[ { "id": 1073, "name": "Pathmind", "slug": "pathmind", "former_names": [ "Skymind" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6f25b083ae5853d0fb4e5eee2ce6794565ac6775.png", "website": "https://pathmind.com", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "Pathmind helps industrial engineers and simulation modelers achieve 10-20% gains in performance for industrial operations, supply chains and mining. We help them apply deep reinforcement learning to simulations and digital twins, and deploy decision-making policies into operations. ", "one_liner": "Pathmind helps industrial engineers and simulation modelers achieve…", "team_size": 15, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1447748416, "tags": [ "Reinforcement Learning", "Warehouse Management Tech", "Supply Chain" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2016", "status": "Inactive", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/pathmind", "api": "https://yc-oss.github.io/api/batches/winter-2016/pathmind.json" }, { "id": 12299, "name": "rct AI", "slug": "rct-studio", "former_names": [ "rct studio Inc.", "rct studio" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c512748c3ff81d34f08d930a9b0d1edc1f08e8d5.png", "website": "http://rct.ai", "all_locations": "Los Angeles, CA, USA", "long_description": "rct AI is providing AI solutions to the game industry and building the true Metaverse with AI generated content. By using cutting-edge technologies, especially deep learning and reinforcement learning, rct AI creates a truly dynamic and intelligent user experience both on the consumers’ side and production’s side.\r\n\r\nThe founding team ever built a company, Raventech together and helped make it acquired by Baidu (NASDAQ:BIDU) in 2017. ", "one_liner": "build AI NPCs", "team_size": 40, "industry": "Consumer", "subindustry": "Consumer -> Content", "launched_at": 1544133609, "tags": [ "Reinforcement Learning", "Gaming", "Metaverse" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2019", "status": "Active", "industries": [ "Consumer", "Content" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/rct-studio", "api": "https://yc-oss.github.io/api/batches/winter-2019/rct-studio.json" }, { "id": 25306, "name": "WorldQL", "slug": "worldql", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/77812b9a6c46bc50c4a0b0326c080c241d029714.png", "website": "https://www.worldql.com/", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "", "one_liner": "Custom AI harnesses for enterprises", "team_size": 4, "industry": "B2B", "subindustry": "B2B", "launched_at": 1648654841, "tags": [ "Artificial Intelligence", "Reinforcement Learning", "Security" ], "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": false, "url": "https://www.ycombinator.com/companies/worldql", "api": "https://yc-oss.github.io/api/batches/winter-2022/worldql.json" }, { "id": 27882, "name": "Velos", "slug": "velos", "former_names": [ "GradientJ" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ea18b8898b03249e01295dac0dccb51928c38c2e.png", "website": "https://www.tryvelos.com", "all_locations": "San Francisco, CA, USA", "long_description": "Velos helps non-technical operations teams automate complex, manual back-office tasks with AI workers instead of overseas teams.\r\n\r\nUnlike traditional robotic process automation (RPA) platforms like UiPath, Velos automations use machine learning to reliably handle ambiguities in their tasks, eliminating the need for an army of maintenance engineers and consultants to build and maintain your automations.\r\n\r\nWe're automating the repetitive work people hate to do.", "one_liner": "Platform to automate manual back-office work", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Operations", "launched_at": 1674503496, "tags": [ "Artificial Intelligence", "Generative AI", "Reinforcement Learning", "Robotic Process Automation", "Automation" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "B2B", "Operations" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/velos", "api": "https://yc-oss.github.io/api/batches/winter-2023/velos.json" }, { "id": 28214, "name": "Atmeto", "slug": "atmeto", "former_names": [ "Keeling Labs" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9903b97190d67a01064a67c7522d3703402c8f0c.png", "website": "https://www.atmeto.com", "all_locations": "Los Angeles, CA, USA", "long_description": "Founded in 2022, Atmeto was started as a place to develop and apply machine learning to solve the world's biggest problem—climate change. Our current priority is getting the grid to run on 100% clean energy, which is currently limited by battery storage (specifically, the algorithms that control them).\r\n\r\nWe're redefining these algorithms to unlock gigawatts of untapped energy storage capacity, enabling the grid to run on more clean energy from wind and solar.", "one_liner": "We develop ML that optimizes how batteries in the grid store energy", "team_size": 2, "industry": "Industrials", "subindustry": "Industrials -> Energy", "launched_at": 1674416288, "tags": [ "Energy Storage", "Reinforcement Learning", "Climate", "Energy", "ClimateTech" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Industrials", "Energy" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/atmeto", "api": "https://yc-oss.github.io/api/batches/winter-2023/atmeto.json" }, { "id": 29347, "name": "Vibrant Labs", "slug": "vibrant-labs", "former_names": [ "Exploding Gradients", "Ragas" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b03fadf62fa443c473d34c31148c9785ea34dceb.png", "website": "https://vibrantlabs.com/", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "We work on methods to autonomously scaling evals/environments for post-training agents. ", "one_liner": "Autonomously scaling environments", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1709606147, "tags": [ "Developer Tools", "Generative AI", "Reinforcement Learning", "Open Source", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/vibrant-labs", "api": "https://yc-oss.github.io/api/batches/winter-2024/vibrant-labs.json" }, { "id": 29353, "name": "Resonance", "slug": "resonance", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f32584f82882e696b3984dfcf4bd700cce69fd5f.png", "website": "https://www.useresonance.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Resonance hyper-personalizes MarTech campaign content and automatically refreshes and stores high performing content for re-use.", "one_liner": "Self-improving content bank for marketing", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Marketing", "launched_at": 1709332105, "tags": [ "Artificial Intelligence", "Reinforcement Learning", "SaaS", "Subscriptions", "Marketing" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "status": "Acquired", "industries": [ "B2B", "Marketing" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/resonance", "api": "https://yc-oss.github.io/api/batches/winter-2024/resonance.json" }, { "id": 29407, "name": "JustAI", "slug": "justai", "former_names": [ "Choice", "Choice AI", "just words", "Just Words" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/166536f94d496e2b50084b3b8edf8b6d9e29040b.png", "website": "https://getjust.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Always-on AI agents for 1-1 personalization at scale", "one_liner": "Agentic Marketing Platform", "team_size": 4, "industry": "B2B", "subindustry": "B2B -> Marketing", "launched_at": 1704840202, "tags": [ "Reinforcement Learning", "Workflow Automation", "Personalization", "Marketing", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2024", "status": "Active", "industries": [ "B2B", "Marketing" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": true, "app_answers": null, "question_answers": true, "url": "https://www.ycombinator.com/companies/justai", "api": "https://yc-oss.github.io/api/batches/winter-2024/justai.json" }, { "id": 29825, "name": "Sepal AI", "slug": "sepal-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/78c48b77fe0ad236d148f827dd2ba0b86450afd3.png", "website": "https://www.sepalai.com", "all_locations": "San Francisco, CA, USA", "long_description": "Sepal is a data research company on a mission to advance human knowledge and capabilities through safe AI.\r\n\r\nWe partner with the world’s leading AI labs and enterprises to help their models get better at the tasks people actually want them to do.\r\n\r\nWe’ve built a Cloud-Native Agent Dataset Factory which turns the process of generating evaluation and training data from manual, inconsistent, and labor-intensive into something automated, standardized, and scalable.\r\n\r\nSepal AI was founded in 2024 by engineers and operators from Vercel and Turing. We went through Y Combinator, raised several million dollars from leading investors, and already count multiple Fortune 500s and top AI research labs as paying customers.", "one_liner": "Data Development for Advanced AI", "team_size": 15, "industry": "B2B", "subindustry": "B2B", "launched_at": 1722966351, "tags": [ "AIOps", "Reinforcement Learning", "Data Labeling", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Acquired", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/sepal-ai", "api": "https://yc-oss.github.io/api/batches/summer-2024/sepal-ai.json" }, { "id": 30125, "name": "Synth", "slug": "synth-3", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/72c8ca1f80fbcb63a0a616d53bce31153601a954.png", "website": "https://www.usesynth.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Choose a coding agent harness, model, and task dataset and optimize context and prompts to get the best performance for long-horizon tasks", "one_liner": "Prompt and Context Optimization for Coding Agents", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1729021221, "tags": [ "Reinforcement Learning", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2024", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/synth-3", "api": "https://yc-oss.github.io/api/batches/fall-2024/synth-3.json" }, { "id": 30173, "name": "Aviro", "slug": "aviro", "former_names": [ "Tropir" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3625aa6dc8b9ca3fedb43ca1043c7246c8185306.png", "website": "https://www.aviro.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Aviro builds RL environments for long-horizon tool use across ML research, live web, and enterprise knowledge work. We partner with frontier labs and Fortune 100 companies to train models as high-stakes operators and perform research-grade work.", "one_liner": "Environments for Long Horizon Tool Use", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1746601065, "tags": [ "Reinforcement Learning" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 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": false, "url": "https://www.ycombinator.com/companies/aviro", "api": "https://yc-oss.github.io/api/batches/spring-2025/aviro.json" }, { "id": 30247, "name": "Osmosis", "slug": "osmosis", "former_names": [ "Gulp" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0e0e653e5da051b7c87a56070249ad76479b0594.png", "website": "https://osmosis.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Osmosis is a post-training platform that helps companies fine-tune language models using reinforcement learning. We work with fast-growing AI companies to train task/domain-specific models that beat foundation models on performance, cost, and latency. Our platform handles compute orchestration, reward modeling, and training run observability as a CLI-based product usable by developers and agents. ", "one_liner": "Reinforcement Learning (RL) for AI Agents", "team_size": 6, "industry": "B2B", "subindustry": "B2B", "launched_at": 1737656598, "tags": [ "Artificial Intelligence", "Machine Learning", "Reinforcement Learning", "Infrastructure" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 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": false, "url": "https://www.ycombinator.com/companies/osmosis", "api": "https://yc-oss.github.io/api/batches/winter-2025/osmosis.json" }, { "id": 30254, "name": "hud", "slug": "hud", "former_names": [ "Human Union Data", "hud", "HUD" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/53fffc4904f866d9d44ba9a47862b594f1d45667.png", "website": "https://www.hud.ai", "all_locations": "San Francisco, CA, USA", "long_description": "HUD (YC W25) is developing agentic evals and RL environments for Computer Use Agents (CUAs) that browse the web for frontier AI labs. Our CUA Evals framework is the first comprehensive evaluation tool for CUAs.\r\n\r\nPeople don't actually know if AI agents are working reliably. To make AI agents work in the real world, we need detailed evals for a huge range of tasks.\r\n\r\nWe're backed by Y Combinator, and work closely with frontier AI labs to provide agent evaluation and training infrastructure at scale.", "one_liner": "Platform for building RL environments and evals ", "team_size": 15, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1740930546, "tags": [ "Reinforcement Learning", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/hud", "api": "https://yc-oss.github.io/api/batches/winter-2025/hud.json" }, { "id": 30278, "name": "Agentin AI", "slug": "agentin-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/da9bdd65896a691965e96e56efdfaa0880fcac0e.png", "website": "https://www.agentin.ai", "all_locations": "San Francisco, CA, USA", "long_description": "At Agentin AI, we build Agents that move data and take actions across enterprise systems, like Salesforce, NetSuite and SAP. These agents are difficult to build because each enterprise heavily customizes their systems but we solved that by training our Agents to learn and adapt from failures, applying reinforcement learning techniques we developed.", "one_liner": "AI Agents that automate enterprise software processes", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1740032946, "tags": [ "Reinforcement Learning", "Enterprise", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 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": false, "url": "https://www.ycombinator.com/companies/agentin-ai", "api": "https://yc-oss.github.io/api/batches/winter-2025/agentin-ai.json" }, { "id": 30350, "name": "TrainLoop", "slug": "trainloop", "former_names": [ "Phoenix" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4fad47355d59021d6d855bc4b2dc95cbc0ef2f1a.png", "website": "http://trainloop.ai", "all_locations": "San Francisco, CA, USA", "long_description": "TrainLoop makes it effortless for developers to supercharge LLM performance through reinforcement learning.", "one_liner": "Reasoning Fine-Tuning", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1738700190, "tags": [ "Developer Tools", "Generative AI", "Reinforcement Learning" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/trainloop", "api": "https://yc-oss.github.io/api/batches/winter-2025/trainloop.json" }, { "id": 30352, "name": "Monte", "slug": "monte", "former_names": [ "Fluidize" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/213b59f051867cd51ae0008db2962b11e76fd9f3.png", "website": "https://trymonte.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Monte is an applied research lab building products for continual learning and recursive self-improvement for AI agents.", "one_liner": "Continual Learning for Agents", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1753994250, "tags": [ "Artificial Intelligence", "Reinforcement Learning", "B2B", "Infrastructure" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], 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"top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 2025", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/clado", "api": "https://yc-oss.github.io/api/batches/spring-2025/clado.json" }, { "id": 30488, "name": "Theta", "slug": "theta", "former_names": [ "Handler AI", "Theta Software" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bef5c2a34194462b75e30c574accec7a94eef920.png", "website": "https://thetasoftware.com", "all_locations": "San Francisco, CA, USA", "long_description": "", "one_liner": "Specialized AI for Every Job", "team_size": 7, "industry": "B2B", "subindustry": "B2B", "launched_at": 1746731099, "tags": [ "Artificial Intelligence", "Reinforcement Learning", "B2B" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 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": false, "url": "https://www.ycombinator.com/companies/theta", "api": "https://yc-oss.github.io/api/batches/spring-2025/theta.json" }, { "id": 30501, "name": "Cartpole", "slug": "cartpole", "former_names": [ "Prophet", "Jazzberry" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/154693ac9eca92a2d8f93ead7153a0858c38d2b3.png", "website": "https://cartpole.com", "all_locations": "San Francisco, CA, USA", "long_description": "We're creating reinforcement learning environments for training frontier models.", "one_liner": "Building reinforcement learning environments", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1744600545, "tags": [ "Artificial Intelligence", "Reinforcement Learning", "Data Labeling", "ML" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 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": false, "url": "https://www.ycombinator.com/companies/cartpole", "api": "https://yc-oss.github.io/api/batches/spring-2025/cartpole.json" }, { "id": 30521, "name": "Kairos", "slug": "kairos", "former_names": [ "Janus" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3b44f7b050ecc3ee1451baeb4e0a733b19937955.png", "website": "https://www.withkairos.co/", "all_locations": "San Francisco, CA, USA", "long_description": "Kairos closes the last-mile reliability gap in AI deployments. \r\n\r\nWe bring frontier techniques to companies in critical industries, deploying specialized agents that encode operator expertise and reliably automate their most manual workflows.", "one_liner": "Specialized AI for Critical Industries", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1748466933, "tags": [ "AIOps", "Machine Learning", "Reinforcement Learning", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 2025", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/kairos", "api": "https://yc-oss.github.io/api/batches/spring-2025/kairos.json" }, { "id": 30629, "name": "Topological", "slug": "topological", "former_names": [ "TipTop", "TipTop AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/2b9c845239fb1f16317889a4a843409e109b24f6.png", "website": "https://www.topological.com", "all_locations": "San Francisco, CA, USA", "long_description": "Topological is developing physics-based foundation models for CAD optimization. We help hardware teams iterate at the same speed that software teams do. Our technology is accelerating the engineering workflow with AI and scales design and optimization to identify the ideal designs for complex problems given their physical constraints with enhanced speed and performance. \r\n\r\nOur first model, UToP-v1, is a SOTA topology optimization model that understands physics, geometry, and manufacturability. It can generate the most efficient design given a problem’s physical requirements. It has <5% compliance error and is 1930x faster than current methods. We're reimagining mechanical engineering and computational design with precision spatial AI.", "one_liner": "Physics-based foundation models for CAD optimization.", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1755729176, "tags": [ "Reinforcement Learning", "Robotics", "Design Tools", "3D Printing", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/topological", "api": "https://yc-oss.github.io/api/batches/summer-2025/topological.json" }, { "id": 30643, "name": "Nuntius", "slug": "nuntius", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/419ca938d39201c4fff4ef02cd003dd413be8954.png", "website": "https://www.nuntius.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Applying artificial intelligence to economically productive use-cases requires bulletproof alignment. We’re solving that.", "one_liner": "Making Models Follow Rules.", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1754692935, "tags": [ "Artificial Intelligence", "Reinforcement Learning" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2025", "status": "Inactive", "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": false, "url": "https://www.ycombinator.com/companies/nuntius", "api": "https://yc-oss.github.io/api/batches/summer-2025/nuntius.json" }, { "id": 30730, "name": "Idler", "slug": "idler", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/be10a2d11184480de4bc819bb87298c7d2766cce.png", "website": "https://idler.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Idler builds reinforcement learning environments that teach AI models to code at expert human levels. We create training environments based on real-world coding scenarios that prepare models for the complex challenges they'll face in production.", "one_liner": "Reinforcement learning environments. ", "team_size": 13, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1749750449, "tags": [ "Reinforcement Learning" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/idler", "api": "https://yc-oss.github.io/api/batches/summer-2025/idler.json" }, { "id": 30851, "name": "Sylvian", "slug": "sylvian", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9ce6100b0248b0cf8f017986931a26cfe36046f3.png", "website": "https://sylvian.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Sylvian helps companies build specialized AI agents. ", "one_liner": "Specialized AI Agents.", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1762195938, "tags": [ "Reinforcement Learning", "B2B", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2025", "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": false, "url": "https://www.ycombinator.com/companies/sylvian", "api": "https://yc-oss.github.io/api/batches/fall-2025/sylvian.json" }, { "id": 30883, "name": "hillclimb", "slug": "hillclimb", "former_names": [ "Plun", "Hillclimb" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/660320374818f816f5cf14de03fe92dcab2740eb.png", "website": "https://hillclimb.com", "all_locations": "San Francisco, CA, USA", "long_description": "We work with frontier AI labs to help train their agents to become AI research scientists", "one_liner": "Training Data for Recursive Self-Improvement", "team_size": 4, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1759385382, "tags": [ "Reinforcement Learning" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Fall 2025", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/hillclimb", "api": "https://yc-oss.github.io/api/batches/fall-2025/hillclimb.json" }, { "id": 30930, "name": "Cortex AI", "slug": "cortex-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/1c9246b8eba5ba12a7373a930ea46cc687dddb98.png", "website": "https://cortexrobot.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Cortex AI builds the world’s most diverse and large-scale real-world workplace robot & egocentric dataset — where the physical world becomes the next training and evaluation set for embodied AI.\r\n\r\nWe power frontier labs developing robotics foundation models and general-purpose robots by providing the data they need:\r\n1️⃣ Egocentric Data — real-workplace human video with hand/body pose, depth, and subtask labels.\r\n2️⃣ Robot Data — trajectories collected from manipulators and humanoids in real industry settings.\r\n3️⃣ Human-in-the-Loop Rollouts & Evals — real-world deployments with remote operators who recover robots when they fail, capturing data that feeds back into training and continuously improves models.\r\n\r\nAdditionally, through the Cortex Marketplace, workplaces get paid to host data-collection and evaluation sessions, while labs access the in-the-wild data that truly matters. This draws on Lucas’s previous experience as co-founder of Carousell, a C2C marketplace that scaled to a $1B+ valuation.", "one_liner": "Large-scale real-world robot & human data for embodied AI", "team_size": 3, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1760045430, "tags": [ "Artificial Intelligence", "Reinforcement Learning", "Robotics" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2025", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/cortex-ai", "api": "https://yc-oss.github.io/api/batches/fall-2025/cortex-ai.json" }, { "id": 31191, "name": "Traverse", "slug": "traverse", "former_names": [ "Clice AI", "Clice" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/35a08dba94f5c7c4efe9e2c566eb21cc8f85b0e1.png", "website": "https://www.traverse.so", "all_locations": "San Francisco, CA, USA", "long_description": "", "one_liner": "Research lab solving non-verifiable work", "team_size": 1, "industry": "B2B", "subindustry": "B2B", "launched_at": 1767260558, "tags": [ "Machine Learning", "Reinforcement Learning", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2026", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/traverse", "api": "https://yc-oss.github.io/api/batches/winter-2026/traverse.json" }, { "id": 31285, "name": "GrazeMate", "slug": "grazemate", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/d2a4475db614107fb0effa347e625a6885ca8045.png", "website": "https://grazemate.com", "all_locations": "Sydney, NSW, Australia", "long_description": "GrazeMate builds autonomous drones that herd cattle.\r\n\r\nOn command, our drones fly to a paddock, position themselves around the mob, and move them where they need to go. What used to take a full day of helicopters, motorbikes, and horses now runs on a schedule.\r\n\r\nWe work with some of the largest cattle ranches in the world. While the drones are herding, they're also estimating animal weights, measuring grass biomass, monitoring water levels, and flagging sick animals. We're building physical AI that lets a grazier manage thousands of head across millions of acres from their phone.", "one_liner": "Robot Cowboys that Herd Cattle with AI Drones", "team_size": 3, "industry": "Industrials", "subindustry": "Industrials -> Agriculture", "launched_at": 1768433444, "tags": [ "Reinforcement Learning", "Drones", "Computer Vision", "Agriculture" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2026", "status": "Active", "industries": [ "Industrials", "Agriculture" ], "regions": [ "Australia", "Oceania", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/grazemate", "api": "https://yc-oss.github.io/api/batches/winter-2026/grazemate.json" }, { "id": 32988, "name": "Markov", "slug": "markov", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/455a1b80a115b180bc211a79ff00bc48bf9cb451.png", "website": "https://www.markovstudios.com/", "all_locations": "San Francisco, CA, USA", "long_description": "We source high quality tasks and data to train the next generation of computer use AI models.", "one_liner": "Data for computer-use ai", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1782277833, "tags": [ "Reinforcement Learning", "Data Labeling", "Data Engineering" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/markov", "api": "https://yc-oss.github.io/api/batches/summer-2026/markov.json" }, { "id": 33022, "name": "Praxis AI", "slug": "praxis-ai-2", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9bc18b804ad9a5d92ef1db88e90491f98e0204a7.png", "website": "https://www.praxisrobotics.io", "all_locations": "San Francisco, CA, USA", "long_description": "Praxis captures the data physical AI is bottlenecked on: egocentric video, 3D scans, and multimodal capture from inside real industrial and residential environments. Embedded within publicly listed and unicorn-scale conglomerates, we reach 60k workers across 5 continents and 150+ environment types, supplying the real-world training data frontier labs and humanoid companies can't source anywhere else.", "one_liner": "Turns every company into a data vendor ", "team_size": 3, "industry": "Industrials", "subindustry": "Industrials", "launched_at": 1782948528, "tags": [ "Hard Tech", "Reinforcement Learning", "Robotic Process Automation" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "Industrials" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": false, "url": "https://www.ycombinator.com/companies/praxis-ai-2", "api": "https://yc-oss.github.io/api/batches/summer-2026/praxis-ai-2.json" } ]