with cloud data warehouses like Google BigQuery.\n\n* **Onboarding and Setup**:\n * Your data team connects Structured to your Google BigQuery warehouse in one-click. \n * **Define Standard Metrics**: Access a library of pre-built metric templates or create custom metrics tailored to your specific business needs. \n * _Example_: Using Structured’s library, you define CAC and MRR with input from finance, marketing, and product. Everyone agrees on what costs are included in CAC and how MRR should be calculated.\n * Structured makes sure that these definitions are consistently applied across your organization.\n\n![uploaded image](/media/?type=post&id=73623&key=user_uploads/1194855/fe1a81fd-f235-4e41-9395-daeefe0b3daa)\n\n* **Governance and Control**:\n * You assign ownership of these metrics to the finance team, but allow other departments to view and suggest changes. Structured’s version control means every change is tracked, so you can always revert if necessary.\n * Now, all departments are using the same metrics. When the CEO asks for the latest CAC and MRR, everyone is looking at the same, accurate numbers \n* **Proactive Alerts**: Structured’s AI-driven features monitor your metrics and flag any anomalies. For example, if CAC suddenly spikes, the platform can alert you before it becomes a bigger issue. These insights help you stay ahead of potential problems and make more informed decisions.\n* **Data Request Tickets:** Structured offers an integrated ticketing system to manage data-related requests across your organization. Whether it's a request for a new metric, a data discrepancy, or additional analysis, your team can easily submit, track, and resolve these requests within the platform.\n* **Knowledge Base**: Structured automatically generates documentation as an artifact of setting up and managing metrics. This documentation is a powerful, quick win for your data team, creating a shared reference point that everyone in the organization can trust. This instant clarity translates directly to business impact—empowering your teams to make aligned decisions faster.\n* **ROI**:\n * Your team saves dozens of hours previously spent reconciling different versions of the same metric. Decisions are made faster and with more confidence.\n * By making data-driven adjustments to your customer acquisition strategies based on the standardized CAC, your company can focus on reducing the acquisition costs. The clarity around MRR helps optimize pricing strategies, contributing to increase in revenue.\n\n![uploaded image](/media/?type=post&id=73623&key=user_uploads/1194855/333b7409-baed-4975-ac28-e4d7a850ba1c)\n\n### **Why Structured?**\n\nWe founded Structured because we believe that every business, regardless of its size or technical sophistication, should have access to reliable, consistent metrics that drive smarter decisions. We’ve experienced firsthand the frustrations of working with inconsistent data and the impact it can have on business outcomes.\n\nWe realized that the tools available today either require significant technical expertise or fall short in ensuring metric consistency across an organization. Structured was born out of a desire to create a solution that bridges this gap—providing a platform that is both powerful enough for data teams and simple enough for business users. By focusing on the specific challenge of metric consistency, we aim to empower companies to make better, data-driven decisions without the technical overhead.\n\n### **Why Now?**\n\nBusinesses are generating and storing more data than ever before, particularly in cloud-based data warehouses. With this explosion of data comes the challenge of making sense of it—turning raw numbers into meaningful metrics that everyone in the organization can trust and use.\n\nAt the same time, the complexity of data environments is increasing. Companies are dealing with multiple data sources, complex data pipelines, and a growing need to make sure that metrics are not only accurate but also consistent across different departments and teams. Traditional BI tools are struggling to keep up with these demands, and businesses are increasingly looking for solutions that can provide a more streamlined, consistent approach to metric computation.\n\nThe market is ripe for a solution that can simplify the process of managing business metrics, reduce the reliance on specialized technical skills, and allow organizations to unlock the full potential of their data. Structured addresses this need head-on, providing a solution that is both timely and essential for the next generation of data-driven businesses.\n\n### **Try Structured**\n\nIf you’ve ever struggled with inconsistent metrics or if you’re looking for a better way to manage and scale your business analytics, we’d love to connect with you. Check us out at [structuredlabs.com](http://structuredlabs.com) or drop us an email at [amrutha@structuredlabs.com](mailto:amrutha@structuredlabs.com) \n\nLooking forward to your feedback and insights!", "url": "https://www.ycombinator.com/launches/J9T-structured-semantic-layer-for-reliable-business-metrics", "votes": 157, "created_at": "2023-07-31T22:30:34.907Z" } ] }, { "id": 29177, "name": "Onyx", "slug": "onyx", "former_names": [ "Danswer" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/def9ae8f0c06685a28e8184b828d9de7e1232ad3.png", "website": "https://www.onyx.app/", "all_locations": "San Francisco, CA, USA", "long_description": "Onyx is the open source chat UI connected to your company’s docs, apps, and people. We ingest and sync from all sources of information (Google Drive, Slack, GitHub, Confluence, Salesforce, etc.) to provide a centralized place for users to ask about anything. Imagine your most knowledgeable co-workers, all-rolled into one, and available 24/7!\r\n\r\nCheck us out on Github: https://www.github.com/onyx-dot-app/onyx\r\nOr visit our website: https://www.onyx.app/", "one_liner": "Open Source AI Chat", "team_size": 20, "industry": "B2B", "subindustry": "B2B -> Productivity", "launched_at": 1704724705, "tags": [ "Open Source", "NLP", "Enterprise", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "status": "Acquired", "industries": [ "B2B", "Productivity" ], "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/onyx", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/onyx.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/in/onyx-dot-app/", "x_url": "https://x.com/onyx_dot_app", "fb_url": null, "cb_url": null, "github_url": "https://github.com/onyx-dot-app/onyx", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/def9ae8f0c06685a28e8184b828d9de7e1232ad3.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": 1493761, "full_name": "Yuhong Sun", "title": "Founder", "founder_bio": "Machine Learning and NLP, previously working on transformers architectures for NL-to-SQL and hybrid search at Alation, inc.", "is_active": true, "linkedin_url": "https://linkedin.com/in/yuhongsun", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/515c75a0f3a5760483f3617aa2d35d7b329ea752.jpg" }, { "user_id": 1493215, "full_name": "Chris Weaver", "title": "Founder", "founder_bio": "Building Onyx. In a past life: top-100 NA League of Legends player + TL at Robinhood.", "is_active": true, "linkedin_url": "https://linkedin.com/in/chris-weaver101", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d0a93d97df47cd7d3f5f3cdca5ecc1e2daf74295.jpg" } ], "jobs": [], "news": [ { "title": "This Pitch Deck Landed AI Agent Startup Onyx a $10 Million Seed Round - Business Insider", "url": "https://www.businessinsider.com/pitch-deck-ai-agent-startup-onyx-seed-round-2025-3", "date": "Mar 18, 2025" }, { "title": "Onyx Raises $10M To Help Teams Find Knowledge Faster with Open Source AI Agents", "url": "https://finance.yahoo.com/news/onyx-raises-10m-help-teams-165500715.html?guccounter=1", "date": "Mar 12, 2025" }, { "title": "Why Onyx thinks its open source solution will win enterprise search | TechCrunch", "url": "https://techcrunch.com/2025/03/12/why-onyx-thinks-its-open-source-solution-will-win-enterprise-search/", "date": "Mar 12, 2025" } ], "launches": [ { "id": 77913, "title": "Onyx - Open Source Unified Search", "tagline": "Connect to all your team’s tools (like Confluence, Google Drive, Slack, Notion, etc.) for a single place to Search and Chat with your team’s documents", "body": "**TL;DR**: Imagine a secure ChatGPT with access to your team’s specific knowledge. As modern teams grow, knowledge becomes dispersed and hard to find. By giving Large Language Models access to parse your knowledge base, [Onyx](https://onyx.app/) can instantly filter through millions of documents to only surface what is relevant.\n\n**Try for Free Here:** [**https://cloud.onyx.app/**](https://cloud.onyx.app/)\n\n**Get Started On-Prem:** [**https://docs.onyx.app/quickstart**](https://docs.onyx.app/quickstart)\n\n![uploaded image](/media/?type=post&id=77913&key=user_uploads/1493761/4c37d5b8-5919-4dab-8d27-c2aa2af46a23)\n\nWe’ve seen teams use Onyx to improve efficiencies from:\n\n* Improving turnaround times for **support** by reducing the time taken to find relevant documentation.\n* Helping **sales teams** get customer context instantly by combing through calls and notes.\n* Preventing lost **engineering** time to answering cross-team questions, building duplicate features due to the inability to surface old tickets or code merges, and helping **on-call** resolve critical issues faster by providing the complete history of an error in one place\n* Self-serving **onboarding** for new members who don’t know where to find information.\n\n### Our Differentiation:\n\nOnyx is [open source](https://github.com/danswer-ai/danswer). You have complete transparency into what is going on with your team’s documents. The deployment of Onyx and your data are completely in your control (we do also offer a [Cloud](https://www.danswer.ai/) version if you prefer being hands-off). You can hook Onyx up with any LLM of your choice or one in-house if you prefer.\n\nOnyx uses the latest methods in information retrieval to make the most of the revolution that is happening in the natural language processing space. Specifically, we use prefix-aware embedding models trained with contrastive loss + BM25 Hybrid Search with reranking options and additional pipeline improvements such as query expansion etc.\n\nYou can also use Onyx to directly get answers in Slack (MS Teams, Discord to come). Many teams have been using this to auto-answer cross-team questions.\n\n![uploaded image](/media/?type=post&id=77913&key=user_uploads/1493761/6813b7f1-9fed-4778-9874-52de4a20bef2)\n\n### Our Ask/Offer:\n\nReach out to us if you’re interested in using Onyx, you can schedule a call with us on [Cal](https://cal.com/team/danswer/founders). Alternatively, you can join our Slack [here](https://join.slack.com/t/onyx-dot-app/shared_invite/zt-2sslpdbyq-iIbTaNIVPBw_i_4vrujLYQ) — we’d love to hear from you!\n\nWe’re already deployed at some outstanding teams and we’re looking to accelerate. If you’re open to closing a deal with us quickly, we can offer you some significant discounts.", "url": "https://www.ycombinator.com/launches/KGf-onyx-open-source-unified-search", "votes": 69, "created_at": "2024-01-29T15:27:24.837Z" } ] }, { "id": 29254, "name": "Reducto", "slug": "reducto", "former_names": [ "Remembrall" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9794d4fb523375d125992ce9fa5e9e9db45daf9a.png", "website": "https://reducto.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Reducto is the agentic document platform for leading AI teams building systems to automate document workflows at enterprise scale.\r\n\r\nOur platform provides a comprehensive toolkit for working with documents the way a human would, combining custom in-house and leading frontier models to power efficient and accurate document workflows. Reducto is trusted by leading AI teams at companies like Harvey, Scale AI, and Vanta.\r\n\r\nWe are built for enterprise workloads with flexible deployment options from the cloud to fully air-gapped environments, SOC II and HIPAA compliance, and zero data retention.\r\n\r\nLearn more: https://reducto.ai/\r\nFind our YC deal: https://reducto.ai/yc", "one_liner": "The complete agentic document platform", "team_size": 30, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1704664395, "tags": [ "Documents", "Data Engineering", "Enterprise Software", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2024", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "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/reducto", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/reducto.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/reducto-ai/", "x_url": "https://x.com/reductoai", "fb_url": null, "cb_url": null, "github_url": "https://github.com/reductoai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/9794d4fb523375d125992ce9fa5e9e9db45daf9a.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": 1577822, "full_name": "Raunak Chowdhuri", "title": "Founder/CEO", "founder_bio": "Co-founder/CTO at Reducto. Before Reducto I studied CS at MIT, founded/scaled a comp. chem consulting company to 200k ARR, published computer vision papers with 100+ citations before finishing high school, and spent a little too much time shit posting on Twitter.", "is_active": true, "linkedin_url": "https://linkedin.com/in/sauhaarda", "x_url": "https://twitter.com/raunakdoesdev", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/942feecb35daadd157058cc33cb2803b8a8950dd.jpg" }, { "user_id": 850889, "full_name": "Adit Abraham", "title": "Founder/COO", "founder_bio": "Co-founder/CEO at Reducto. Before Reducto I studied CS at MIT, made Ads/Search things as a PM for Google, did ML research at MIT's Media Lab, and spent an unreasonable amount of time playing Pokemon Showdown.", "is_active": true, "linkedin_url": "https://linkedin.com/in/aditabraham", "x_url": "https://twitter.com/AbrahamAdit", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/53a289d9d77cc6b4edf978ab557af36e1442ed30.jpg" } ], "jobs": [ { "id": 69144, "title": "Software Engineer", "url": "https://www.ycombinator.com/companies/reducto/jobs/2BVFiJu-software-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$140K - $240K", "equity_range": "0.10% - 1.00%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Python", "TypeScript", "Torch/PyTorch", "Computer Vision" ] }, { "id": 70899, "title": "Machine Learning Engineer", "url": "https://www.ycombinator.com/companies/reducto/jobs/3VS78nJ-machine-learning-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$150K - $240K", "equity_range": "0.10% - 1.00%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Python", "Torch/PyTorch", "Computer Vision", "Data Analytics" ] }, { "id": 71707, "title": "LLM/ML Engineer (Inference)", "url": "https://www.ycombinator.com/companies/reducto/jobs/roVp40O-llm-ml-engineer-inference", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$200K - $300K", "equity_range": "0.10% - 1.00%", "experience": "3+ years", "visa": "Will sponsor", "skills": [] } ], "news": [ { "title": "Reducto: AI document parsing & extraction software", "url": "https://reducto.ai/blog/reducto-series-b-funding", "date": "Oct 14, 2025" }, { "title": "Reducto: Making Human Data LLM-Ready With State-of-the-Art Accuracy", "url": "https://www.youtube.com/watch?v=QBC_cViA7j8", "date": "May 01, 2025" }, { "title": "Exclusive: Reducto, AI document parsing startup, raises $24.5 million Series A led by Benchmark | Fortune", "url": "https://fortune.com/2025/04/25/exclusive-reducto-ai-document-parsing-startup-raises-24-5-million-series-a-led-by-benchmark/", "date": "Apr 25, 2025" }, { "title": "Reducto Document Ingestion API", "url": "https://reducto.ai/blog/reducto-series-a-funding", "date": "Apr 25, 2025" }, { "title": "Reducto Document Ingestion API", "url": "https://reducto.ai/blog/seed-round", "date": "Oct 02, 2024" } ], "launches": [ { "id": 82871, "title": "Reducto - Structured data from any unstructured document", "tagline": "Reducto’s new API makes it easy to turn complex documents and spreadsheets into structured data that fits your schema, with zero fine tuning. ", "body": "![uploaded image](/media/?type=post&id=82871&key=user_uploads/850889/1ce99361-5539-4e75-809d-d432d24470a2)\n\n[Reducto](https://reducto.ai) is a team from MIT building vision models to turn complex documents into LLM-ready inputs. Every part of our core API was made to offer the most accurate extraction possible, and we now power document ingestion for leading AI teams ranging from startups to Fortune 10 enterprises. \\\n\\\nWe’re excited to release a new **Structured Extraction API**, which leverages LLMs along with our vision models to extract the data that you need with exceptional accuracy and flexibility. \n\n# **📃 The Problem**\n\n**Nearly 80% of enterprise data is in unstructured formats like PDFs and spreadsheets**\\\nPDFs are the status quo for enterprise knowledge in nearly every industry. They’re stored in a structure that’s simply impractical for use in digital workflows, which leads to [dozens of wasted hours every week](https://www.reducto.ai/blog/the-real-cost-of-manual-document-processing).\\\n\\\n**Custom extraction models require hundreds of hours to build and maintain**\n\nCompanies often use traditional solutions to build a custom extraction pipeline for _every_ document layout they’re working with. That requires dozens of hours for labeling and training the model, and constant maintenance when models break from changing layouts.\\\n\\\n**LLMs offer better flexibility at the cost of reliability**\n\nOff the shelf LLMs _can_ offer exceptional reasoning but they struggle with hallucinations and extraction inaccuracies, making them unreliable for production use cases. \n\n# \n\n# **✅ Our Solution**\n\nWe’ve built vision models to read documents the way a human would, and a language model that we trained for schema-based extraction. Our new model can handle significantly larger documents, and is trained to _cite_ the source for each piece of information, allowing you to audit and verify outputs easily.\n\n![uploaded image](/media/?type=post&id=82871&key=user_uploads/850889/b090586c-1014-4293-a5f3-266950b46338)\n\nThis means you can:\n\n* Extract important fields with simple, natural language instructions\n* Verify any information using our source citations\n* Build powerful automations by integrating Reducto’s API with your custom workflow\n\n# **🚀 Automate your unstructured data processing**\n\nOur API is live in production with leading teams across insurance, healthcare, and finance, and we would love to work with you to improve your unstructured data ingestion.\\\n\\\nThis new API leverages all of the work that we’ve put into improving our document understanding models to make structured extraction work across all layouts with best in class accuracy. \\\n\\\nYou can [sign up](https://reducto.ai/pricing) to get started right away, or reach out to us at [founders@reducto.ai](mailto:founders@reducto.ai) for a demo!", "url": "https://www.ycombinator.com/launches/LYd-reducto-structured-data-from-any-unstructured-document", "votes": 28, "created_at": "2024-08-09T14:58:47.814Z" }, { "id": 78035, "title": "🔮 Reducto - Unlocking data behind complex documents", "tagline": "Our API allows you to convert complex, unstructured documents into structured outputs that are perfect for RAG, process automation, and more.", "body": "Hey everyone - [Raunak](https://www.raunakdoes.dev/) and [Adit](https://www.linkedin.com/in/aditabraham/) here 👋🏼\\\n\\\nWe met four years ago while studying computer science at MIT, and we’ve spent the past few years building ML products at companies like Google and NVIDIA. We started building [Reducto](https://reducto.ai) after struggling with document ingestion while consulting for teams integrating LLMs into their applications.\n\n# \n\n# **📃 The Problem**\n\n**Nearly 80% of enterprise data is in unstructured formats like PDFs**\n\nPDFs are the status quo for enterprise knowledge in nearly every industry. Insurance claims, financial statements, invoices, and health records are all stored in a structure that’s simply impractical for use in digital workflows. This isn’t an inconvenience—it’s a critical bottleneck that leads to [dozens of wasted hours every week](https://www.reducto.ai/blog/the-real-cost-of-manual-document-processing).\n\n**Traditional approaches fail at reliably extracting information in complex PDFs**\n\nOCR and even more sophisticated ML approaches work for simple text documents but are unreliable for anything more complex. Text from different columns are jumbled together, figures are ignored, and tables are a nightmare to get right. Overcoming this usually requires a large engineering effort dedicated to building specialized pipelines for every document type you work with. \n\n# **🚀 Our Solution**\n\nReducto breaks document layouts into subsections and then contextually parses each depending on the type of content.\n\n![](https://framerusercontent.com/images/OGE17IqyrDhYFCvrxMFNNtwpLs.png?scale-down-to=2048)\n\nThis is made possible by a combination of vision models, LLMs, and a suite of heuristics we built over time. Put simply, we can help you:\n\n* Accurately extract text and tables even with nonstandard layouts\n* Automatically convert graphs to tabular data and summarize images in documents\n* Extract important fields from complex forms with simple, natural language instructions\n* Build powerful retrieval pipelines using Reducto’s document metadata\n* Intelligently chunk information using the document’s layout data\n\nYou can try a demo [here](https://app.reducto.ai/share/5ecd4726-f482-498d-a1c2-b76e12f595a8). \n\n# **🙏🏼 Our Ask**\n\nWe’re looking to onboard more teams across insurance, healthcare, and finance and would really appreciate your help. If you or someone you know is struggling with high-volume document workflows (5000+ pages), please reach out to us at [founders@reducto.ai](mailto:founders@reducto.ai).", "url": "https://www.ycombinator.com/launches/KId-reducto-unlocking-data-behind-complex-documents", "votes": 41, "created_at": "2024-01-31T20:01:45.526Z" } ] }, { "id": 29322, "name": "Glimmer", "slug": "glimmer", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/1f9244446556b92b815b4c9bc9ee878f7d53d992.png", "website": "https://www.withglimmer.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Glimmer is a new way to search massive PDFs using AI.\r\n\r\nIf you've ever dealt with massive PDFs (i.e. over 1,000 pages), you know how broken search can be. You have no choice but to use “Cmd-F” and manually sift through thousands of search results to find specific information. Professionals in construction, law, finance, and healthcare spend hours doing this every day.\r\n\r\n\r\nGlimmer makes searching these large documents 10x easier. \r\n\r\n\r\nWhen you upload a PDF, Glimmer uses AI to intelligently index your documents. You can then search your PDF in natural language and get answers with verifiable page sources.\r\n\r\n\r\nYou can get started with Glimmer for free with any kind of PDF at withglimmer.com. ", "one_liner": "AI search for massive PDFs", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1709828657, "tags": [ "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "status": "Inactive", "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/glimmer", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/glimmer.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/98872753/admin/feed/posts/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/1f9244446556b92b815b4c9bc9ee878f7d53d992.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/2314b6f576ea28b6eba436e0a58912c4e784bb4e.png" ], "primary_partner": { "name": "Pete Koomen", "url": "https://www.ycombinator.com/people/pete-koomen" }, "founders": [ { "user_id": 1399956, "full_name": "Praneeth Guduguntla", "title": "Founder", "founder_bio": "Praneeth is the co-founder and CEO of Glimmer. Previously, he built computer vision widgets at Palantir and launched the first AI video editing tool for Facebook Reels at Meta. Praneeth graduated from UIUC with a computer science degree where where he and his co-founder built software for nonprofits together. ", "is_active": false, "linkedin_url": "https://linkedin.com/in/praneethguduguntla", "x_url": "https://twitter.com/prantheman__", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4fa3a454dd848efe86f23f966a8ce9afffd3e970.jpg" }, { "user_id": 1389259, "full_name": "Arman Rafati", "title": "Founder", "founder_bio": "Arman is the co-founder and CTO of Glimmer. Previously, he worked on corporate cards at Mercury, the first-ever BNPL debit card at Affirm, and helped launch AWS's Heroku competitor, AppRunner. Arman studied Computer Science & Economics at UIUC. He spent most of his free time in college building software for nonprofits, and most of his free time in high school reselling sneakers and building an app that he sold to his high school.", "is_active": false, "linkedin_url": "https://linkedin.com/in/armanrafati", "x_url": "https://twitter.com/gillbates408", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b4433aaec3c87c0d922110763b5a776aaf68cfe1.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 79168, "title": "✨ Glimmer – AI search for massive PDFs", "tagline": "Glimmer makes searching large documents 10x easier.", "body": "**TLDR:**\n\n**If you've ever dealt with massive PDFs (i.e. over 1,000 pages), you know how broken search can be.** We know people in construction, law, finance, and healthcare who spend hours doing this every day. With Glimmer, you can search PDFs in natural language and get answers with verifiable page sources. Start uploading your own PDFs for free at [withglimmer.com](http://withglimmer.com).\n\nHi! We’re [Praneeth](https://linkedin.com/in/praneethguduguntla) and [Arman](https://linkedin.com/in/armanrafati) and we’re building Glimmer, **a new way to search massive PDFs using AI.**\n\n![uploaded image](/media/?type=post&id=79168&key=user_uploads/1399956/43131bdf-6eac-4848-a206-fa8c5b88be96)\n\n**The Problem:**\n\nSearching through huge PDFs sucks!\n\nMeet James, a project manager at a commercial HVAC subcontractor that does construction projects for the City of San Francisco. The worst part of his job is spending hours every day searching through an 8000-page construction specification trying to figure out things like “What is the fire damper manufacturer?” or “What is the motor speed for HVAC fans?”. Currently, the entire industry uses **“Cmd-F” keyword search and manually goes through thousands of search results** to find answers to their questions.\n\n![](https://lh7-us.googleusercontent.com/tQrqCYJYcWN1CFlL11qpllX9JFYAM9c1VYCTBjPBJ85tbOTLkSofjyWZr7dK3f5U2Hcp1z9WYAInhHOrzG8ikM-mymFmiXcuuMf8T_rA1iV4hzY8TYvRiY491mhJIouH9j0jUR94Q4xum05YFcJm14g)\n\n![uploaded image](/media/?type=post&id=79168&key=user_uploads/1399956/f06c509a-6a3b-402a-b25f-c3b0da9f50ca)\n\nWhen we first met James at his office, **he was trying to feed his document into ChatGPT but he couldn’t even upload it** – his PDF was over 200MB!\n\nJames’ experience is far from unique. In law, lawyers and paralegals read and write long legal papers and contracts, searching for specific details that help them advise clients and do legal work. In healthcare insurance and coding, employees read through massive manuals and patient records to get the right information for billing and handling insurance claims.\n\n**✨ The Solution**\n\n**Glimmer uses AI to make finding information in large documents 10x easier.** When a user loads in their PDF (up to 10k pages), we intelligently chunk up their documents so they’re able to use natural language to ask questions and find exact sources within their documents.\n\nTry the interactive [demo](https://demo.withglimmer.com).\n\n**Get Started**\n\nGlimmer is completely self-serve, free to use, and works on any kind of PDF. **Visit **[**withglimmer.com**](http://withglimmer.com)** and upload your first PDF.** You can immediately begin searching for anything in your document in natural language.\n\n**About us**\n\nWe both met during our freshman year at UIUC and built several viral generative AI apps together that have been used by 50k+ people. Arman has previously worked on Mercury’s corporate credit/debit cards, helped launch the first-ever BNPL Debit card at Affirm, and worked on AWS’s Heroku competitor called App Runner. Praneeth built the first-ever AI video editing tools for Facebook Reels at Meta and developed image annotation tools for computer vision workflows at Palantir. **Now, we’re building the best search engine for PDFs.**\n\n![](https://lh7-us.googleusercontent.com/rXn8fL_PUfXh_VyKSvkB31IYubKvHQlx7ojC671s1F3ltyDpQXQ6f4LtiK767-cuTvRavzMgBSa3g8VnRQbspYB5eniFWMvPlSa9kLAxmVXC9x6b1tf2JzS3sSvQ7iEuKi4qsOtgbPAFgu6jubwOpsY)\n\nThis is us at a $3B+ construction site where James’ company works in SF.\n\n![](https://lh7-us.googleusercontent.com/1JLtj9A1EhuDIAEo4DZHf9_A4U6qLKtpWacbHPC7TiQ83aCYTSDzrL2CuGKapxNPslqYVY6h-a0R_lv8g8QCkOIaMRp24i40N7nu4uDrW_tDRSLplv2oXEvzkcpQ_oPxH6a4E_40fFKpp2kO6x6EZY0)\n\n**Our Asks**\n\n* Upload your own PDFs and start searching with Glimmer\n* We’re looking for introductions to folks who deal with large PDFs on a daily basis across construction, finance, law, etc. Feel free to reach out to us at [founders@withglimmer.com](mailto:founders@withglimmer.com)\n\n \n\n---\n\n---\n\n---", "url": "https://www.ycombinator.com/launches/Kau-glimmer-ai-search-for-massive-pdfs", "votes": 51, "created_at": "2024-03-07T17:35:16.748Z" } ] }, { "id": 29379, "name": "Trieve", "slug": "trieve", "former_names": [ "Arguflow" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e020de4d43f09ae8fc176bd79fd704be218893b7.png", "website": "https://trieve.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Infrastructure for search teams building retrieval and RAG. Trieve combines search language models with tools for tuning ranking and relevance.\r\n\r\nBuilding excellent search is difficult and can take months to implement then even more time to maintain. Trieve offers production-ready infrastructure that works out of the box to help search teams build adjustable AI search and RAG into their products. With tools for custom models, relevancy weighting, date-recency biasing, semantic full-text hybrid search, recommendations, and more Trieve covers the full spectrum of what a team wants from their discovery infrastructure. ", "one_liner": "All-in-one API for search, RAG, & recommendations", "team_size": 7, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1709233497, "tags": [ "Developer Tools", "Open Source", "API", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "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": [ { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "Power every retrieval experience on the internet" } ], "url": "https://www.ycombinator.com/companies/trieve", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/trieve.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/trieveai/", "x_url": "https://twitter.com/trieveai", "fb_url": null, "cb_url": null, "github_url": "https://github.com/devflowinc/trieve", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e020de4d43f09ae8fc176bd79fd704be218893b7.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": 1655076, "full_name": "Nick Khami", "title": "Founder/CEO", "founder_bio": "Find me at https://github.com/skeptrunedev . My primary hobbies are reading progression fantasy, riding motorcycles, and running. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/nicholas-khami-5a0a7a135", "x_url": "https://twitter.com/skeptrune", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7b50f2ec9e0f2ceff369f57009958e67d682ca5c.jpg" }, { "user_id": 1655175, "full_name": "Denzell Ford", "title": "Founder/CTO", "founder_bio": "Find me at https://github.com/cdxker. I'm mostly active on x", "is_active": true, "linkedin_url": "https://linkdin.com/in/denzell-ford", "x_url": "https://twitter.com/cdxker", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/cd5cbf22ed11a4cfd6f8c6a76af57272a04a0299.jpg" } ], "jobs": [], "news": [ { "title": "Trieve Secures $3.5M to Power AI Search and RAG — Trieve", "url": "https://trieve.ai/trieve-fundraise-announcement/", "date": "Sep 11, 2024" } ], "launches": [ { "id": 78949, "title": "Trieve 🔎 Infrastructure for search teams", "tagline": "We want to power every search bar on the internet.", "body": "Howdy, y’all!\n\nWe’re [Denzell](https://linkedin.com/in/denzell-ford) and [Nick](https://github.com/skeptrunedev), founder engineers of [Trieve](https://trieve.ai/).\n\n![uploaded image](/media/?type=post&id=78949&key=user_uploads/1655175/c03dd5a8-16e9-4d39-8885-fdbb3d7e0626)\n\n**Trieve offers source-available all-in-one infrastructure for entire search teams. Engineers implement a pipeline that connects their data source to our backend then non-technical staff can fine-tune relevance using included frontends.**\n\nCustomers have the option to either **(1)** use the managed cloud version of the software suite or **(2)** buy a license to host it themselves in their own cloud or on-premise.\n\nWe want to power every search bar on the internet.\n\n### Problem - Poor search quality, lack of modularity, inaccessibility\n\n1. Engineers first have to build APIs on top of DBs and AI models which then need to be maintained, scaled, and improved.\n2. Engineers then need to build UI's for tuning which then need to be maintained, scaled, and improved.\n3. Improving retrieval quality requires hiring more engineers such that you have enough bandwidth to both maintain and improve your custom and complex infrastructure.\n4. Ineffective spend of your search budget as you have to hire engineers to build and maintain things before you can even start adjusting retrieval quality.\n\n### Solution - Quality retrieval & source-available license\n\nTrieve provides a complete infrastructure suite that is both easy for a single engineer to integrate and allows businesses to hire more non-technical staff who can use the included front-end tools for fine-tuning relevance.\n\nWith Trieve's solution, businesses can get more value out of their search budget whether it's $10k or $10M.\n\n### WE have two asks!\n\n1. Could you introduce us to developers and teams building search and relevancy into their products?\n2. [Can you star our GitHub](https://github.com/devflowinc/trieve)?\n\n![uploaded image](/media/?type=post&id=78949&key=user_uploads/1655175/b72dc26d-867b-4637-8df5-111b2f869fa0)\n\n### About us\n\nDenzell and I are software engineers who walked into this problem trying to build semantic search for a knowledge management system that helped business teams argue better.\n\nWhen we went to go sell the arguing app we found out that the underlying search infrastructure was what people really wanted to buy.\\\n\\\n[founders@trieve.ai](https://mailto:founders@trieve.ai)", "url": "https://www.ycombinator.com/launches/KXN-trieve-infrastructure-for-search-teams", "votes": 137, "created_at": "2024-03-01T18:52:01.827Z" } ] }, { "id": 29459, "name": "Lumona", "slug": "lumona", "former_names": [ "Scout" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0d72c3d83183445e4992cb58763a5f76c303afd5.png", "website": "https://www.lumona.ai", "all_locations": "Boston, MA, USA", "long_description": "Lumona is an AI-enabled search engine featuring insights from social media. When you’re looking to buy a new phone, instead of adding reddit to the end of your search and opening tabs of threads that take forever to get through, you can search on Lumona to get all of that processed for you on one page so you can make the best decision while saving time.", "one_liner": "AI-enabled search engine featuring insights from social media.", "team_size": 1, "industry": "Consumer", "subindustry": "Consumer", "launched_at": 1704065772, "tags": [ "Generative AI", "Consumer", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "status": "Inactive", "industries": [ "Consumer" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/lumona", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/lumona.json", "year_founded": 2023, "city": "Boston", "country": "US", "linkedin_url": "https://www.linkedin.com/company/lumona-ai", "x_url": "https://twitter.com/LumonaAI", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0d72c3d83183445e4992cb58763a5f76c303afd5.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1635225, "full_name": "Dylan Walker", "title": "Founder", "founder_bio": "Dylan is the co-founder and CEO of Lumona. He is from Ventura, CA and studied computer science and engineering at MIT. He gained insights into how systems and APIs at scale work with experience at Google and Stripe. Not having exposure to traditional avenues of applying to elite colleges, he relied on Reddit and Google to learn about how to maximize his chances of being admitted. Since then, he's been focused on enabling everyone to pursue their goals and equalize the playing field.", "is_active": false, "linkedin_url": "https://linkedin.com/in/dawalker-24", "x_url": "https://twitter.com/SS6_ELL", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/02abc79d6b302c18c2fd7f3482d6c94cdd2215bf.jpg" }, { "user_id": 1647712, "full_name": "Qiong Zhou Huang", "title": "Founder", "founder_bio": "Qiong is the Co-Founder and CTO of Lumona. \n\nHe previously studied Computer Science at MIT, where he conducted Machine Learning research at the McGovern Institute of Brain and Cognitive Science. He was also co-president of MIT web.lab, a group that teaches web development to hundreds of MIT students over the winter semester.\n\nQiong has extensive experience building products that prioritize user experience, working at startups in diverse areas including computer vision, remote work, and edtech.", "is_active": false, "linkedin_url": "https://linkedin.com/in/qiong-zhou-huang-627243141", "x_url": "https://twitter.com/qionghuang6", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d5408d70c214e6ffc107d0cd53fff0798eed9827.jpg" }, { "user_id": 1647687, "full_name": "Philena Liu", "title": "Founder", "founder_bio": "Philena is the Co-founder and CFO of Lumona.\n\nShe studied CS with a minor in Math at MIT, where she was one of the lecturers in a student-run web-development class (web.lab) and the Tournament Co-Director of one of the most prestigious math tournaments in the world (HMMT). She had also worked on NLP and ML research projects before exploring LLM-assisted search with Lumona.", "is_active": false, "linkedin_url": "https://linkedin.com/in/philena-liu", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6e35981f320e1d1e924817259e73e9d090748f72.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 84330, "title": "🔍 Lumona: AI-enabled search engine featuring perspectives from social media", "tagline": "Never add 'reddit' to your search again.", "body": "# **TLDR: When you’re looking to buy a new phone, instead of Googling iPhone 16 reviews and opening multiple Reddit threads, blog reviews, and YouTube videos to know if you want to buy it, you can search once on Lumona to get all that info on one page. Try it on our website!**\n\nHey YC, we’re Dylan and Qiong, the co-founders of Lumona. After working on a product search engine, we’ve decided to build a new general search engine that incorporates those insights, and more, to make search better.\n\n# **❌ The Problem**\n\nIt’s hard to find quality information online to help you decide which product to buy, which restaurant to go to, or whether a red flag is beige. You can easily get a list of links, an AI-generated answer, or even a summary of relevant Reddit threads, but you’ll then have to comb through the many new tabs you open, make sure the AI isn’t giving you fake news, and still go to each thread to make sure the commenters weren’t spam themselves. If search engines know so much about their results and users, why can’t they make something better that integrates all 3 into one result so you don’t have to spend your evening opening and closing tabs?\n\n# **✅ Our Solution**\n\n![uploaded image](/media/?type=post&id=84330&key=user_uploads/1635225/ab12b1fe-c995-4798-a98e-6a242e913253)\n\nLumona is our answer to that question. We believe that search should be something that builds trust, reflects what you find valuable on the internet, and, most importantly, helps you find the information you need. Instead of expecting you to blindly trust our AI, we give you reasons to by supporting it with information from real people and platforms you already trust. Instead of expecting you to sort through Reddit comments or YouTube videos to find that one comment or moment, we sift through them for you and give you the information you need, all on the same page. Instead of making decisions harder by giving you contradictory ads so that we can make more money, we give you the information you need to make the best decision for _you_.\n\n\n\n# **🙏 Asks**\n\nTry it at [lumona.ai](https://lumona.ai). We’d also really appreciate any feedback you have, which you can submit on the website or by joining our [Discord](https://discord.gg/axDKbaUX). Thanks for reading!", "url": "https://www.ycombinator.com/launches/LwA-lumona-ai-enabled-search-engine-featuring-perspectives-from-social-media", "votes": 31, "created_at": "2024-09-27T00:57:16.102Z" }, { "id": 79201, "title": "☀️ Lumona - Skincare product search with Reddit and YouTube reviews", "tagline": "Product search featuring recommendations from creators you trust.", "body": "# **TLDR - Our Ask (Try it!)**\n\nLumona simplifies your product research by serving Reddit and YouTube reviews directly alongside products.\n\n### **🔎 Search on Lumona for skincare products at** [lumona.ai](https://lumona.ai/)\n\nJoin our [Discord](https://discord.gg/zUXn5uu3bk) to chat with us or reach out to us via email ([founders@lumona.ai](mailto:founders@lumona.ai)), through the feedback button (top-left), [Twitter](https://twitter.com/LumonaAI), or [YouTube](https://www.youtube.com/@Lumona-AI).\n\n---\n\n# **🏋️ Who We Are**\n\n![uploaded image](/media/?type=post&id=79201&key=user_uploads/1635225/bbb1c779-0cba-46b4-b432-9df92710160d)\n\nI’m [Dylan](https://linkedin.com/in/dawalker-24) (center), co-founder and CEO of [**Lumona**](https://www.ycombinator.com/companies/lumona), a product search engine that brings you high-quality recommendations from the creators you trust. Instead of wading through pages of clickbait Google results or adding ‘reddit’ to your search, we deliver you products you’d otherwise spend hours researching for by compiling Reddit and YouTube reviews about the product.\\\n\\\nI’m working on Lumona alongside two fellow MIT Class of 2024s, [Philena Liu](https://linkedin.com/in/philena-liu) (left), our CPO, and [Qiong Zhou Huang](https://linkedin.com/in/qiong-zhou-huang-627243141) (right), our CTO. We have been working together since meeting during our sophomore year at MIT, recently winning HackMIT this past September. As part of MIT’s student-run Web Lab class, we taught over 300 students modern web development. As Gen-Zers ourselves, we’ve grown up being influenced by the content creators we watch every day and buying products online. We’ve spent hours combing over r/SkincareAddiction posts and watching YouTube videos to get to the skincare routines we have right now, and that’s where we want to help.\n\n---\n\n# **❌ The Problem and ✅ Solution**\n\nProduct research takes time. As a teenager, I had acne that was painful and affected my self-esteem, and to solve this problem I was willing to spend hours looking for the right product because if I was wrong I would break out for weeks and make it worse. Before Lumona, I would’ve had to do this:\n\n1. Research on Reddit, YouTube, and Google for what makes a good face wash for the skin type I have (combination)\n2. Search Amazon for “face wash for combination skin”\n3. Look into each of the individual Amazon products\n4. Cross-reference each product with reviews about it on Reddit and YouTube\n 1. Reddit: Google Search “{product name} reddit”\n 2. YouTube: Search on popular skincare influencer’s channels for the product\n5. Decide which product I want to buy\n\nLet’s look at that same process with Lumona:\n\n1. Search on Lumona for “face wash for combination skin”\n\n ![uploaded image](/media/?type=post&id=79201&key=user_uploads/1635225/b625fcee-a485-453b-91fa-73094129d95c)\n\n2. Research each product on Lumona, with the Reddit reviews and YouTube videos about the product already being associated with it\n\n ![uploaded image](/media/?type=post&id=79201&key=user_uploads/1635225/210013d4-23c9-4dfa-83b7-83ff2ed85206)\n\n3. Decide which product I want to buy\n\nBy serving Reddit and YouTube reviews directly alongside the product, Lumona simplifies the product research process into one tab and search while giving you more confidence when you’re buying a product. Right now, we currently support skincare, and are excited to see what you think!\n\n# **🙏 Asks**\n\nSearch on Lumona for your skincare products [here.](https://lumona.ai)\n\nWe’d love to hear your feedback! Join our [Discord](https://discord.gg/zUXn5uu3bk) to chat with us or reach out to us via email ([founders@lumona.ai](mailto:founders@lumona.ai)), through the feedback button (top-left), [Twitter](https://twitter.com/LumonaAI), or [YouTube](https://www.youtube.com/@Lumona-AI).", "url": "https://www.ycombinator.com/launches/KbR-lumona-skincare-product-search-with-reddit-and-youtube-reviews", "votes": 107, "created_at": "2024-03-08T03:31:03.531Z" } ] }, { "id": 29573, "name": "Undermind", "slug": "undermind", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/2e44c4453098a5caed0988fdf2dff5b898cdcac4.png", "website": "https://www.undermind.ai", "all_locations": "San Francisco, CA, USA", "long_description": "At Undermind, we're building a search engine that can handle extremely complex questions. It’s geared at experts, like research scientists and doctors, who need to find very specific resources to solve high-stakes problems. \r\n\r\nWe’ve rebuilt search from the ground up to address this. Our new approach employs high-quality LLMs to adaptively explore a database, mimicking how a human researcher carefully discovers information. This approach dramatically outperforms (by 10-50x) traditional keyword search and other modern AI-based retrieval methods.\r\n\r\nOur first target users are the 50 million researchers searching for scientific literature on PubMed and Google Scholar every month. We’ve have paying users across fields like medicine, ML, biotech, finance, and more.", "one_liner": "An AI agent for scientific research", "team_size": null, "industry": "B2B", "subindustry": "B2B", "launched_at": 1718632251, "tags": [ "Machine Learning", "Biotech", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/undermind", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/undermind.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/undermind-ai", "x_url": "https://twitter.com/UndermindAI", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/2e44c4453098a5caed0988fdf2dff5b898cdcac4.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": 1394068, "full_name": "Joshua Ramette", "title": "Founder/CEO", "founder_bio": "Josh is the co-founder and CEO at Undermind. He has research experience in gravitational-wave astrophysics (LIGO), particle physics (CERN), atomic physics, and quantum information science. He did his PhD at MIT studying quantum computing architecture design and inventing techniques for generating entangled multi-particle states using atom-light interactions, with 3 patents pending and 6 peer-reviewed publications.", "is_active": true, "linkedin_url": "https://linkedin.com/in/joshua-ramette-50984791", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b83cecf8c968f61bbefc70b6d5c28c2a7d512460.jpg" }, { "user_id": 1862556, "full_name": "Tom Hartke", "title": "Founder/CTO", "founder_bio": "Tom is the co-founder and CTO at Undermind. He studied physics at Princeton, then did his PhD in quantum physics at MIT. His thesis was recognized by the American Physical Society as one of the top 4 theses in the field of atomic physics in 2022, and he has published 10 papers in the field, including two in Nature and Science. Tom has deep expertise in building and deploying complex systems.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/thomas-hartke-229a0679/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7a52405c32e607c04ab8d9ec083887ee8c51205f.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 82132, "title": "🔍 Undermind - Radically better search for scientific research", "tagline": "Imagine sending a smart colleague to find exactly what you need", "body": "# **📝 TL;DR:**\n\n* [Undermind](https://undermind.ai/1) is Google for scientific research. It finds exactly what you need, no matter how complex, using an AI agent.\n* **Try it for free -** [www.undermind.ai](https://undermind.ai/1)\n\n# **———**\n\n---\n\n---\n\n---\n\n---\n\n# **❌ The Problem:**\n\nFinding the right resources to solve problems is crucial for doing impactful research. However, researchers often need to find really complex information, and tools like PubMed and Google Scholar simply aren’t up to the task. They only work for simple, short keyword phrases, and it often takes hours to sift through the (mostly irrelevant) results.\n\n# **✨ Our Solution:**\n\nWe rebuilt search from the ground up to make it work really well for complex questions. Our AI agent mimics how a human researcher carefully discovers information. It spends a few minutes carefully looking through hundreds of papers for you and adapts based on what it finds to uncover everything. Ultimately **Undermind’s results are 10-50x better** than traditional keyword search engines.\n\n![uploaded image](/media/?type=post&id=82132&key=user_uploads/1394068/4ce1e368-67de-47c7-af78-860c088daf7e)\n\n# **⚙️ How to use it**\n\nJust write down your complex research topic, describing exactly what you want, as if to a colleague. After 2-3 minutes, Undermind will give you the precisely relevant articles you need.\n\n![uploaded image](/media/?type=post&id=82132&key=user_uploads/1394068/8b54d81a-70b5-4001-b0a8-2235cf27e4b2)\n\n# **Why we’re doing this**\n\nWe’re scientists ourselves, and we’ve spent a lot of time looking through scientific literature. We’ve personally experienced those “aha” moments - when you realize you could have saved 3 months of work if only you knew about that one paper earlier! So, our goal isn’t to save you a few minutes of searching yourself; it’s to make it easy to find the solutions that can drastically improve your overall research plan and trajectory.\n\n# **——**\n\n# **🙏 Asks**\n\n* Try Undermind now - [www.undermind.ai](https://undermind.ai/1)\n* **Share** this post with anyone you know who reads research - it’s helpful for anyone, from machine learning to biotech, medicine, and engineering!", "url": "https://www.ycombinator.com/launches/LMi-undermind-radically-better-search-for-scientific-research", "votes": 50, "created_at": "2024-07-15T15:37:14.292Z" } ] }, { "id": 30090, "name": "Origami", "slug": "origami-2", "former_names": [ "Charm", "origami", "Origami", "Origami Agents" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/90a150650f55581e2db5d27ee09895ea19f1d1a7.png", "website": "https://origami.chat/", "all_locations": "San Francisco, CA, USA", "long_description": "Origami is the first AI that automatically gets customers for you. Enter your domain, and let it start working.\r\n\r\nUsed daily by 10,000+ businesses, Origami gets smarter everyday - learning from your campaigns to find better buyers and drive more revenue for you. On average, customers get 3-6x more positive replies than traditional outbound.\r\n\r\nOrigami combines Outbound, Lead Generation, Website Visitors Identification, Viral Content & more, starting at $29/month.", "one_liner": "Enter your domain & Origami gets you customers.", "team_size": 10, "industry": "B2B", "subindustry": "B2B -> Sales", "launched_at": 1731183180, "tags": [ "Workflow Automation", "Sales", "Search" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2024", "status": "Active", "industries": [ "B2B", "Sales" ], "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/origami-2", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/origami-2.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/origamiagents/", "x_url": "https://x.com/fin465", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/origami-agents", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/90a150650f55581e2db5d27ee09895ea19f1d1a7.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "David Lieb", "url": "https://www.ycombinator.com/people/david-lieb" }, "founders": [ { "user_id": 713453, "full_name": "Finn Mallery", "title": "Founder/CEO", "founder_bio": "Find your perfect customers with a prompt, start free → origami.chat", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/finn-m/", "x_url": "https://x.com/fin465", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d0d2b2bbc262de4cd6ba4c89d37a9e3140e7b3a8.jpg" }, { "user_id": 1856165, "full_name": "Kenson Chung", "title": "Founder/CTO", "founder_bio": "Former CTO of an enterprise sales platform. Engineered research workflows used by sales teams at companies with $1B+ in revenue. CS dropout from UCL.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/kenson-chung-837849230/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/01b96ddb8847e1e589d914149f38079a02f04640.jpg" } ], "jobs": [], "news": [ { "title": "Y Combinator’s hottest startup, Origami Agents, secures $2M seed round to supercharge sales teams with AI | VentureBeat", "url": "https://venturebeat.com/ai/y-combinators-hottest-startup-origami-agents-secures-2m-seed-round-to-supercharge-sales-teams-with-ai/", "date": "Jan 24, 2025" } ], "launches": [ { "id": 85537, "title": "Origami 🕊️ — Find who's looking", "tagline": "AI agents that find you companies actively searching for your solution", "body": "Hi everyone, we’re [Finn](https://www.linkedin.com/in/~finn/) and [Kenson](https://www.linkedin.com/in/~kenson/) - cofounders of Origami.\n\n### TL;DR\n\nWe train AI research agents to help you find leads at the point of maximum buying intent. Our agents scour the internet to surface the 99% of insights that exist beyond structured databases for you.\n\n\n\n### The problem: outbound sales in 2024 is broken\n\nResponse rates have hit rock bottom and quotas are going unmet while companies continue bombarding prospects pulled from the same databases with the same signals.\n\nThey're missing the one thing that matters most: reaching buyers when they're ready to purchase.\n\n### Our solution: Agents scouring the web to find you interested buyers\n\nAt our last companies, we solved this by building AI agents to make sense of niche sources — product reviews, google activity, linkedin engagements, and dozens of others. This immediately became the biggest lever of customer success.\n\n![uploaded image](/media/?type=post&id=85537&key=user_uploads/713453/a357b0d1-a22c-4eee-b08d-7a5ff25feeba)\n\nWe’re realizing the power of the entire internet’s unstructured data by building a generalized solution any company can make use of. Our agents work around the clock to spot high-potential buyers, providing actionable, human-like insights for each one.\n\n### We’ll find you buyers!\n\nThere’s enough information on the internet to know who already wants your solution, let Origami help you find it: [origami.chat](https://origami.chat)\n\nCan’t find time or have a question? Reach out to [finn@origamiagents.com](mailto:finn@origamiagents.com)\n\nSee our other launches on [X](https://x.com/fin465/status/1856774380233396309) and [LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:7262544301073530880/).", "url": "https://www.ycombinator.com/launches/MFd-origami-find-who-s-looking", "votes": 94, "created_at": "2024-11-13T17:43:00.486Z" } ] }, { "id": 30413, "name": "Serafis", "slug": "serafis", "former_names": [ "MetaSpark" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/67a815e7868c4a6d85c8b5fdb3324a29a6600f72.png", "website": "https://serafis.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Serafis is building the AI knowledge graph for market intelligence, starting with the information frontier. Trusted by top investors ($70B+ AUM) and the largest global consulting firms.", "one_liner": "Deep intelligence for private markets", "team_size": 4, "industry": "B2B", "subindustry": "B2B", "launched_at": 1755276267, "tags": [ "SaaS", "Finance", "Investing", "Search", "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/serafis", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/serafis.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/serafis/", "x_url": "https://x.com/serafis_ai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/67a815e7868c4a6d85c8b5fdb3324a29a6600f72.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 288934, "full_name": "Rohan Sharma", "title": "Founder/CEO", "founder_bio": "Rohan is the Founder and CEO of Serafis. He was formerly the Head of Data at Ribbit Capital ($12B AUM). Prior to that, he was a Software Engineer at Google, then bootstrapped and exited Kosmos — a data integrations SaaS for wealth managers. He studied Computer Science at Princeton University.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/r9a", "x_url": "https://x.com/r9a_co", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f49768f138c77dda85aaebb2a33a7aff6e10dbc3.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 102769, "title": "Serafis - the podcast app for investors", "tagline": "Narrative intelligence to help you find the next 10x idea", "body": "**TL;DR** Serafis helps you discover ideas worth paying attention to, from the voices that matter. Our data platform is already used by top asset managers and the world's largest consulting firms. Now, narrative intelligence fits in your pocket.\n\n> “Anytime somebody from one of the \\[AI\\] labs goes on a podcast … it’s so important to listen … it’s in my job description. There are some real needles in the haystack.”\n>\n> Gavin Baker, CIO of Atreides Management ($5B AUM)\n\n🎥 [Discover the next 10x idea with Serafis.](https://www.youtube.com/watch?v=979mS9_-Bs4)\n\n**Serafis indexes the information frontier**\n\nThe sharpest minds now go direct. CEOs, founders, researchers and investors increasingly share their perspectives on independent channels. Hearing from credible voices is one of the most effective ways to:\n\n* Generate new investment ideas\n* Research markets and companies\n* Develop your view on any given topic\n\n**The problem:** harnessing this information is a challenge. It’s scattered and buried across thousands of hours of audio.\n\nSerafis is building a structured, queryable knowledge graph for narratives – starting with podcasts. Search by person, company or theme across 100K+ curated transcripts.\n\n**We’re democratizing narrative intelligence**\n\nToday we’re launching the [Serafis mobile app](https://serafis.app): the world’s first podcast app for investors, with an AI-native interface.\n\n* Discover content relevant to the companies you follow\n* Explore a feed of the most insightful ideas found in podcasts\n* Ask questions about episodes, shows, or the Serafis library. Get answers with timestamped citations that take you to the exact quote.\n\nWe’ve thoughtfully integrated AI to help you find better information, faster. We also know how important it is to stay close to the source. For any episode, you can export, search, and highlight the full transcript.\n\n**Download the app now:**\n\n* [**iOS**](https://apps.apple.com/us/app/serafis/id6751523936)\n* [**Android**](https://play.google.com/store/apps/details?id=com.r9a.serafis.mobileui)\n\nWe’d love your feedback. Feel free to get in touch: [founders@serafis.ai](mailto:founders@serafis.ai)\n\nIf you’re a business interested in using our data platform, please [request a demo.](https://tally.so/r/gD0xxK)\n\n![uploaded image](/media/?type=post&id=102769&key=user_uploads/288934/86305a78-39e4-40ea-8dae-eb7ef9315e5e)\n\n", "url": "https://www.ycombinator.com/launches/QjZ-serafis-the-podcast-app-for-investors", "votes": 21, "created_at": "2026-06-08T23:06:52.317Z" } ] }, { "id": 30755, "name": "Pally", "slug": "pally", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/849f2561c7a883d8d5d6523f27f8404ee02820ed.png", "website": "https://pally.com", "all_locations": "San Francisco, CA, USA", "long_description": "You text Pally like you’d text a friend, and it connects to everything else: your inboxes, calendar, files, tasks and the web", "one_liner": "Your personal AI Assistant", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Productivity", "launched_at": 1750346506, "tags": [ "Consumer", "B2B", "Productivity", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "B2B", "Productivity" ], "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/pally", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/pally.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/pallyai", "x_url": "https://x.com/pally", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/pally-6028", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/849f2561c7a883d8d5d6523f27f8404ee02820ed.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": 978619, "full_name": "Haz Hubble", "title": "Founder/CEO", "founder_bio": "High-school dropout and super connector. Previously founded the London Founder House. Designer, salesman, engineer, accountant (weird mix but it works). Building at the intersection of AI x Human Relationships.", "is_active": true, "linkedin_url": "https://linkedin.com/in/hazhubble", "x_url": "https://www.x.com/hazhubble", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c37eee3db110066348edbfa4c228437896100809.jpg" }, { "user_id": 1287862, "full_name": "Wyatt Lansford", "title": "Founder/CTO", "founder_bio": "ML Engineer in robotics & autonomy. Won the DARPA AlphadogFight Trials, built autonomous planes & robots. Acquired by Shield AI. Now building human x AI interfaces.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/wyattlansford", "x_url": "https://x.com/wylansford", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d43ddd0152f12a0991b9729192a536030a3ebc00.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 91579, "title": "Pally - AI Relationship Management", "tagline": "All your pals, from all your socials.", "body": "![uploaded image](/media/?type=post&id=91579&key=user_uploads/978619/4443ca80-d250-46a5-8234-98a9d5852a59)\n\nHey 👋, we’re Haz and Wyatt, the founders of Pally.\n\nWe’re building Pally because in a world full of ‘AI SDRs’, our warm connections become more important than ever… Yet today, keeping track of thousands of connections across countless social platforms is impossible. \n\nWe forget to respond, we miss important moments, and we lose touch.\n\nWe’re building Pally to solve that.\n\nIf this resonates, we’d love your early feedback. Download Pally here: [www.pally.com](http://www.pally.com)\n\n# Demo\n\nhttps://www.youtube.com/watch?v=g97sd_roD14\n\n## How does it work?\n\nWe bring together all your connections, across all your socials (inc. iMessage, WhatsApp, LinkedIn, Google, Email, Calendar, and X), and then our AI researches everything they’ve ever posted online.\n\nPally uses this deep understanding of each relationship to help you:\n\n* Prepare you for meetings\n* Stay in touch with your potential customers/hires/investors (or just remind you to ring your mum from time to time)\n* Search over your network to find the right person for any query (”who do I know in New York that I’ve not spoken to in a while?”)\n* Add people to pipelines\n\nWe also added some fun relationship stats, so you can see how social you are compared to other Pally users, how each relationship is growing/declining over time, how long you take to reply vs how long others take to reply to you, and more.\n\n## Who’s it for?\n\nWe’re mostly focused on founders and investors, but we’ve also seen salespeople, recruiters, agency owners, and creators get a lot of value from Pally; essentially anyone in a role where relationships are a primary focus (and honestly, we believe in the world of AI, that’s _everyone_).\n\n## What's next?\n\nWe're excited to launch this into public beta today, learn from what our users are loving, and where they’re wanting more, and build that. \n\nOur roadmap includes adding integrations with AI meeting note takers, so all your relationship context is in one place, adding an iOS app so you have all your data on the go, and adding more agentic capabilities.\n\n# Ask\n\nPlease upvote us here and on [Product Hunt](https://www.producthunt.com/products/pally-ai-relationship-management), and give Pally a test and let us know how you get on! \n\nWe’re also hiring! If you’re interested in working with us, please email [hiring@pally.com](mailto:hiring@pally.com) with your cv.\n\nThanks for your support,\n\nHaz + Wyatt 🫶 ", "url": "https://www.ycombinator.com/launches/Np5-pally-ai-relationship-management", "votes": 149, "created_at": "2025-06-24T16:42:07.104Z" } ] }, { "id": 30913, "name": "Captain", "slug": "captain", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0c5e5da360891b80b4a1d6d511de963d613bbf08.png", "website": "https://captain.dev", "all_locations": "San Francisco, CA, USA", "long_description": "Captain makes product catalogs and complex, multimodal files searchable for customers and AI agents. It syncs files from sources like S3, SharePoint, and Google Drive and tunes search to your data and workloads.\r\n\r\nInstead of stitching together parsers, embeddings, vector databases, rerankers, and retrieval infrastructure, developers use Captain to power product search and file retrieval through managed APIs.\r\n\r\nOn evaluated file-search benchmarks, Captain’s tuning improved retrieval accuracy from roughly 78% with standard RAG to over 97%.\r\n\r\nBuilt for complex catalogs and demanding file-search workloads, including regulated and high-stakes applications.", "one_liner": "Self-tuning search for products and files", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1762492786, "tags": [ "B2B", "API", "Data Engineering", "Search", "Infrastructure" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2026", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/captain", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/captain.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/runcaptain/", "x_url": "https://x.com/captaindotdev", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/captain-technologies", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0c5e5da360891b80b4a1d6d511de963d613bbf08.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/8c35ef0b175d1d2ac7f51910ee2cfa78036029ea.png" ], "primary_partner": { "name": "Garry Tan", "url": "https://www.ycombinator.com/people/garry-tan" }, "founders": [ { "user_id": 2044865, "full_name": "Lewis Lien Polansky", "title": "Founder/CEO", "founder_bio": "CEO @ Captain, building self-tuning search for products & files. Engineer, designer, founder of a cybersecurity CTF org, recipient of U.S. Congressional recognition for community software, and randomly co-invented the soy-based milk carton.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/lewispolansky/", "x_url": "https://x.com/lienpolansky", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ecd86ae152b80dc12d55579cb0913bf93c33451f.jpg" }, { "user_id": 2578789, "full_name": "Edgar Babajanyan", "title": "Founder/CTO", "founder_bio": "CTO @ Captain | Published AI NLP Researcher | Built OCR engines @ Boar's Head | Scaled high-performance RAG pipelines for the past 4 years | Built a Cross Region L2 Datacenter", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/edgar-babajanyan-a28230217/", "x_url": "https://x.com/BabajanyanEdgar", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a4db53926ab205e15c802b0761d911b65a0e7321.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 95291, "title": "Captain - The Reliable Alternative to RAG", "tagline": "Search files with much higher accuracy than RAG (Avg improves 78% → 95%)", "body": "**TL; DR**: Captain delivers **the most accurate** general-purpose **knowledge search** engine **ever built.** \n\nIf you need to search through large text or multimodal files, and:\n\n* Don’t have time for **a tedious RAG sprint** -or-\n* Your current search pipeline lacks accuracy\n\nWe should talk. → [runcaptain.com/sales](https://runcaptain.com/sales?utm_source=ycombinator&utm_medium=launchyc&utm_campaign=launchyc&utm_content=weshouldtalk1) or [founders@runcaptain.com](mailto:founders@runcaptain.com)\n\nhttps://www.youtube.com/shorts/RxMPHqou94o\n\nIn a world where **90% of enterprise knowledge** cannot be stored in traditional databases, this ‘unstructured data’ is an **untapped goldmine** for decision-making. \n\nThe issue is that current RAG solutions have poor retrieval quality overall and are only performant on pre-optimized question types. \n\n![uploaded image](/media/?type=post&id=95291&key=user_uploads/2044865/425e4da0-2c47-43ea-a2c5-5dc32b2168fd)\n\n**Captain** unlocks **better responses** thanks to an effectively **Infinite Context window**. We distribute it across many LLMs in parallel + some embeddings sprinkled in, and then ‘Map-Reducing’ responses down to a single output.\n\n![uploaded image](/media/?type=post&id=95291&key=user_uploads/2044865/4be36851-322b-4836-be35-8e447cea51cd)\n\nWithout context limits, we have great freedom to optimize our retrieval engine for maximal accuracy. We can now play with a truly dynamic top-k or just run the LLM exhaustively (if a full knowledge audit is needed).\n\n![uploaded image](/media/?type=post&id=95291&key=user_uploads/2044865/e373def8-4aec-4add-b2a6-7d5fd9a02cb3)\n\n**We’re two lifelong builders** obsessed with data! \n\n* Lewis (right) solved hallucinations for code generation through his previous startup. \n* Edgar (left) researched NLP+AI and has built production RAG pipelines for the past 3 years.\n\nWe’ve seen the pains and limits of these systems from the start, and **the industry is primed for a more accurate alternative.** \n\nThis past summer, we met with every engineer we could at **Snowflake** and **Databricks**, and we kept hearing the same thing again and again: \n\n> There’s **no good** scalable **unstructured** data **search**.\n\nUntil **today**.\n\n**Captain abstracts the engineering of retrieval entirely.** Connecting files is all that’s needed, and we’ll beat your RAG's accuracy. Guaranteed. \n\n**Our Ask**\n\nIf you know a **CTO** or **Head of AI** at a mid-market or enterprise **AI-native** (or are building one yourself), we would love an introduction!\n\nCheck us out at [RunCaptain.com](https://runcaptain.com?utm_source=ycombinator&utm_medium=launchyc&utm_campaign=launchyc&utm_content=website)\\\nor reach us directly through [founders@runcaptain.com](mailto:founders@runcaptain.com)\\\n\\\nHappy Shipping! 🚢", "url": "https://www.ycombinator.com/launches/Omx-captain-the-reliable-alternative-to-rag", "votes": 115, "created_at": "2025-11-07T19:59:17.455Z" } ] } ]