[
  {
    "id": 644,
    "name": "Whirlscape",
    "slug": "whirlscape",
    "former_names": [
      "Minuum"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3cc04b8a638bb1593e2663d97582844316a033cb.png",
    "website": "http://getdango.com",
    "all_locations": "Toronto, ON, Canada",
    "long_description": "Whirlscape high-tech startup with roots in human-computer interaction (HCI) and advanced machine learning/computational linguistics.",
    "one_liner": "Mobile keyboards + emoji + deep learning. We make Dango. We also made…",
    "team_size": 3,
    "industry": "Consumer",
    "subindustry": "Consumer -> Social",
    "launched_at": 1385119277,
    "tags": [
      "Deep Learning"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2014",
    "status": "Inactive",
    "industries": [
      "Consumer",
      "Social"
    ],
    "regions": [
      "Canada",
      "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/whirlscape",
    "api": "https://yc-oss.github.io/api/batches/winter-2014/whirlscape.json"
  },
  {
    "id": 748,
    "name": "Akido Labs",
    "slug": "akido-labs",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c312db8ea593be844305acc22a48eb2b87c24264.png",
    "website": "http://akidolabs.com",
    "all_locations": "Los Angeles, CA, USA",
    "long_description": "For the first time in history, technology exists to create a healthcare system that anticipates your needs, responds with precision and is accessible to everyone -- regardless of financial means or geography.\r\n\r\nSince 2015, Akido Labs singular focus has been to make this vision a reality.",
    "one_liner": "Rebuilding healthcare with AI at the core",
    "team_size": 1000,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Healthcare IT",
    "launched_at": 1416218485,
    "tags": [
      "Artificial Intelligence",
      "Deep Learning",
      "Healthcare",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2015",
    "status": "Active",
    "industries": [
      "Healthcare",
      "Healthcare IT"
    ],
    "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/akido-labs",
    "api": "https://yc-oss.github.io/api/batches/winter-2015/akido-labs.json"
  },
  {
    "id": 756,
    "name": "Numerion Labs",
    "slug": "numerion-labs",
    "former_names": [
      "Atomwise"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c3b218096497a702becc32895ca1b21c4cae78bd.png",
    "website": "https://www.numerionlabs.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Numerion Labs is an AI-native company accelerating the discovery of life-saving medicines through the development and use of cutting-edge machine learning algorithms. The company unites computational chemistry, structural biology, and medicinal chemistry to pioneer the next generation of AI-driven drug discovery platforms.",
    "one_liner": "Artificial intelligence for drug discovery.",
    "team_size": 67,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Drug Discovery and Delivery",
    "launched_at": 1416219336,
    "tags": [
      "AI-powered Drug Discovery",
      "Deep Learning",
      "Biotech",
      "Drug discovery",
      "Oncology"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2015",
    "status": "Active",
    "industries": [
      "Healthcare",
      "Drug Discovery and Delivery"
    ],
    "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/numerion-labs",
    "api": "https://yc-oss.github.io/api/batches/winter-2015/numerion-labs.json"
  },
  {
    "id": 856,
    "name": "Mashgin",
    "slug": "mashgin",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd7ff2b3db9bbbc82a79afababdad873d16b6e54.png",
    "website": "http://mashgin.com",
    "all_locations": "Palo Alto, CA, USA",
    "long_description": "Mashgin creates better retail experiences through visual automation. \r\n\r\nWe’ve built a self-checkout kiosk that uses computer vision to scan multiple items without barcodes, reducing checkout time by 10x. We’re completely recreating the checkout experience in an industry that’s had little innovation in decades.\r\n\r\nOur clients see dramatic reductions in lines and revenue increases of as much as 400% as a result.",
    "one_liner": "Self-Checkout using Computer Vision.",
    "team_size": 150,
    "industry": "B2B",
    "subindustry": "B2B -> Retail",
    "launched_at": 1421189374,
    "tags": [
      "Artificial Intelligence",
      "Cashierless Checkout",
      "Deep Learning",
      "Hardware",
      "Computer Vision"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Winter 2015",
    "status": "Active",
    "industries": [
      "B2B",
      "Retail"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Growth",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": true,
    "url": "https://www.ycombinator.com/companies/mashgin",
    "api": "https://yc-oss.github.io/api/batches/winter-2015/mashgin.json"
  },
  {
    "id": 999,
    "name": "Focal Systems",
    "slug": "focal-systems",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e25ba04ae8686807d958c48f1372502741b4b2e3.png",
    "website": "http://www.focal.systems",
    "all_locations": "San Francisco, CA, USA; Toronto, ON, Canada; London, England, United Kingdom",
    "long_description": "Focal Systems is on a mission to lower the cost of living for all mankind by automating and optimizing Brick and Mortar Retail with the latest advancements in AI. Focal Systems is the industry leader in retail automation solutions. By digitizing store shelves hourly and unleashing FocalOS, retailers unlock huge operational efficiencies, optimized merchandising, and streamlined supply chains which deliver impactful financial results. Focal is transforming retail by empowering store management to make automated, data-driven decisions. We are the operating system of retail.",
    "one_liner": "Building the Operating System for B&M Retail using Deep Learning",
    "team_size": 170,
    "industry": "B2B",
    "subindustry": "B2B -> Retail",
    "launched_at": 1447280416,
    "tags": [
      "Deep Learning",
      "Grocery",
      "Computer Vision"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2016",
    "status": "Active",
    "industries": [
      "B2B",
      "Retail"
    ],
    "regions": [
      "United States of America",
      "Canada",
      "United Kingdom",
      "America / Canada",
      "Europe",
      "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/focal-systems",
    "api": "https://yc-oss.github.io/api/batches/winter-2016/focal-systems.json"
  },
  {
    "id": 1033,
    "name": "Netomi",
    "slug": "netomi",
    "former_names": [
      "msg.ai"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a39a790ec2d3d1805e80c70308782d153f1c2294.png",
    "website": "https://netomi.com",
    "all_locations": "San Mateo, CA, USA",
    "long_description": "Welcome to Netomi AI, where we are revolutionizing the world of customer experiences through cutting-edge artificial intelligence. Our mission is clear: to create AI that not only solves mission-critical problems for the world's largest brands but also fosters genuine customer love. Backed by industry titans like Y-Combinator, Index Ventures, Jeffrey Katzenberg, and Greg Brockman, Netomi AI is at the forefront of defining the future of AI-driven customer engagement.\r\n\r\nAt Netomi, we embody our core values in everything we do:\r\n\r\nPassion: We love what we do, genuinely. We persevere, creatively problem solve, and grow from adversity.\r\n\r\nCustomer Focus: We obsess over creating a positive impact and deliver experiences that meet or exceed the needs of our customers, and their customers.\r\n\r\nOne Team: We believe in working collaboratively as One Team to meet shared objectives and goals.\r\n\r\nJoining Netomi AI means being part of a dynamic, fast-growing team that values innovation, creativity, and hard work. As a key player in the Generative AI revolution, you'll have the opportunity to significantly impact our success while developing your skills and career in the ever-evolving field of AI.\r\n\r\nNetomi AI is not just a workplace; it's a community of visionaries shaping the future of customer engagement. If you're ready to be part of something extraordinary, where your passion aligns with our values and vision, we invite you to explore opportunities with us. Your journey to redefine customer experiences with brand-safe AI starts here.\r\n\r\nMission\r\nTo empower the highest quality customer experiences with brand-safe AI.\r\n\r\nVision\r\nBuilding Brand and Customer Love",
    "one_liner": "Self-Driving Customer Care",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Marketing",
    "launched_at": 1447567509,
    "tags": [
      "Deep Learning",
      "Customer Service",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Winter 2016",
    "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": false,
    "app_answers": null,
    "question_answers": false,
    "url": "https://www.ycombinator.com/companies/netomi",
    "api": "https://yc-oss.github.io/api/batches/winter-2016/netomi.json"
  },
  {
    "id": 1663,
    "name": "D-ID",
    "slug": "d-id",
    "former_names": [
      "De-ID"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0bfa35a853487ffdadce50500de098ce5beb965d.png",
    "website": "http://www.d-id.com",
    "all_locations": "Tel Aviv-Yafo, Tel Aviv District, Israel",
    "long_description": "D-ID enables creators and developers to generate realistic high-quality AI personas easily and ethically through the use of our platform and APIs, based on deep-learning and AI-powered technology - Enabling Creative Reality™.\r\n\r\nD-ID is a Tel Aviv-based Creative Reality™ startup specializing in patented video reenactment technology using AI and deep learning. \r\nEstablished in 2017, D-ID created the first facial image de-identification solution to protect images and videos from facial recognition software. \r\nD-ID's products range from animating still photos to facilitating high-quality video productions and creating viral user experiences. ",
    "one_liner": "Enable creators and developers to generate realistic AI personas",
    "team_size": 27,
    "industry": "B2B",
    "subindustry": "B2B -> Security",
    "launched_at": 1493789454,
    "tags": [
      "Artificial Intelligence",
      "Deep Learning",
      "Generative AI",
      "Entertainment"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2017",
    "status": "Active",
    "industries": [
      "B2B",
      "Security"
    ],
    "regions": [
      "Israel",
      "Middle East and North Africa",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Growth",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": false,
    "question_answers": false,
    "url": "https://www.ycombinator.com/companies/d-id",
    "api": "https://yc-oss.github.io/api/batches/summer-2017/d-id.json"
  },
  {
    "id": 1729,
    "name": "Spellbrush",
    "slug": "spellbrush",
    "former_names": [
      "Sizigi",
      "Sizigi Studios"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/fb441dcc9519c0cc22e2464b6b1c3b1ca462403b.png",
    "website": "https://spellbrush.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Here at Spellbrush, we're passionate about making a good anime game.\r\n\r\nWe also happen to be the world's leading generative AI studio — we're the team behind niji・journey.\r\n\r\nWe are currently investigating how AI can be used to help human artists perform masterpieces in the most complex medium of our times: video games.\r\n\r\nOur games are characterized by a no-compromise approach to well-balanced gameplay married to a truthful love of visual arts.",
    "one_liner": "Making Anime Real",
    "team_size": 30,
    "industry": "Consumer",
    "subindustry": "Consumer -> Gaming",
    "launched_at": 1669939734,
    "tags": [
      "Artificial Intelligence",
      "Deep Learning",
      "Generative AI",
      "Gaming"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Winter 2018",
    "status": "Active",
    "industries": [
      "Consumer",
      "Gaming"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Growth",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": false,
    "url": "https://www.ycombinator.com/companies/spellbrush",
    "api": "https://yc-oss.github.io/api/batches/winter-2018/spellbrush.json"
  },
  {
    "id": 1759,
    "name": "Sepsis Scout",
    "slug": "sepsis-scout",
    "former_names": [
      "Patch'd Medical",
      "Patchd Medical",
      "Sepsis Scout (Formerly Patchd Medical)"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6e203795c935dfa82049aa5e5236b3db6130851e.png",
    "website": "https://sepsisscout.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Formerly known as Patchd Medical, now acquired by Cytovale.\r\n\r\nWearable technology and AI to predict and prevent sepsis outside of hospital.\r\n\r\n",
    "one_liner": "Wearables and AI to predict and prevent sepsis outside the hospital",
    "team_size": 5,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Healthcare IT",
    "launched_at": 1509599012,
    "tags": [
      "Deep Learning",
      "Health Tech",
      "Medical Devices"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2018",
    "status": "Acquired",
    "industries": [
      "Healthcare",
      "Healthcare IT"
    ],
    "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/sepsis-scout",
    "api": "https://yc-oss.github.io/api/batches/winter-2018/sepsis-scout.json"
  },
  {
    "id": 1760,
    "name": "Macromoltek",
    "slug": "macromoltek",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/22ef7592b0b7fa7b4f61dab016b46e8d7b52fcd6.png",
    "website": "https://www.macromoltek.com",
    "all_locations": "Austin, TX, USA",
    "long_description": "Macromoltek: Revolutionizing antibody design.\r\n\r\nDescription:  Macromoltek, a computational de novo drug design company, rapidly produces accurate and credible antibody designs. We have built a proprietary platform that enables design against difficult targets inaccessible by traditional methods and have are already designing antibodies for large biopharmas and smaller biotechs. ",
    "one_liner": "Computational antibody design",
    "team_size": 14,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Drug Discovery and Delivery",
    "launched_at": 1509599015,
    "tags": [
      "AI-powered Drug Discovery",
      "Deep Learning",
      "Therapeutics"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2018",
    "status": "Active",
    "industries": [
      "Healthcare",
      "Drug Discovery and Delivery"
    ],
    "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/macromoltek",
    "api": "https://yc-oss.github.io/api/batches/winter-2018/macromoltek.json"
  },
  {
    "id": 1823,
    "name": "Vathys",
    "slug": "vathys",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/14e804e717af07615be4e94540c5779418d30a78.png",
    "website": "http://vathys.ai",
    "all_locations": "Portland, OR, USA; Remote",
    "long_description": "Previously: Building machine learning hardware 10X faster than ASICs.\r\n\r\nAfter Pivot: Accelerating deep learning through software\r\n\r\nYC Winter 2018.",
    "one_liner": "Accelerating machine learning through software",
    "team_size": 6,
    "industry": "Industrials",
    "subindustry": "Industrials -> Manufacturing and Robotics",
    "launched_at": 1510167825,
    "tags": [
      "Artificial Intelligence",
      "Deep Learning",
      "Machine Learning"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2018",
    "status": "Inactive",
    "industries": [
      "Industrials",
      "Manufacturing and Robotics"
    ],
    "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/vathys",
    "api": "https://yc-oss.github.io/api/batches/winter-2018/vathys.json"
  },
  {
    "id": 1858,
    "name": "Jido Maps",
    "slug": "jido-maps",
    "former_names": [
      "MapSync"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/788dc05b2141606cea9f6805759306ec0ea99f5e.png",
    "website": "https://jidomaps.com/",
    "all_locations": "Berkeley, CA, USA; San Francisco, CA, USA; Remote",
    "long_description": "We help teams that may or may not have machine learning expertise quickly turn their data into deployed computer vision models. Example applications include security camera monitoring, automating data entry, interpreting web scraped images, validated user photo inputs, augmented reality and mobile product scanning. \r\n\r\nIf you have visual or scanned data that you wish your software could interpret at scale, we can turn around a first proof of concept in under a week. Reach out and see how computer vision can change your business.",
    "one_liner": "Turn your data into a deployed computer vision model",
    "team_size": 6,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1512529341,
    "tags": [
      "Deep Learning",
      "Indoor Mapping",
      "Machine Learning",
      "Computer Vision"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2018",
    "status": "Acquired",
    "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/jido-maps",
    "api": "https://yc-oss.github.io/api/batches/winter-2018/jido-maps.json"
  },
  {
    "id": 1910,
    "name": "Activeloop",
    "slug": "activeloop",
    "former_names": [
      "Snark.ai",
      "Snark AI"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c516ed5054847ecb1afb63f795f712b8d5c7f23d.png",
    "website": "https://activeloop.ai/",
    "all_locations": "San Francisco, CA, USA; Mountain View, CA, USA",
    "long_description": "We provide a simple API for creating, storing, versioning, and collaborating on multi-modal AI datasets of any size. With Activeloop's open-core stack, you can rapidly transform and stream data while training models at scale. Deep Lake powers foundational model training by acting as a vector database with significant benefits, such as (1) the ability to use multi-modal datasets to fine-tune your own LLM models, (2) storing both the embeddings and the original data with automatic version control, so no embedding re-computation is needed (3) truly serverless service with no vendor lock-in. How cool is that?\r\n\r\nGitHub loves us - we're one of the fastest-growing libraries there, and we're used by little-known companies like Google, Waymo, and Intel. No big deal. \r\n\r\nOur founding team hails from places like Princeton, Stanford, Google, and Tesla, and we're backed by Y Combinator & other Silicon Valley heavyweights. \r\n\r\nActiveloop is hiring, and we want you! Check out our open roles on our YC page and join the fun.\r\n\r\n10-min demo: https://activeloop.wistia.com/medias/aibvo0dst2\r\nWhitepaper: https://www.deeplake.ai/whitepaper",
    "one_liner": "Database for AI",
    "team_size": 15,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1524690140,
    "tags": [
      "Computational Storage",
      "Deep Learning",
      "Generative AI",
      "Computer Vision",
      "Open Source"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2018",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "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,
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    "id": 11895,
    "name": "Overview",
    "slug": "overview",
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    "website": "https://overview.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Here’s a secret between you and me: even the world’s largest manufacturers, companies like Tesla and Toyota, waste billions of dollars every year making products with quality issues. Building high-quality things at scale is incredibly hard. It doesn’t just happen because you hire smart people or buy good machines. It requires seeing problems early, understanding them deeply, and acting in real time, something factories were never designed to do.\r\n\r\nAt Overview.ai, we’re changing that. We build custom hardware, edge AI, and software systems that give manufacturers real visibility into how their products are actually being made. Our technology helps catch defects earlier, reduce waste, and fundamentally improve how factories operate. This work matters, not just for our customers, but for keeping American manufacturing competitive in a world that’s moving faster every year.",
    "one_liner": "Reshaping industrial quality with AI, hardware, and software",
    "team_size": 40,
    "industry": "Industrials",
    "subindustry": "Industrials -> Manufacturing and Robotics",
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    "id": 11994,
    "name": "Sapling.ai",
    "slug": "sapling-ai",
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    "website": "https://sapling.ai",
    "all_locations": "Los Angeles, CA, USA",
    "long_description": "Sapling offers an API and SDK to help businesses integrate language models into their applications. Its messaging assistant sits on top of CRMs and messaging platforms to help users more efficiently compose responses.",
    "one_liner": "Language models for enterprise applications.",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Productivity",
    "launched_at": 1541266318,
    "tags": [
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    "url": "https://www.ycombinator.com/companies/sapling-ai",
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    "id": 12611,
    "name": "AudioFocus",
    "slug": "audiofocus",
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    "website": "http://audiofocus.io",
    "all_locations": "Oakland, CA, USA",
    "long_description": "If we've learned anything in the last few years it's that spending time with our friends and family is essential to our well-being and happiness. Unfortunately, loud and noisy places like weddings and family gatherings hinder human connection, especially for the hearing impaired. In fact, the biggest unmet need in the entire hearing healthcare industry is hearing in noisy places. AudioFocus helps patients hear in these environments by only enhancing voices nearby the patients and ignoring ones farther away. We do this using acoustics informed machine learning and custom microphone array design.\r\n\r\nOur goal is to increase the adoption rate of 37M US adults with hearing loss, only 8M use hearing aids today.\r\n\r\nFounded by Auditory Neuroscientist & AI expert and a Hearing Aid hardware design expert.",
    "one_liner": "Hearing aids that work in noisy places, like restaurants, using ML.",
    "team_size": 4,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Medical Devices",
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    "url": "https://www.ycombinator.com/companies/audiofocus",
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    "id": 12846,
    "name": "Traces",
    "slug": "traces",
    "former_names": [
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    "website": "https://www.traces.ai",
    "all_locations": "San Francisco, CA, USA; Mountain View, CA, USA",
    "long_description": "We analyze thousands of video streams to find and track people without facial recognition. \r\nOur tech is available as an API and has multiple use cases. Unique people counting, forensic people search, falsa alarm filtering and many more. \r\n",
    "one_liner": "Transform your video monitoring with AI",
    "team_size": 10,
    "industry": "B2B",
    "subindustry": "B2B -> Security",
    "launched_at": 1559260859,
    "tags": [
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      "Deep Learning",
      "Computer Vision"
    ],
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    "top_company": false,
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    "nonprofit": false,
    "batch": "Summer 2019",
    "status": "Active",
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    ],
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      "America / Canada",
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      "Partly Remote"
    ],
    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/traces",
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    "id": 13144,
    "name": "Datasaur",
    "slug": "datasaur",
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    "website": "https://datasaur.ai",
    "all_locations": "San Francisco, CA, USA; Sunnyvale, CA, USA; Remote",
    "long_description": "Datasaur builds intelligent, optimized, human-centric LLM/NLP tools. If you're still using Excel or maintaining your own in-house tools, Datasaur can offer you significant cost-savings and improve the quality of your training data. We provide tools custom-built for power users. Our built-in intelligence helps augment your human labelers and avoid costly mistakes; our workforce management tool allows you to assign projects to and cross-validate the results from multiple labelers to ensure you can train your models with the utmost confidence.\r\n\r\nI have 10 years of consumer product experience. I'd be happy to help discuss product strategy and gamification.\r\nI'd love to get any advice you have to offer on SaaS and enterprise sales.",
    "one_liner": "Datasaur builds a data labeling workforce management platform for NLP.",
    "team_size": 55,
    "industry": "B2B",
    "subindustry": "B2B -> Engineering, Product and Design",
    "launched_at": 1583202722,
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      "Developer Tools"
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    "url": "https://www.ycombinator.com/companies/datasaur",
    "api": "https://yc-oss.github.io/api/batches/winter-2020/datasaur.json"
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    "id": 13504,
    "name": "Handl",
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    "former_names": [
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    "website": "https://handl.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Handl converts documents (invoices, income stabs, custom forms, etc.)  into structured data through simple API. 100% automation implies no need for any supervision/manual work on your side and allows you to reduce costs, improve response time, and operate accurate data. Powered by the merge of ML/AI + humans-in-the-loop to work in real-time even for the most complicated cases",
    "one_liner": "Handl converts documents (invoices, income stabs, custom forms, etc.)…",
    "team_size": 29,
    "industry": "B2B",
    "subindustry": "B2B -> Operations",
    "launched_at": 1584485742,
    "tags": [
      "Documents",
      "Deep Learning",
      "Fintech"
    ],
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    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/handl",
    "api": "https://yc-oss.github.io/api/batches/winter-2020/handl.json"
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  {
    "id": 21969,
    "name": "Nextera Robotics",
    "slug": "nextera-robotics",
    "former_names": [
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      "NeXtera Robotics",
      "NextEra Robotics"
    ],
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    "website": "https://nexterarobotics.com",
    "all_locations": "Boston, MA, USA",
    "long_description": "Nextera Robotics is an AI-native Robotics and Industrial Automation company founded at MIT.",
    "one_liner": "AI-native Robotics",
    "team_size": 20,
    "industry": "Industrials",
    "subindustry": "Industrials -> Manufacturing and Robotics",
    "launched_at": 1599762606,
    "tags": [
      "Artificial Intelligence",
      "Autonomous Delivery",
      "Deep Learning",
      "Robotics",
      "Construction"
    ],
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    "batch": "Summer 2020",
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    "regions": [
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      "America / Canada"
    ],
    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/nextera-robotics",
    "api": "https://yc-oss.github.io/api/batches/summer-2020/nextera-robotics.json"
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  {
    "id": 22008,
    "name": "Aquarium Learning",
    "slug": "aquarium-learning",
    "former_names": [
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    ],
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    "website": "https://www.aquariumlearning.com/",
    "all_locations": "San Francisco, CA, USA; Remote",
    "long_description": "ML models are only as good as the datasets they're trained on, and that means that most improvement to model performance comes from improvement to the quality and diversity of their datasets.\r\n\r\nOur tooling makes it easy for ML teams to find anomalies + failure patterns in their datasets and fix these problems by editing / adding the right data. So the next time you retrain your model, it just gets better.",
    "one_liner": "We help ML teams improve their models by improving their datasets",
    "team_size": 12,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1597122957,
    "tags": [
      "Deep Learning",
      "Developer Tools",
      "Generative AI",
      "Machine Learning",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
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    "nonprofit": false,
    "batch": "Summer 2020",
    "status": "Acquired",
    "industries": [
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      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Fully Remote"
    ],
    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/aquarium-learning",
    "api": "https://yc-oss.github.io/api/batches/summer-2020/aquarium-learning.json"
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  {
    "id": 22661,
    "name": "Quadrant Eye",
    "slug": "quadrant-eye",
    "former_names": [],
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    "website": "https://quadranteye.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Quadrant Eye is on a mission to improve eyecare access and fight preventable blindness. We're building the next generation eye exam... inside the average American home. \r\n\r\nOur team wants to enable patients' best and most visually rich lives. To this end, we're looking for curious and nimble minds to help us develop the world's first fully online eye exam and ultimately deliver high-quality eyecare at scale.\r\n\r\nSome QE highlights:\r\n\r\n🐝 We're a team of surgeons, scientists, venture partners, and hackers who mean business.\r\n💸 Our backers include Y Combinator, Khosla Ventures, and the creator of Google Image Search.\r\n🍥 We're a mission-driven company tackling an eye health market worth $USD137B.\r\n🌟 Our core focus is currently the development of patentable deep tech, and our overall vision is to change eyecare for good. \r\n",
    "one_liner": "Breaking new ground in online eyecare. ",
    "team_size": 4,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Medical Devices",
    "launched_at": 1614620673,
    "tags": [
      "Artificial Intelligence",
      "Deep Learning",
      "Digital Health",
      "Telemedicine"
    ],
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    "top_company": false,
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    "nonprofit": false,
    "batch": "Winter 2021",
    "status": "Inactive",
    "industries": [
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      "Medical Devices"
    ],
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      "America / Canada",
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      "Partly Remote"
    ],
    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/quadrant-eye",
    "api": "https://yc-oss.github.io/api/batches/winter-2021/quadrant-eye.json"
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  {
    "id": 22743,
    "name": "Segments.ai",
    "slug": "segments-ai",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ac88c08aefaf0753bc9d29d8d7ced656148b852c.png",
    "website": "https://segments.ai",
    "all_locations": "Brussels, Brussels, Belgium; Remote",
    "long_description": "[Segments.ai](http://segments.ai/) is helping robotics and automotive companies label their multi-sensor data for AI training and validation. Our platform enables customers to efficiently annotate their point cloud and image data, accelerating their path to autonomy.\r\n\r\nWe're a fast-growing, remote-first YC startup with a lean team and healthy runway. [Segments.ai](http://segments.ai/) is used by large organizations as well as innovative startups building the next generation of autonomous drones, delivery robots, self-driving cars, and more.",
    "one_liner": "Build better computer vision models by building better datasets",
    "team_size": 8,
    "industry": "B2B",
    "subindustry": "B2B -> Engineering, Product and Design",
    "launched_at": 1615300374,
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      "Developer Tools",
      "Computer Vision"
    ],
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    "batch": "Winter 2021",
    "status": "Acquired",
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    ],
    "regions": [
      "Belgium",
      "Europe",
      "Remote",
      "Fully Remote"
    ],
    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/segments-ai",
    "api": "https://yc-oss.github.io/api/batches/winter-2021/segments-ai.json"
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  {
    "id": 22855,
    "name": "Mindee",
    "slug": "mindee",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/be982807a0a8b2d7a22e6247219e680616ddd517.png",
    "website": "https://mindee.com",
    "all_locations": "Paris, Île-de-France, France; San Francisco, CA, USA; London, England, United Kingdom",
    "long_description": "Documents are everywhere. Through all industries, understanding their content is a critical step of many processes that software builders throughout the globe want to automate.\r\n\r\nWe focus on the science, the AI, the deep learning to give these builders the one API they need to automate the understanding of documents and give their software human-like superpowers.\r\n\r\nBased on three pillars:\r\n- A product: a universal self-service platform where builders can train their own models\r\n- A catalog of use-cases: We push critical use cases to the limits of human performances in critical markets such as Accounting software, AP Automation, Expense Management, KYC and many others\r\n- A internal capability to make tailor-made AI: We answer to specific needs from large customers that needs a custom AI of their own to handle their use cases",
    "one_liner": "AI Document Understanding API",
    "team_size": 60,
    "industry": "B2B",
    "subindustry": "B2B -> Engineering, Product and Design",
    "launched_at": 1623073794,
    "tags": [
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      "Deep Learning",
      "Developer Tools",
      "Automation",
      "APIs"
    ],
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    "nonprofit": false,
    "batch": "Winter 2021",
    "status": "Active",
    "industries": [
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      "Engineering, Product and Design"
    ],
    "regions": [
      "France",
      "United States of America",
      "United Kingdom",
      "Europe",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Growth",
    "app_video_public": false,
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    "url": "https://www.ycombinator.com/companies/mindee",
    "api": "https://yc-oss.github.io/api/batches/winter-2021/mindee.json"
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  {
    "id": 22866,
    "name": "Anima",
    "slug": "anima",
    "former_names": [
      "Continuum Health"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/d24874dbb8f1a038430d7e22f6fa5816224e777c.png",
    "website": "http://www.animahealth.com",
    "all_locations": "London, England, United Kingdom; Remote",
    "long_description": "Hey, thanks for reading! We founded Anima out of a very personal problem. Again and again, we saw people dying because they got bad care plans, weeks or months late. \r\n\r\nWe build Care Enablement for care teams - combining online consultation with productivity tools in a realtime multiplayer dashboard. By doing this, we get people optimal care within 24 hours.",
    "one_liner": "The next generation care enablement platform",
    "team_size": 20,
    "industry": "Healthcare",
    "subindustry": "Healthcare",
    "launched_at": 1614119144,
    "tags": [
      "Deep Learning",
      "SaaS",
      "Consumer Health Services"
    ],
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    "top_company": false,
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    "batch": "Winter 2021",
    "status": "Active",
    "industries": [
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    ],
    "regions": [
      "United Kingdom",
      "Europe",
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      "Fully Remote"
    ],
    "stage": "Growth",
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    "url": "https://www.ycombinator.com/companies/anima",
    "api": "https://yc-oss.github.io/api/batches/winter-2021/anima.json"
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  {
    "id": 23190,
    "name": "Biodock",
    "slug": "biodock",
    "former_names": [
      "Biodock AI"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ef18ff7a47b6ccba783538bb34567deafd6e5a0f.png",
    "website": "https://www.biodock.ai",
    "all_locations": "Austin, TX, USA",
    "long_description": "Biodock's cloud platform accelerates microscopy analysis, automating months of microscopy analysis and infrastructure to minutes with our end-to-end AI architecture.  Scientists enjoy auto-scaling storage, GPU compute, and 30-50% more accurate analysis.\r\n\r\nWe're building an amazing experience to translate microscopy images to therapeutic insights for academic and enterprise scientists.",
    "one_liner": "Cloud AI microscopy automation",
    "team_size": 8,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Therapeutics",
    "launched_at": 1614650681,
    "tags": [
      "Artificial Intelligence",
      "Deep Learning",
      "SaaS"
    ],
    "tags_highlighted": [],
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    "nonprofit": false,
    "batch": "Winter 2021",
    "status": "Active",
    "industries": [
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      "Therapeutics"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/biodock",
    "api": "https://yc-oss.github.io/api/batches/winter-2021/biodock.json"
  },
  {
    "id": 26718,
    "name": "Cerrion",
    "slug": "cerrion",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a2cfaa4757206398a9d1678a216905dc2a248f11.png",
    "website": "https://www.cerrion.com/",
    "all_locations": "Zürich, ZH, Switzerland",
    "long_description": "Cerrion helps manufacturers automatically detect, understand and eliminate problems on their production lines using video-based Computer Vision. Our AI leverages standard CCTV cameras and learns how a manufacturing process looks like when things are going well and can automatically detect and track problems in real-time.\r\n\r\nFor example, one of our customers, a Pepsi supplier producing 500 bottles per minute now automatically detects and reacts to a fallen bottle before it starts blocking their production line. ",
    "one_liner": "Video AI to automatically detect and respond to production line…",
    "team_size": 16,
    "industry": "Industrials",
    "subindustry": "Industrials",
    "launched_at": 1659519771,
    "tags": [
      "Artificial Intelligence",
      "Deep Learning",
      "Computer Vision",
      "Video",
      "Manufacturing"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2022",
    "status": "Active",
    "industries": [
      "Industrials"
    ],
    "regions": [
      "Switzerland",
      "Europe"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": false,
    "url": "https://www.ycombinator.com/companies/cerrion",
    "api": "https://yc-oss.github.io/api/batches/summer-2022/cerrion.json"
  },
  {
    "id": 28037,
    "name": "Diffuse Bio",
    "slug": "diffuse-bio",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/961a0aa4b25ffd0049a0526af8ad2406763125f4.png",
    "website": "http://diffuse.bio",
    "all_locations": "San Carlos, CA, USA",
    "long_description": "Diffuse is building generative AI for protein design. Our mission is to build AI systems that engineer new and useful proteins with unprecedented control and accuracy. Our team has been behind breakthroughs in AI protein design for the past 7 years, including the first experimental validation of AI-generated proteins and diffusion models for protein structure and sequence. ",
    "one_liner": "Generative AI for protein design",
    "team_size": 10,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Drug Discovery and Delivery",
    "launched_at": 1673220633,
    "tags": [
      "AI-powered Drug Discovery",
      "Deep Learning",
      "Generative AI",
      "Machine Learning",
      "Biotech"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Winter 2023",
    "status": "Active",
    "industries": [
      "Healthcare",
      "Drug Discovery and Delivery"
    ],
    "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/diffuse-bio",
    "api": "https://yc-oss.github.io/api/batches/winter-2023/diffuse-bio.json"
  },
  {
    "id": 28812,
    "name": "Cedana",
    "slug": "cedana",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/765d8a1a78a1c5939f354a26be87d3c1c1696290.png",
    "website": "https://cedana.ai",
    "all_locations": "New York, NY, USA; Remote",
    "long_description": "Cedana (YC S23) brings hyperscaler and frontier-lab orchestration capabilities for AI workflows. Our core capability is live migration for CPUs and GPUs workloads. This increases cost savings up to 80%, accelerates time to first token 2-10x, and enables stateful reliability of training jobs even through catastrophic GPU failures. We've integrated our solution into K8s, and support Kueue and Slurm for training distributed jobs, and Kserve for serving inference.    \r\n\r\nOpenAI, Meta and Microsoft have flavors of these capabilities internally and we’re bringing them to everyone. \r\n\r\nOur vision is to transform cloud compute into a real-time, arbitraged commodity. \r\n\r\nhttps://www.cedana.ai",
    "one_liner": "Fast, reliable, reproducible AI with GPU live migration",
    "team_size": 5,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1689189501,
    "tags": [
      "Deep Learning",
      "Developer Tools",
      "Cloud Computing",
      "Infrastructure",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2023",
    "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/cedana",
    "api": "https://yc-oss.github.io/api/batches/summer-2023/cedana.json"
  },
  {
    "id": 28998,
    "name": "Automorphic",
    "slug": "automorphic",
    "former_names": [
      "Sibyl"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6632890863ba8f97b3c98f84ce71158a24887e9a.png",
    "website": "https://automorphic.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Automorphic has invented a way to infuse knowledge into LLMs via fine-tuning (surpassing context window limitations), enabling developers to rapidly iterate on and successively improve custom models cheaply and efficiently.",
    "one_liner": "Infuse knowledge into language models with just 10 samples",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B",
    "launched_at": 1691588844,
    "tags": [
      "AIOps",
      "Deep Learning",
      "Developer Tools",
      "Infrastructure",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2023",
    "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/automorphic",
    "api": "https://yc-oss.github.io/api/batches/summer-2023/automorphic.json"
  },
  {
    "id": 29375,
    "name": "Preloop",
    "slug": "preloop",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/62ebbbabf82f9c9badd0bbcb60f8749d391d5a78.png",
    "website": "https://www.preloop.com",
    "all_locations": "Seattle, WA, USA",
    "long_description": "Only 2 out of 10 ML models make it from experiment to production. Preloop helps automate the process of deployment, helping companies realize more value from their machine learning teams, while focusing teams' attention on science instead of engineering.",
    "one_liner": "Translate your experimental scripts into production ML services",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Engineering, Product and Design",
    "launched_at": 1702654646,
    "tags": [
      "Artificial Intelligence",
      "Deep Learning",
      "Developer Tools",
      "Machine Learning",
      "Data Science"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2024",
    "status": "Active",
    "industries": [
      "B2B",
      "Engineering, Product and Design"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": false,
    "url": "https://www.ycombinator.com/companies/preloop",
    "api": "https://yc-oss.github.io/api/batches/winter-2024/preloop.json"
  },
  {
    "id": 29412,
    "name": "Ligo Biosciences",
    "slug": "ligo-biosciences",
    "former_names": [
      "Ligo",
      "Ligo Bio"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd381079ddb420940e9b57512009e6b5bc73e27a.png",
    "website": "https://www.ligo.bio",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We are building the next generation of deep-learning models for enzyme design to slash the cost of chemical manufacturing. The $6 trillion chemical industry is flawed: It produces 20% of industrial greenhouse gases, and is responsible for 15% of global energy usage. \r\n\r\nEnzymes offer a far more sustainable alternative to chemical synthesis and have already revolutionised how a select few chemicals are produced. The problem is each enzyme takes years of trial and error to develop. Our enzyme models learn the principles of catalysis, allowing us to design enzymes for each reaction, in days not years. \r\n",
    "one_liner": "Enzyme design models.",
    "team_size": 4,
    "industry": "Healthcare",
    "subindustry": "Healthcare -> Industrial Bio",
    "launched_at": 1723173017,
    "tags": [
      "Artificial Intelligence",
      "Deep Learning",
      "Synthetic Biology",
      "Biotech",
      "Climate"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2024",
    "status": "Active",
    "industries": [
      "Healthcare",
      "Industrial Bio"
    ],
    "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/ligo-biosciences",
    "api": "https://yc-oss.github.io/api/batches/summer-2024/ligo-biosciences.json"
  },
  {
    "id": 29713,
    "name": "Anthrogen",
    "slug": "anthrogen",
    "former_names": [
      "Arctic Capture"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b82e4439479abc4af4baa257faf13f88240781b4.png",
    "website": "https://anthrogen.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Proteins power everything from the cells in your body to creating materials you rely on every day—but until now, we’ve been forced to discover their functions by trial and error. Designing a new therapeutic can take decades and billions of dollars, and even our best industrial catalysts work at a snail’s pace compared to their theoretical optimums.\r\n\r\nAnthrogen is changing that. By training massive AI foundation models on protein sequences and structures, we’ve unlocked the ability to generate—on demand—completely novel molecular machines with atomic-level precision. Simply describe the function you need, and our platform imagines the peptide or protein that will deliver it.\r\n\r\nWe're building models to speed up billions of years of evolution into the span of an afternoon's worth of compute.\r\n\r\nThe result? New-to-nature therapies, ultra-efficient catalysts for sustainable manufacturing, and a whole new frontier of molecular innovation—designed as precisely as any cutting-edge aircraft or microchip.",
    "one_liner": "We're training the next generation of protein foundation models.",
    "team_size": 6,
    "industry": "Healthcare",
    "subindustry": "Healthcare",
    "launched_at": 1717790065,
    "tags": [
      "Artificial Intelligence",
      "Deep Learning",
      "Biotech"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2024",
    "status": "Active",
    "industries": [
      "Healthcare"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": false,
    "url": "https://www.ycombinator.com/companies/anthrogen",
    "api": "https://yc-oss.github.io/api/batches/summer-2024/anthrogen.json"
  },
  {
    "id": 30077,
    "name": "AutoComputer",
    "slug": "autocomputer",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/5a60f5ba14460a149ec6587f93611a37f06bcb11.png",
    "website": "https://www.autocomputer.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "AutoComputer is a desktop robotic process automation system.  Given just a text prompt, our AI automates tedious tasks such as financial data entry by performing all the clicks and keystrokes for you.",
    "one_liner": "Desktop RPA with AI computer use",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B",
    "launched_at": 1731450144,
    "tags": [
      "Artificial Intelligence",
      "Deep Learning",
      "B2B",
      "Enterprise",
      "Automation"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Fall 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": false,
    "url": "https://www.ycombinator.com/companies/autocomputer",
    "api": "https://yc-oss.github.io/api/batches/fall-2024/autocomputer.json"
  },
  {
    "id": 30771,
    "name": "Flywheel AI",
    "slug": "flywheel-ai",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/20c7285daed594e49818c41dd7e677796b8e4b61.png",
    "website": "https://useflywheel.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Flywheel AI converts any existing excavators for contractors to enable remote ops to increase safety and productivity, and use robotics context dataset to train autonomous policies.",
    "one_liner": "Waymo for excavators",
    "team_size": 2,
    "industry": "Industrials",
    "subindustry": "Industrials -> Manufacturing and Robotics",
    "launched_at": 1750156128,
    "tags": [
      "Deep Learning",
      "Hardware",
      "Construction"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2025",
    "status": "Active",
    "industries": [
      "Industrials",
      "Manufacturing and Robotics"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": false,
    "url": "https://www.ycombinator.com/companies/flywheel-ai",
    "api": "https://yc-oss.github.io/api/batches/summer-2025/flywheel-ai.json"
  },
  {
    "id": 30904,
    "name": "Tensr",
    "slug": "tensr",
    "former_names": [
      "Core",
      "Tensr",
      "Core"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7f3743327b1fc85ded6caddbd532e0c600eb2cb0.png",
    "website": "https://tensr.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Tensr is building fully autonomous robotic factories that make scaling hardware as effortless as scaling on AWS. We’re a group of Berkeley graduate robotics researchers who previously won a full scale autonomous IndyCar competition at 160mph.",
    "one_liner": "Fully autonomous robotic factories",
    "team_size": 3,
    "industry": "Industrials",
    "subindustry": "Industrials -> Manufacturing and Robotics",
    "launched_at": 1763065446,
    "tags": [
      "Deep Learning",
      "Hardware",
      "Robotics",
      "Automation",
      "Industrial"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Fall 2025",
    "status": "Active",
    "industries": [
      "Industrials",
      "Manufacturing and Robotics"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": false,
    "url": "https://www.ycombinator.com/companies/tensr",
    "api": "https://yc-oss.github.io/api/batches/fall-2025/tensr.json"
  },
  {
    "id": 30961,
    "name": "Arcten",
    "slug": "arcten",
    "former_names": [
      "Cortex",
      "ArcTen",
      "Arc Ten",
      "ArcTen",
      "ArcTen, Inc.",
      "Arcten, Inc."
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b8b85a37c57824f6296369d2840bc02a481f8bf6.png",
    "website": "https://arcten.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "",
    "one_liner": "Lab working on long-horizon autonomous research and coding agents",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Engineering, Product and Design",
    "launched_at": 1761245566,
    "tags": [
      "Deep Learning",
      "Generative AI",
      "Machine Learning",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "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/arcten",
    "api": "https://yc-oss.github.io/api/batches/fall-2025/arcten.json"
  },
  {
    "id": 31019,
    "name": "Ndea",
    "slug": "ndea-com",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/51a1310dd1344aa031a62430dad084db50b7229d.png",
    "website": "https://ndea.com",
    "all_locations": "Remote",
    "long_description": "Ndea is building frontier AI systems that blend intuitive pattern recognition and formal reasoning into a unified architecture.\r\n\r\n# AI for Scientific Advancement\r\n\r\nUnlike all life before us, humanity's ascent is a story of ingenuity, not just biological evolution. Our progress has been driven by the curiosity to acquire knowledge, the ability to pass it on, and an intrinsic drive to innovate. We build technology which gives us leverage beyond our biology.\r\n\r\nWe stand at the top of a knowledge and technology colossus that we collectively created over the past ten thousand generations. Scientific progress has helped us overcome burdens that long defined human life — famines, plagues, and widespread illiteracy. Science will continue to redefine the boundaries of the human condition.\r\n\r\nToday, the acceleration of scientific progress hinges on one factor: AI capable of independent invention and discovery. This capacity is the gateway to advancements beyond our wildest imagination.\r\n\r\n# A New Research Lab\r\n\r\nWe're starting Ndea — an AI research and science lab. The name - like 'idea' with an 'n' - is inspired by the Greek concepts ennoia (intuitive understanding) and dianoia (logical reasoning), capturing our first goal to merge deep learning with program synthesis.\r\n\r\nNdea is entirely focused on developing and operationalizing AGI to realize unprecedented scientific progress in our lifetime for the benefit of all current and future generations.\r\n\r\nBuilding AGI alone is a monumental undertaking, but our mission is even bigger. We're creating a factory for rapid scientific advancement — a factory capable of inventing and commercializing N ideas.\r\n\r\nFrom our vantage point today, we see many 'known' frontiers like self-driving vehicles, drug discovery, sustainable energy, robotics, and space exploration. While AGI will benefit all of these, the most exciting adventure lies in the 'unknown'. AGI promises discoveries and progress we cannot imagine today.",
    "one_liner": "Building AGI that can innovate.",
    "team_size": 15,
    "industry": "Industrials",
    "subindustry": "Industrials",
    "launched_at": 1772669917,
    "tags": [
      "Artificial Intelligence",
      "Deep Learning",
      "Hard Tech",
      "Remote Work",
      "ML"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2026",
    "status": "Active",
    "industries": [
      "Industrials"
    ],
    "regions": [
      "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/ndea-com",
    "api": "https://yc-oss.github.io/api/batches/winter-2026/ndea-com.json"
  },
  {
    "id": 31516,
    "name": "Expanse",
    "slug": "expanse",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/26ca5cac6157441dd12d0352f7d3fd4a77c4c39a.png",
    "website": "https://expanse.sh",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Expanse unlocks wasted GPU capacity. We recover idle compute through three capabilities: resource prediction (right-sizing job submissions before they reach the scheduler), optimisation suggestions (code and config changes researchers can apply themselves), and failure prediction (catching jobs that will fail before they consume hours of GPU time).\r\n\r\nWe’re four engineers. We ran HPC and GPU training workloads at the largest quant funds and national supercomputing centres. We faced this problem first hand and the only fix was to over-provision and burn millions. Ismaeel built the first multimodal HPC resource predictor as research at EPCC (Edinburgh’s Parallel Computing Centre), which beat every published baseline. This is the tool we wish we had.",
    "one_liner": "Unlock wasted GPU capacity.",
    "team_size": 4,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1777849866,
    "tags": [
      "Deep Learning",
      "B2B",
      "Infrastructure",
      "ML"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Spring 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/expanse",
    "api": "https://yc-oss.github.io/api/batches/spring-2026/expanse.json"
  },
  {
    "id": 33805,
    "name": "Edviro",
    "slug": "edviro",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c179a28044a3d4a8c3ceaf2a5b8cecbf90f66aa7.png",
    "website": "https://edviroenergy.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Edviro helps facilities managers at large buildings save on their energy bills. Facilities managers are overwhelmed with dashboards, alarms, and analytics for hundreds of boilers, HVAC units, and heat pumps across their buildings. Edviro continuously monitors and optimizes buildings using energy world models to detect anomalies, simulate interventions, and suggest the best options to the facilities manager.  Once the changes occur, we create a comprehensive M&V report to ensure savings show up on the bill. \r\n\r\nOur tools also allow institutions to maintain detailed service history & equipment logs, and perform maintenance with AR.  ",
    "one_liner": "Energy world models that fix energy waste before it becomes expensive.",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1782807553,
    "tags": [
      "Deep Learning",
      "B2B",
      "Energy"
    ],
    "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/edviro",
    "api": "https://yc-oss.github.io/api/batches/summer-2026/edviro.json"
  }
]