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We automatically classify your website and mobile app's event data and stop bad actors at scale.", "one_liner": "Smyte stops bad actors on social networks and marketplaces.", "team_size": 11, "industry": "B2B", "subindustry": "B2B -> Security", "launched_at": 1416218728, "tags": [ "Security", "Fraud Detection" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2015", "status": "Acquired", "industries": [ "B2B", "Security" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/smyte", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2015/smyte.json", "year_founded": null, "city": "San Francisco", "country": "US", "linkedin_url": null, "x_url": null, "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/smyte", "github_url": null, "logo_url": null, "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 4251, "full_name": "Pete Hunt", "title": "Founder", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/profile/view?id=21952666", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7c3090b2a38b82916117ac3322340892e643ffc1.jpg" }, { "user_id": 11182, "full_name": "Julian Tempelsman", "title": "Founder", "founder_bio": null, "is_active": true, "linkedin_url": "https://www.linkedin.com/profile/view?id=130147198", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/dbbe11997e34b99304fbb5cb16c0efd3310c4e12.jpg" }, { "user_id": 8547, "full_name": "Josh Yudaken", "title": "Founder/Lead Infrastructure Engineer", "founder_bio": null, "is_active": true, "linkedin_url": "http://za.linkedin.com/pub/josh-yudaken/5/ba8/958", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fcdc8b6f69e2d5fc0e43265fc1487d054aca9e53.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 1032, "name": "Castle", "slug": "castle-2", "former_names": [ "Castle.io", "Castle", "Castle Intelligence" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/8d7fdbafd7f8ab52a136ccd6f568c251351c96b8.png", "website": "https://castle.io", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "Instantly stop automated and human-originated account takeovers, fake accounts, and any behavior that violates your platform policies.", "one_liner": "Block bots & bad behavior", "team_size": 20, "industry": "B2B", "subindustry": "B2B -> Security", "launched_at": 1447566615, "tags": [ "Developer Tools", "SaaS", "Security", "Fraud Detection" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2016", "status": "Active", "industries": [ "B2B", "Security" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/castle-2", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2016/castle-2.json", "year_founded": null, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/castle-intelligence", "x_url": "https://twitter.com/Castle_IO", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/castle-2", "github_url": "https://github.com/castle", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/8d7fdbafd7f8ab52a136ccd6f568c251351c96b8.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 40815, "full_name": "Johan Brissmyr", "title": "Founder/CEO", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/brissmyr", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7b325a5454671a0c325a49b2f9b6940c8af0b4c0.jpg" }, { "user_id": 40816, "full_name": "Sebastian Wallin", "title": "Founder/CTO", "founder_bio": "", "is_active": true, "linkedin_url": "http://se.linkedin.com/in/sebwal/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/113e2eb5b57b288539628814d18c16361bc14ca9.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 1917, "name": "Inscribe", "slug": "inscribe", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/56ca6c4bee93b67aa430b76fd900b6de1e467c6a.png", "website": "https://www.inscribe.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Inscribe is AI-powered document fraud detection software for banks, credit unions, and lenders. It detects forged, fabricated, and AI-generated financial documents (including deepfake bank statements, manipulated pay stubs, and synthetic tax forms) in seconds, giving risk teams the clarity they need to stop fraud without slowing approvals.\r\n\r\nInscribe created the document fraud detection category and has been purpose-built for financial institutions since 2017. Our agentic AI is trained on millions of real-world applications and strengthened by in-house data scientists and fraud experts. According to Inscribe's 2026 State of Document Fraud Report, 1 in 16 documents show signs of fraud — with AI-generated document fraud growing 5x from April to December 2025.\r\n\r\nCustomers include BCU ($5.6M in prevented fraud losses), Logix Federal Credit Union ($3M in losses prevented in 8 months), and Ramp (30-minute reduction in application processing). Inscribe is SOC 2 Type II and ISO 27001 certified, and is backed by Threshold Ventures, Uncork Capital, Y Combinator, Crosslink Capital, and Foundry.", "one_liner": "AI Document Fraud Detection for Banks, Credit Unions & Lenders", "team_size": 35, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1524711773, "tags": [ "Fintech", "Fraud Detection", "AI" ], "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": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/inscribe", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2018/inscribe.json", "year_founded": 2017, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/inscribe", "github_url": "https://github.com/InscribeAI", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/56ca6c4bee93b67aa430b76fd900b6de1e467c6a.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 200233, "full_name": "Ronan Burke", "title": "Founder/CEO", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/rnnbrk/", "x_url": "https://twitter.com/rnnbrk", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/361681c3e4cb93c53eeaa26266cc30665aa56a4a.jpg" }, { "user_id": 126670, "full_name": "Conor Burke", "title": "Founder/CTO", "founder_bio": null, "is_active": true, "linkedin_url": "https://www.linkedin.com/in/conorbrk/", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0c3673dcdfd462ac2f076ee20303e9c540159767.jpg" } ], "jobs": [], "news": [ { "title": "Inscribe bags $25M to fight financial fraud with AI • TechCrunch", "url": "https://techcrunch.com/2023/01/26/inscribe-bags-25m-to-fight-financial-fraud-with-ai/", "date": "Jan 26, 2023" }, { "title": "Announcing our Series B: We’ve raised $25M to help companies build digital trust with Risk Intelligence", "url": "https://www.inscribe.ai/blog/announcing-our-series-b?utm_source=linkedin&utm_campaign=2022-Q4-Series-B-Launch&utm_medium=organicsocial", "date": "Jan 25, 2023" } ], "launches": [] }, { "id": 23796, "name": "Slope", "slug": "slope", "former_names": [ "AirDesk" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bb362ee6e3bfce450e6a8196b555db1534feef1b.png", "website": "http://www.slopepay.com", "all_locations": "San Francisco, CA, USA", "long_description": "Accept and reconcile B2B payments seamlessly.", "one_liner": "The B2B Payments Platform ", "team_size": 16, "industry": "Fintech", "subindustry": "Fintech -> Payments", "launched_at": 1625009192, "tags": [ "Fintech", "Machine Learning", "Payments", "Fraud Detection", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "Fintech", "Payments" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/slope", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/slope.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/slope-tech/", "x_url": "https://twitter.com/slopepay", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/bb362ee6e3bfce450e6a8196b555db1534feef1b.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Brad Flora", "url": "https://www.ycombinator.com/people/brad-flora" }, "founders": [ { "user_id": 135035, "full_name": "Lawrence Lin Murata", "title": "Founder", "founder_bio": "Lawrence Lin Murata, co-founder and CEO. Lawrence's family spent 3 decades in wholesale, which inspired us to build Slope. Previously, Lawrence founded Newton Technologies (acq by Nauto), which used AI to analyze smartphone data and help people drive better. Later, he led AI Platforms & Data Science at Nauto, a 200-people autonomous driving company. Lawrence is a Stanford grad, where he led and co-founded CS+Social Good, a tech for impact organization.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/lawrencelm", "x_url": "https://twitter.com/LawLM", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4781d65c774c5fe5630c27ecead08f8215673dfb.jpg" }, { "user_id": 417302, "full_name": "Alice Deng", "title": "Founder", "founder_bio": "Co-founder & CPO of Slope. Previously, Alice co-founded Glisten AI (YC W20), providing e-commerce product categorization using CV and NLP. Prior to Glisten, she was a product manager at Dropbox. Alice is a UC Berkeley grad, where she led Blueprint, a tech for impact organization and was named under the Forbes 30 Under 30 list in 2023.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alicedeng/", "x_url": "https://twitter.com/alicevdeng", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f3f47ed71ba8d938a54dc0f3c7b6c816cef4d1d3.jpg" } ], "jobs": [ { "id": 111261, "title": "Senior Backend Engineer", "url": "https://www.ycombinator.com/companies/slope/jobs/O4fxXXJ-senior-backend-engineer", "location": "San Francisco / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$200K - $250K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 115247, "title": "Senior Accountant / Accounting Manager", "url": "https://www.ycombinator.com/companies/slope/jobs/K4yJj4A-senior-accountant-accounting-manager", "location": "San Francisco / Remote (US)", "type": "Full-time", "role": "Finance", "role_type": null, "salary_range": "$170K - $190K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "Slope Announces New Round of Funding from J.P. Morgan | by Lawrence Lin Murata | Slope Stories | Jul, 2024 | Medium", "url": "https://medium.com/slope-stories/slope-announces-new-round-of-funding-from-j-p-morgan-070b204f2ac3", "date": "Jul 17, 2024" }, { "title": "Payments Startup Slope Raises $30 Million From Sam Altman And Others To Ramp Up AI Tools", "url": "https://www.forbes.com/sites/alexyork/2023/09/27/payments-startup-slope-raises-30-million-from-sam-altman-and-others-to-ramp-up-ai-tools/", "date": "Sep 27, 2023" }, { "title": "Inside Silicon Valley Bank's bet on buy now/pay later for businesses | PaymentsSource | American Banker", "url": "https://www.americanbanker.com/payments/news/inside-silicon-valley-banks-bet-on-buy-now-pay-later-for-businesses", "date": "Sep 30, 2022" }, { "title": "Slope brings on new CFO, customers, capital as it rises to offer businesses buy now, pay later – TechCrunch", "url": "https://techcrunch.com/2022/04/15/slope-series-a-buy-now-pay-later-b2b-payments/", "date": "Apr 15, 2022" }, { "title": "Slope takes in first capital with visions of being ‘Stripe for global B2B payments’ – TechCrunch", "url": "https://techcrunch.com/2021/11/22/slope-stripe-for-global-b2b-payments/", "date": "Nov 22, 2021" } ], "launches": [] }, { "id": 26749, "name": "Ballerine", "slug": "ballerine", "former_names": [ "Whis" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/40c2e4d4f16e7f43d9fe9f060296b72b7d16de22.png", "website": "http://www.ballerine.com", "all_locations": "Tel Aviv-Yafo, Tel Aviv District, Israel", "long_description": "Ballerine is an AI-native risk intelligence platform built for financial institutions, acquirers, and payment providers that need to underwrite and monitor merchants at scale in real time.\r\n\r\nInstead of relying on fragmented point solutions, Ballerine unifies data from business registries, transaction patterns, sanctions lists, and web intelligence into one composable infrastructure. \r\n\r\nIts AI Agents continuously analyze these signals to deliver dynamic and explainable risk assessments across the merchant lifecycle.\r\nWith a modular architecture, teams can automate underwriting, detect emerging risks, and monitor merchant behavior while aligning to their own policies and compliance frameworks.\r\n\r\nUnlike traditional systems with static models, Ballerine’s AI Agents learn and adapt from live data, uncovering changes in merchant behavior that indicate fraud, laundering, or non-compliance.\r\n\r\nAlready deployed across multiple regions, Ballerine has enabled acquirers and fintechs to accelerate underwriting decisions, reduce manual reviews, and cut false positives, redefining how modern institutions manage merchant risk.\r\n", "one_liner": "Agentic Intelligence Layer for Merchant Risk Decisions", "team_size": 16, "industry": "Fintech", "subindustry": "Fintech", "launched_at": 1659913917, "tags": [ "Compliance", "Fraud Detection" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "Fintech" ], "regions": [ "Israel", "Middle East and North Africa" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/ballerine", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/ballerine.json", "year_founded": 2022, "city": "Tel Aviv-Yafo", "country": "IL", "linkedin_url": "https://www.linkedin.com/company/ballerine-inc", "x_url": "https://twitter.com/Ballerine_io", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/40c2e4d4f16e7f43d9fe9f060296b72b7d16de22.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Nicolas Dessaigne", "url": "https://www.ycombinator.com/people/nicolas-dessaigne" }, "founders": [ { "user_id": 401171, "full_name": "Noam Izhaki", "title": "Founder", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/noamizhaki/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c579c0b3b08b74017abe8932ade3b8edb58f4159.jpg" }, { "user_id": 996688, "full_name": "Nitzan Gelbard", "title": "Founder", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nitzan-guy-63ba9142/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/382b0f2b170e6614de85751f93dd6d2403553057.jpg" }, { "user_id": 813430, "full_name": "Alon Peretz", "title": "Founder", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alonp", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8da0d4f745c4bfc59a3cd15c37ecb6059add34e2.jpg" } ], "jobs": [], "news": [ { "title": "Ballerine brings open source to banks' risk and identity decision-making | TechCrunch", "url": "https://techcrunch.com/2023/05/23/ballerine-brings-open-source-to-banks-risk-and-identity-decision-making/", "date": "May 23, 2023" } ], "launches": [ { "id": 74539, "title": "Ballerine: Open-source & global KYB platform, now with LLM-based OCR!", "tagline": "LLM plugin with OCR capability that can scan various document types (e.g. bank statements, utility bills, etc.) in different formats and languages ", "body": "So, it’s not new that Ballerine is a global platform for KYB (Know Your Business), built on top of open source core. But — we’ve just released a new LLM OCR plugin 🚀\n\nOne big challenge of KYB is managing documents in different formats and languages. We’ve built a new LLM plugin with OCR capability that can scan various document types (e.g. bank statements, utility bills, etc.) in different formats and languages and map the information to the appropriate fields, which allows you to apply rules on top of it. \\\n\\\n[Watch the demo](https://www.youtube.com/watch?v=YbCNExY4wzY) and let us know if you’d like access!\n\n[Founders@ballerine.com](mailto:Founders@ballerine.com)", "url": "https://www.ycombinator.com/launches/JOF-ballerine-open-source-global-kyb-platform-now-with-llm-based-ocr", "votes": 8, "created_at": "2023-08-31T16:30:57.749Z" }, { "id": 65187, "title": "Ballerine: Open & self-served user risk decisioning platform", "tagline": "Automate your decisions for onboarding, transaction monitoring, credit underwriting, and more, without boundaries.", "body": "Ballerine is the first Open Source Infrastructure for Identity and Risk management.\n\n**TL;DR: Ballerine is an Open Source Infrastructure for Identity and Risk management.**\n\n**Use Ballerine to get:**\n\n* **KYC/KYB UI flows**\n* **A case management dashboard for manual user approval**\n* **Integrations with identity verification providers**\n* **A rule engine to automate your decisioning process.**\n\n**All are open for you to customize and extend to your needs.**\n\nCompanies offering financial services need to identify their customers and assess their risk to avoid fraud and money laundering.\n\nEach company has different audience types, services offered, geographies in which they operate, changing regulations, risk appetite, and more\n\nThis means every company needs to take different risk measures, and wants to provide different experiences to their customers.\n\n### Problem\n\nThe vendors in the market today offer solutions that are:\n\n1. **Closed** - The solutions in the market come with minimal customizability, you can’t change things to fit your changing needs, making it not future-proof.\n2. **Single point solutions** - to cover the whole range of a company’s specific needs it would need to put in extra work instead of focusing on its product.\n3. **Not white label** - The user experience and trust level of a company’s user is hurt by going through a flow that doesn’t look like the rest of the product.\n\n### Solution\n\nBallerine helps you build your KYC/KYB and risk workflows using open-source building blocks for user identity and risk management.\n\nAllowing any business to compose solutions for any vertical and geographical-specific use case.\n\n**Why Open Source?**\n\n* Future proof - modular and extendable building blocks\n* Global - Multiple vendors are accessible in one UI\n* White label - Customizable UX and UI\n* Strong and secure - Tested and maintained by the community\n\nAll of those provide a flexible, long-term solution that won't fail you along the way.\n\n**What is included**\n\n* KYC/KYB UI flows\n* A case management dashboard for manual user approval\n* Integrations with identity verification providers\n* A rule engine to automate your decisioning process\n\n**Examples**\n\nCompanies who already used Ballerine were able to create various solutions like:\n\n* Low-cost KYC solution made out of Identity verification building blocks\n* Multi-vendor KYC flow for users from multiple geographies\n* KYB document collection flow with UI and case management dashboard\n* Vendor-agnostic UI for a company where multiple bank partners that are approving their users\n* And more.\n\n### Build with Ballerine\n\nIn the upcoming weeks, we’ll be gradually releasing our code to our public repository for you to be able to start building with.\n\nLearn more about the project here: \n\nYou can already sign yourself up to get early access [here](https://ballerine.io/request-access)\n\nFeel free to reach out to us at [noam@ballerine.io](mailto:noam@ballerine.io).\n\nWe would love to talk to you and get your feedback regarding what we’re building.\n\nThank you,\n\nNoam, Alon, and Nitzan from Ballerine.", "url": "https://www.ycombinator.com/launches/GxP-ballerine-open-self-served-user-risk-decisioning-platform", "votes": 27, "created_at": "2022-08-07T22:37:45.445Z" } ] }, { "id": 26776, "name": "Overwatch", "slug": "overwatch", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/1def1e618878c538c08d4635fb23a133ed14d476.png", "website": "http://www.overwatchdata.ai", "all_locations": "New York City, NY, USA", "long_description": "Overwatch Data offers real-time AI agents to scale fraud, cyber, and security teams. Our agents automate the threat intelligence and brand protection workflow, while also conducting risk reviews at scale to detect fraud and financial crime.", "one_liner": "Enterprise software for enterprise risk and cyber fraud intelligence", "team_size": 14, "industry": "B2B", "subindustry": "B2B -> Security", "launched_at": 1657829137, "tags": [ "Cybersecurity", "Fraud Prevention", "Fraud Detection", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "B2B", "Security" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/overwatch", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/overwatch.json", "year_founded": 2022, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/overwatchdata/", "x_url": "https://twitter.com/Overwatch_data", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/1def1e618878c538c08d4635fb23a133ed14d476.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 977105, "full_name": "Arjun Bisen", "title": "Founder", "founder_bio": "I started my career as an Australian diplomat, during which time I analyzed geopolitical and cyber risk across APAC and writing Australia's Cyber Engagement Strategy. I then studied the intersection of technology and risk at Harvard on a full Fulbright scholarship. I've since led risk teams at Stripe and Google Search before founding Overwatch to help those teams get ahead of emerging cyber, fraud, and financial crime risks. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/arjun-bisen/", "x_url": "https://twitter.com/ArjunBisen1", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/35718f1a274cd5ca0063277cf94d8ea427b33a52.jpg" }, { "user_id": 1081618, "full_name": "Zara Perumal", "title": "Founder", "founder_bio": "Co-founder / CTO at Overwatch Data\n\nZara studied Artificial Intelligence and Cybersecurity at MIT LIDS/CSAIL focusing on active learning for malware detection and risk-limiting post-election audits. After graduating, Zara worked on Google Assistant then at Google’s Threat Analysis Group, where she built machine learning and software systems to use threat intelligence and malware analysis to better protect against targeted threats and disinformation by state sponsored actors.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/zperumal/", "x_url": "https://twitter.com/zaraperumal", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4cf5bf21b04591b4d94995d4372e5b115c12e836.jpg" } ], "jobs": [], "news": [ { "title": "What launching a startup taught me about foreign policy | The Hill", "url": "https://thehill.com/opinion/international/3757491-what-launching-a-startup-taught-me-about-foreign-policy/", "date": "Dec 01, 2022" } ], "launches": [ { "id": 64636, "title": "🌍 ⚠️ Overwatch - Automating global risk detection and analysis", "tagline": "Monitor data streams, including news and satellite imagery, for narratives and events that will impact operations, supply chain, and reputation", "body": "Hey YC! Tad, Zara and I are excited to launch [Overwatch 🦉](http://www.overwatchdata.io)​ - to help automate global risk detection and analysis. Our tool allows enterprises to monitor data streams, including news and satellite imagery, for narratives and events that will impact their operations, supply chain, and reputation. Our tool can be used for live monitoring OR historic due-diligence on a location.\n\n**😠The Problem**\n\n* Businesses have many points of failure in their global operations and struggle to monitor all the locations and narratives that matter to them \n* Current event detection tools are often noisy and don't provide any context on why that event might be significant to a specific business.\n\n \n\n**🔨Our Solution**\n\nWe cut through the noise.\n\n* We help companies filter more accurately for events and narratives that matter to them.\n* We provide visualizations of how those events or narratives have changed over time and layer these events on top of other datasets, e.g. satellite imagery, shipping routes, trade data, commodity production sites, flood and fire maps, and other risk indices. \n* This helps draw connections and provide a holistic picture of how that event could impact their business.\n\n![](https://lh3.googleusercontent.com/OdI55U0rZf6ZSicyHS8BtsJ-xIY54kVTVw4wAojwTV6qvKYY-ZIjOc3Ae8hyKLVyYCNhE7YhFupIv-x9poZABJ-d7F8Mq3h1cc_E4C-l0M5k4c0OZRZOloKzWBee6FSH0qjsF7NXv7qJZphCiWONUs8)\n\n![](https://lh5.googleusercontent.com/dWmnLh1Pj0BJC39LrVN9R-j2u0hdrEbmj_qk_i5WcD0p9W0GGvU8a2sEWnwPjRTr_JaxqvavyJnCQtpkRgomQ018TbDsqPnT1Tlfe2J5ZgUDvgER0iIyrkai3_4FCHXU0IDwXOSWpOu7JQj-g05g2NU)\n\n**The Ask❓**\n\n* Do you (or anyone you know) struggle to detect, analyze, or predict events and narratives that could disrupt your business operations, brand, or supply chain? Then please reach out to ([founders@overwatchdata.io](mailto:founders@overwatchdata.io)) or [book a call](https://calendly.com/arjun-overwatch/30min) and we’d love to help.\n* Are you a data provider with interesting risk-related data? We’d love to discuss how we might incorporate it into our product.", "url": "https://www.ycombinator.com/launches/GoW-overwatch-automating-global-risk-detection-and-analysis", "votes": 18, "created_at": "2022-07-14T20:09:34.423Z" } ] }, { "id": 27952, "name": "Finic", "slug": "finic", "former_names": [ "Triton", "Buff", "Sidekick", "Psychic", "Dealwise" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/06180819ad31c1d2ee1b4b62d506fd5e7655aec4.png", "website": "https://www.finic.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Finic helps teams stop fraud, abuse, and financial crimes with system on models that make accurate decisions in fractions of a second, and system two agents that automate investigations end-to-end.", "one_liner": "Frontier AI to stop fraud, abuse, and financial crimes.", "team_size": 4, "industry": "Fintech", "subindustry": "Fintech", "launched_at": 1677272534, "tags": [ "Compliance", "Enterprise", "Automation", "Fraud Detection", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Fintech" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/finic", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/finic.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/finic-ai/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/06180819ad31c1d2ee1b4b62d506fd5e7655aec4.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/1c9eadc7f39d66903aedf0861616956033002285.png" ], "primary_partner": null, "founders": [ { "user_id": 139135, "full_name": "Jason Fan", "title": "Founder", "founder_bio": "Ex security & product @ Robinhood. University of Toronto Computer Science.", "is_active": true, "linkedin_url": "https://ca.linkedin.com/in/jason-fan-b59767104", "x_url": "https://twitter.com/jfan001", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/970d5fd882ee5ceec1544145866e19f878696341.jpg" }, { "user_id": 487696, "full_name": "Ayan Bandyopadhyay", "title": "Founder", "founder_bio": "Founder @ Finic (YC W23). Caltech class of 2020, former software engineer at Robinhood.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ayan-bandyopadhyay-9a45b1103/", "x_url": "https://twitter.com/ayanicband", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/84f47331f39f9e66682b24a2dc6262c3ce44ca15.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 120575, "title": "Grayson: A universal decision model for fraud, risk, and financial crimes", "tagline": "Real-time risk decisions at 1/100th the cost of Sonnet 5.5", "body": "Hey everyone 👋 I'm Jason, CEO of [Finic](https://finic.ai). Today we're launching **Grayson**, the first universal decision model trained to catch fraud, abuse, and financial crimes in real time.\n\n**TL;DR:** Send Grayson any context (logins, transactions, case notes, call transcripts) and a set of questions. Get back a calibrated probability for every answer in under 150ms. On fraud and risk benchmarks it's as accurate as Claude Sonnet 5.5 at 1/100th the cost, and it beats Jev. Zero data retention by default. [Request API access](https://finic.ai) and get $25 in free credit.\n\n![uploaded image](/media/?type=post&id=120575&key=user_uploads/139135/042eac49-1269-4f2c-b51e-d7f1476d31da)\n\n### ❌ The problem\n\nI joined Robinhood pre-IPO and built our fraud investigations platform, along with some of our most critical authentication controls. I saw the same problem over and over: many risk decisions need human-level judgment, but they come up so often that paying a human (or an AI agent) to make them is financially unworkable.\n\n* Should this new device require step-up authentication?\n* Should we allow this transaction, or block it?\n* Should this ticket go to fraud, or to customer support?\n* Does this call log show signs the customer is being scammed?\n* Is this fraud?\n\nToday, teams answer these with some mix of:\n\n* **Rules** that are brittle and get gamed the week after they ship\n* **Custom ML models** that take months, need labeled data, and have to be retrained every time a question or data source changes\n* **Frontier LLMs** that take seconds, cost dollars per thousand calls, return prose you have to parse, and are confidently wrong\n* **Humans or agents** who can do it well, but not on every login, transaction, and ticket\n\n### ✅ The solution: Grayson\n\nGrayson is a **decision model**: you can't chat with it. You give it context and typed questions, and it returns only probabilities, so a rules engine, router, or review queue can use them directly.\n\n```\n// Request (abridged)\n{\n \"context\": { \"known_devices\": [...], \"events\": [...] },\n \"questions\": [\n { \"name\": \"is_ato\", \"type\": \"noul\", \"instructions\": \"Are there signs of an ATO?\" },\n { \"name\": \"top_device_family\", \"type\": \"choice\",\n \"instructions\": \"Which device family do they use the most?\",\n \"options\": [\"apple\", \"android\", \"other\"] }\n ]\n}\n\n// Response\n{\n \"is_ato\": { \"value\": true, \"probability\": 0.999 },\n \"top_device_family\": { \"value\": \"apple\",\n \"probabilities\": { \"apple\": 0.999, \"android\": 0.001, \"other\": 0.000 } }\n}\n```\n\n![uploaded image](/media/?type=post&id=120575&key=user_uploads/139135/7b6becc9-a9de-4478-a6ac-a7920a35f2cd)\n\n* **Universal:** any context, structured or unstructured, and any question: yes/no, multiple choice, or a score. No retraining when your data or processes change.\n* **Calibrated:** when Grayson says 0.8, it's right about 8 times in 10. So you can set thresholds, auto-approve the clear calls, send the close ones to review, and move the threshold later without retraining.\n* **Fast and cheap:** under 150ms of model time for typical requests, at $0.035 per million input tokens (about $0.11 per 1,000 requests on our benchmarks).\n* **Private:** zero data retention by default. We never store the context, questions, or answers you send.\n\nGrayson is trained with Reinforcement Learning for Calibrated Decisions (RLCD). Credit to the TypeSafe team behind Jev for pioneering RLCD and the \"System One\" model. Grayson takes that idea and specializes it for fraud, risk, and trust & safety.\n\n### 📊 Benchmarks\n\nWe ran Grayson against Jev, Claude Opus 5.5, Claude Sonnet 5.5, GPT-6.1 Sol, Kimi K3, and Laya on six fraud and risk datasets: four public benchmarks (LendingClub, customs fraud, fake job postings, scam/phishing email) and two of our own held-out evaluation sets.\n\n![uploaded image](/media/?type=post&id=120575&key=user_uploads/139135/4c4a9563-db5f-4ce0-a307-9ad52568bb5c)\n\n![uploaded image](/media/?type=post&id=120575&key=user_uploads/139135/9d4f3e5c-a61d-4f68-907c-d69f89342eab)\n\nGrayson has the best log loss and the highest % right of any model, which matters most for a model whose job is to output probabilities you can threshold on. On the \"is it fraud?\" questions with known outcomes, it has the best log loss of every model tested:\n\n![uploaded image](/media/?type=post&id=120575&key=user_uploads/139135/470e932a-f3d2-4b02-80c2-f16c77137a3a)\n\nAnd on general business decisions (JevBench and EIKOS), Grayson is level with Jev (0.971 vs. 0.974 AUC-PR), while frontier models score 0.988–0.998 at 100–500x the cost.\n\nFull methodology, datasets, and charts are in the [announcement post](https://finic.ai/blog/introducing-grayson).\n\n### 🙏 The ask\n\n* **If you run fraud, risk, or trust & safety** at a fintech, bank, lender, or marketplace: [request API access](https://finic.ai). Every account gets $25 in free credit. We're in early beta (GPUs are still hard to get!) and are rolling out access gradually.\n* **Intros:** we'd love to meet heads of fraud, risk, and T&S, and the engineers who own rules engines and decisioning platforms.\n* Questions or ideas for what Grayson should answer? Email me at [jason@finic.ai](mailto:jason@finic.ai).", "url": "https://www.ycombinator.com/launches/VMl-grayson-a-universal-decision-model-for-fraud-risk-and-financial-crimes", "votes": 2, "created_at": "2026-10-07T20:23:18.171Z" }, { "id": 85671, "title": "Finic 🕵️‍♂️ - AI that investigates financial fraud", "tagline": "Helping fintechs increase fraud ops capacity, improve accuracy, and resolve cases 80x faster.", "body": "We’re Jason and Ayan, the founders of Finic. We help fintechs automate fraud investigations with AI agents.\n\n![uploaded image](/media/?type=post&id=85671&key=user_uploads/139135/4a8b26cc-6258-4e8a-93c7-1779e1768748)\n\n## tl;dr\n\nFinic lets fraud ops teams at financial institutions scale up their capacity by 10x without increasing headcount. We use intelligent automation to analyze structured data (SQL queries, risk scores) and unstructured data (customer support tickets, adverse media), allowing us to triage tickets and generate case notes twenty times faster and at 80% lower cost compared to tier 1 fraud analysts.\n\n---\n\n—\n\n---\n\nAt Robinhood, we led the identity team. One of our key mandates was to prevent, detect, and investigate fraud, with a specific focus on identity fraud. We were able to reduce account takeover (ATO) fraud losses by 95%, but it took us 10 months and a lot of new hires to get there, largely due to **bottlenecks in the human review process.** We tried automating fraud SOPs, but weren’t able to arrive a compelling solution because at the time there wasn’t a way to automate the biggest bottle neck to fraud reviews - subjective reasoning.\n\nNow, we’re building Finic to be the product we wish we had, with AI that now makes this possible.\n\n## **Why is this important?**\n\nConsumer financial fraud losses crossed $10bn for the first time in 2023, and is likely to keep growing as new technology also enables new attack vectors, most recently with synthetic identities created by GenAI.\n\nThe way financial institutions prevent fraud is usually split into three parts:\n\n* **Prevention:** Find and closing loopholes that enable fraud in the first place.\n* **Detection:** using ML or heuristic models (aka “rules engine”) to detect unusual activity.\n* **Response:** Investigating cases of potential fraud, deciding on a course of action, and, in some cases, writing reports detailing the investigation.\n\nPrevention is typically achieved through product improvements, and the market for data products that help with fraud detection is mature. However, **the way that companies respond to fraud is still mostly done by human review** by a combination of highly skilled onshore and low-skilled offshore analysts.\n\n## **Problem**\n\nUsing humans as the front line of fraud investigations is not ideal for several reasons:\n\n* **Humans make mistakes.** For CS tickets, mistakes mean frustrated customers. For fraud cases, mistakes can result in thousands or millions in losses and compliance headaches that last years.\n* **Humans take a long time to train.** Fraudsters change tactics frequently. By the time a team of analysts is fully trained on a new fraud typology, fraudsters have already moved on to other strategies.\n* **Humans are expensive to scale.** The average offshore agent costs $43,000 per year and has a throughput of about 250 cases per month. This means it costs companies $14 per case to investigate fraud, and that’s not even factoring in the period it takes to onboard and train new agents, which is a constant problem since turnover is so high.\n\nFinancial institutions aggressively overhire when fraud is bad, yet still find themselves unable to respond quickly to new fraud typologies, leading to **millions in preventable losses per quarter.** As a result, every fraud ops team I’ve worked with is either understaffed and overworked, or overstaffed and just waiting to be laid off.\n\n## **Solution**\n\nFinic allows fraud ops teams to build custom AI agents that execute SOPs autonomously, improving on the work of junior fraud analysts and allowing the team to scale capacity by 100x with the click of a button.\n\n![uploaded image](/media/?type=post&id=85671&key=user_uploads/139135/1211dd85-7c36-4f73-b1f8-5ab59a333550)\n\nThis gives fraud ops teams several superpowers:\n\n* **Accuracy and explainability:** Finic agents document each step of their workflow, making it easy for senior analysts to audit, which means fewer mistakes. Case notes written by human analysts take much longer and are often inconsistent.\n* **React faster to new fraud vectors:** Finic agents can be created, trained, and scaled to 1000s of cases per day in a matter of hours, something that would be impossible for a team of human analysts.\n* **Scale capacity instantly:** Fraud ops can now scale capacity as easily as engineering can scale web services. Finic agents complete investigations in 1-5 minutes, compared to 1-2 hours for human analysts.\n\nThe Finic platform has three parts, all of which are built on top of a multimodal reasoning engine.\n\n### **SOPs**\n\nEach Finic agent is defined with a SOP, or list of actions they need to take in order to investigate a ticket. This can include data retrieval steps to build a profile of the customer’s activity, analysis steps to reason about whether fraud is taking place (and if so, what kind), as well as actions the agent can take once a decision is made, like escalating tickets for human review.\n\nCustomers can create an SOP on Finic from scratch or import an existing document to **automatically generate each action based on existing SOPs.**\n\n![uploaded image](/media/?type=post&id=85671&key=user_uploads/139135/d3036616-7071-4c1a-a95d-9777fe9b412c)\n\n### **Tools**\n\nFraud analysts need many tools to conduct an investigation, from internal dashboards to SQL tables to vendor data.\n\nFinic uses intelligent automation to connect to any data source and use it the same way a human analyst would: through a browser.\n\nRather than waiting for the engineering team to build new APIs, all it takes to get started with Finic is a list of credentials for the tools they use, drastically **reducing the time it takes to get to ROI.**\n\n### **Audit Logs**\n\nWhether the investigator is an AI agent or a human analyst, keeping a trail of why decisions were made is just as important as making the right decision. In fact, several regulations mandate that financial institutions keep logs of not just transactions but also the reasons why transactions or accounts were flagged as fraudulent.\n\nFinic agents “think out loud” and document each step in the SOP, including what data sources they used, how they arrived at their conclusions, and what steps they took afterward. This makes it easy for senior analysts to review agent activity to ensure compliance and is usually more comprehensive than the reports generated by junior analysts.\n\n## Learn more\n\nHere’s a list of some of the types of fraud we’re helping customers remediate:\n\n* Identity Fraud: Reviewing KYC tickets to determine if new signups are using stolen/synthetic identities, or if the account is being used as a proxy by a malicious third party.\n* Friendly Fraud: Reviewing account activity and dispute packages from third parties to determine if customer-reported issues like credit card chargebacks are legitimate.\n* Account Takeovers (ATO): Reviewing accounts flagged with unusual login activity, and looking at device fingerprint, IP address, transaction activity and other signals to determine if the account has been compromised.\n\nIf you’re a fintech looking for ways to 10x the capacity of your fraud ops team without increasing headcount, **email me at **[**jason@finic.ai,**](mailto:jason@finic.ai)** and I’d be happy to show you a demo.**", "url": "https://www.ycombinator.com/launches/MHn-finic-ai-that-investigates-financial-fraud", "votes": 22, "created_at": "2024-11-18T20:31:43.972Z" }, { "id": 77023, "title": "🌱 Dealwise: an AI powered investment bank for startups", "tagline": "Get acquisition offers in just 3 months. No exclusivity, cancel anytime", "body": "We’re Jason and Ayan from the W23 batch of YC, and we’re building **Dealwise, an AI powered investment bank for startups**\n\n### **TL;DR:**\n\nDealwise helps founders sell their startup. We use AI to automate the back office work associated with dealmaking. This let us serve startups too small for traditional investment banks, while running an accelerated process that gets founders offers in half the time.\n\nContact me at [jason@godealwise.com](mailto:jason@godealwise.com) or visit [godealwise.com](http://godealwise.com) to learn more!\n\n### Background\n\nBefore being founders, Ayan and I were each other’s PMs and software engineers at Robinhood. Around August 2023, we noticed a lot of interest in M&A activity from our founder friends, including one batchmate who we advised informally in the sale of his startup. We started learning about what it takes to get deals done, and that’s when we realized there was a massive gap in advisory services for founders who want to step off the venture path (or were never on it in the first place) that existing service providers don’t fill.\n\n![uploaded image](/media/?type=post&id=77023&key=user_uploads/139135/6ef79a46-b348-4364-b427-e798acfd74d7)\n\n### **The problem**\n\nSelling a company is hard. In fact, **over 90% of deals fall apart before the transaction closes!** Acquisitions are like babies - each one is a miracle, but somehow they still happen all the time. This difficulty is why the investment banking industry rakes in over $300bn per year. \n\nGiven the high failure rates, founders must run an efficient process. They can’t afford to waste months or even years on a deal that never closes.\n\nUnfortunately, if you’re the founder of a startup worth less than $50M, no reputable investment bank is going to work with you, as they won’t generate enough in fees for it to be worth their time.\n\nThis leaves founders with just a few options that, frankly, all suck.\n\n**Traditional M&A Advisors**\n\nThere are many M&A advisors (sometimes called business brokers) in the lower end of the market that work with small businesses. However, selling a small company is just as much work as selling a much bigger one, so successful advisors tend to move upmarket. That means most advisors working with startups are either bad at their job, or very new and inexperienced. \n\nDue to the high failure rates in M&A, advisors sign as many clients as possible and lock them in to exclusivity agreements that ensure they get paid a fee if any client sells in the next 2 years, regardless of whether the advisor was involved. This is not an extreme case, **these predatory agreements are the norm** for M&A advisory firms.\n\nPlus, traditional M&A advisors almost always come from a business background - few understand the software industry or have networks that are better than the founders they work with.\n\n**Marketplaces**\n\nMarketplaces like Flippa or Acquire.com are great for four, five and six figure transactions paid for in cash by individuals, but serious corporate and institutional buyers avoid these platforms.\n\nFor transactions that are seven figures and up, buyers usually need some combination of equity and/or debt financing, which means the bar is much higher for diligence. It also means founders expect a lot more support through the process than what they get listing on a public marketplace.\n\n**DIY**\n\nGiven the alternatives, it’s no wonder most founders with between $1mm and $5mm revenue opt to DIY their acquisition process.\n\nUnfortunately we see many founders who’ve gotten stuck in a viscous cycle of: spend time chasing buyers → get distracted → growth declines → selling gets harder → spend even more time chasing buyers → growth declines more → ultimately shut down.\n\nThe median founder sells 0 companies in their lifetime, so it makes no sense to become a M&A expert when you have a business to run.\n\n### **The solution**\n\nWe started Dealwise in December 2023 to help startup founders get acquired. We use AI to automate the back office work, which means we get healthy margins on transactions that are too small for investment banks. More importantly, it allows us to **run a process in just 3 months** compared to the typical 6-12 months process for M&A advisors and investment banks. As a founder, it means you can get feedback sooner instead of wasting a year with an M&A advisor who’s stringing you along but not doing much work for you behind the scenes.\n\nWe’re new, but our track record is phenomenal. Traditional M&A advisors typically take a minimum of 6 months to close a deal, and 12 months or more if it’s their first deal. We went from launch → 7 figure deal closed in just 3 months, with several more under offer. Our record for clients was just 4 weeks from the start of our engagement to first offer submitted.\n\nAs we scale, we’ll apply our experience building LLM tooling at our previous startup to continue automating tasks like buyer prospecting, thesis generation, NDA signing, and data room creation so we can focus our time on supporting founders through their acquisition journey.\n\n### **Why is this important?**\n\nPatrick and John Collison sold a little known startup called Auctomatic in 2008 before founding Stripe. Sam Altman’s first company, Loopt, raised $30M from investors only to sell for a “modest” $40M. Before Uber, Travis Kalanick spent six years trying to make a startup called Red Swoosh work, at one point moving back in with his parents, before finally selling it in 2007. And Socialnet was considered a failure by its founder, Reid Hoffman, after being acquired for a “modest sum” in 2002.\n\nFor startups that struggle to hit escape velocity, sometimes the best option is to sell it so the founders can move on to greener pastures.\n\n![uploaded image](/media/?type=post&id=77023&key=user_uploads/139135/a9f2b058-74b2-404f-a8f0-eb8921afbc1a)\n\nBut selling a startup is a difficult, and often lonely process. We want to be there for founders when their VCs have stopped replying.\n\n## **Our ask**\n\nIf you know a founder who are considering an exit, email me at [jason@godealwise.com](https://www.gsb.stanford.edu/experience/about/centers-institutes/ces/research/search-funds). We keep conversations 100% confidential and I’ll be more than happy to chat even if all they’re looking for is some advice and insights on the market.\n\nThe founders who will best fit for Dealwise have a few things in common:\n\n* They sell software, or software-enabled services\n* They have at least $1mm in ARR\n* They are profitable or have a clear path to profitability\n* They are motivated - if they had to choose between an exit and raising a round, they’d choose to exit\n* They already have buyer interest and are looking to run a more competitive process\n\nLearn more on [our website](https://godealwise.com/), or reach me at [jason@godealwise.com](https://www.gsb.stanford.edu/experience/about/centers-institutes/ces/research/search-funds) if you want to chat about acquiring or exiting a startup.", "url": "https://www.ycombinator.com/launches/K2J-dealwise-an-ai-powered-investment-bank-for-startups", "votes": 24, "created_at": "2023-12-19T17:04:54.030Z" } ] }, { "id": 28077, "name": "Corgi Labs", "slug": "corgi-labs", "former_names": [ "CorgiAI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/48ab8ae360dddc3659ad06077db5d3349b75b91f.png", "website": "https://www.corgilabs.ai", "all_locations": "Singapore, Singapore", "long_description": "End-to-end suite of fraud detection and prevention solutions, including an analytics product to monitor and understand dispute + fraud metrics, an AI product which highlights transactions for merchants to follow up on, and an AI solution to recommend rules that can be implemented in payment provider platforms (Stripe, Shopify, Adyen) to proactively reduce dispute + fraud rates.", "one_liner": "AI to increase payment acceptance and reduce fraud for businesses", "team_size": 3, "industry": "Fintech", "subindustry": "Fintech -> Payments", "launched_at": 1677769779, "tags": [ "Payments", "Analytics", "E-commerce", "Fraud Detection", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Fintech", "Payments" ], "regions": [ "Singapore", "Southeast Asia", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": true, "app_answers": [ { "question": "What is your company going to make? Please describe your product and what it does or will do.", "answer": "End-to-end suite of fraud detection and prevention solutions, including an analytics product to monitor and understand dispute + fraud metrics, an AI product which highlights transactions for merchants to follow up on, and an AI solution to recommend rules that can be implemented in payment provider platforms (Stripe, Shopify, Adyen) to proactively reduce dispute + fraud rates." } ], "question_answers": null, "url": "https://www.ycombinator.com/companies/corgi-labs", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/corgi-labs.json", "year_founded": 2022, "city": "Singapore", "country": "SG", "linkedin_url": "https://www.linkedin.com/company/corgiai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/48ab8ae360dddc3659ad06077db5d3349b75b91f.png", "app_video_url": null, "dday_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/video_event/60/1680797717037-b3zrie54je5-a536a7c3970623624f5cc8afa2567756/235544e43fbad7d99d2453d70ba407c8.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQC4NIECABPQ2AHNW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022712Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=c25cdb58e907eb0a6723b9f38d62f5d1afe79894866f3512bb2e2402c32fa60b", "company_photos": [], "primary_partner": { "name": "Gustaf Alstromer", "url": "https://www.ycombinator.com/people/gustaf-alstromer" }, "founders": [ { "user_id": 814645, "full_name": "Saif Farooqui", "title": "Founder", "founder_bio": "Saif is the founder of Corgi Labs, where he builds modern fraud and payments intelligence for fast-growing businesses. Previously, Saif was a Data Scientist at Stripe, Google and Facebook (now Meta), and a healthcare economist in an earlier life.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/sfarooqui1/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f2ad4d600f7b49b35fd5a55c920cd563d9bc8281.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 29533, "name": "Indemni", "slug": "indemni", "former_names": [ "Alacrity" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/5e66c11bb21c9a5ccdcf4a475ed7e48693b41f2b.png", "website": "http://www.indemni.com", "all_locations": "San Francisco, CA, USA", "long_description": "We are building a safer supply chain. Cargo Theft has been increasing yearly, and the supply chain industry is urgently looking for ways to solve this. Using experience fighting fraud at the largest delivery marketplaces and logistics tech companies, we are building solutions to stop Cargo Theft and improve profit margins and revenue for warehouses and freight brokers.", "one_liner": "Cargo Theft and Fraud Prevention Platform", "team_size": 7, "industry": "B2B", "subindustry": "B2B -> Supply Chain and Logistics", "launched_at": 1708029636, "tags": [ "Identity", "Logistics", "Supply Chain", "Fraud Prevention", "Fraud Detection" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2024", "status": "Active", "industries": [ "B2B", "Supply Chain and Logistics" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/indemni", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/indemni.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/100487698/admin", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/5e66c11bb21c9a5ccdcf4a475ed7e48693b41f2b.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Pete Koomen", "url": "https://www.ycombinator.com/people/pete-koomen" }, "founders": [ { "user_id": 1781891, "full_name": "Omar Draz", "title": "Founder", "founder_bio": "Ex-DoorDash, worked on Fraud, Growth and Logistics. Currently building!", "is_active": true, "linkedin_url": "https://linkedin.com/in/odraz", "x_url": "https://twitter.com/oamdraz", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e414fe2b8469d656aa1c919c6fa00f1366ecccb3.jpg" } ], "jobs": [ { "id": 112115, "title": "Full Stack Engineer", "url": "https://www.ycombinator.com/companies/indemni/jobs/97Pl9a9-full-stack-engineer", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$100K - $200K", "equity_range": "0.05% - 0.50%", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [] }, { "id": 31132, "name": "MouseCat", "slug": "mousecat", "former_names": [ "Agnostic" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/71e74a47767755403f90f1778ab3ab410649c82e.png", "website": "https://www.mousecat.ai", "all_locations": "New York City, NY, USA", "long_description": "MouseCat enables companies to detect suspicious activity, automate investigations, and act before the money leaves.\r\n\r\nWe work with Fortune 500 companies and startups to deploy real-time models and AI agents that reduce fraud, compliance incidents, and friction for good customers. ", "one_liner": "AI to fight financial crime", "team_size": 6, "industry": "B2B", "subindustry": "B2B", "launched_at": 1769108383, "tags": [ "Compliance", "Fraud Detection" ], "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": null, "url": "https://www.ycombinator.com/companies/mousecat", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/mousecat.json", "year_founded": 2026, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/mousecat", "x_url": "https://x.com/MouseCatAI", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/mousecat", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/71e74a47767755403f90f1778ab3ab410649c82e.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/5ec2f1ad71792d8eacc2adeab69c2ad82fee3f9d.png" ], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 339225, "full_name": "Nicholas Aldridge", "title": "Founder/CEO", "founder_bio": "Nicholas Aldridge is the Co-Founder and CEO of MouseCat, and a core maintainer of MCP. He spent 6.5 years as a Principal Engineer at AWS AI, where he helped launch and lead Amazon Bedrock Knowledge Bases, Agents, and AgentCore. He also represented Amazon on the A2A steering committee. Nicholas has helped some of the largest and most security and privacy sensitive organizations in the world to plan and implement their AI strategies.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nicholas-aldridge-341162a4/", "x_url": "https://x.com/nickaldridge_", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/80b8bc031a06a83d6780bdce207e3a32ba77a9a3.jpg" }, { "user_id": 730542, "full_name": "Joseph McAllister", "title": "Founder/CTO", "founder_bio": "Joseph McAllister is the Co-Founder and CTO of MouseCat. Before MouseCat, Joseph spent 4 years building systems that power every risk decision at Coinbase. He focused on streaming pipelines, large-scale data processing workloads, and improving ACH and ATO risk models. While studying Computer Science at Cornell University, he founded Roo Storage, which was acquired in 2020 by Handled (now UniGroup).", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/joseph-mcallister/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4f259307f32410bc8099721c3843b0f5f26ec23e.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 98387, "title": "MouseCat 🐭🐱 AI-Powered Fraud Investigations", "tagline": "MouseCat does human-quality fraud investigation at scale", "body": "# **TL;DR**\n\n**MouseCat uses AI agents to do human-quality fraud investigation at scale**. MouseCat integrates with your proprietary data, learns from your feedback and past cases, and runs continuously, not just at transaction time.\n\n**Ask:** If you or someone you know is struggling with label quality or time-to-mitigation for new fraud trends, visit [mousecat.ai](http://mousecat.ai) to book a demo!\n\n\n\n# **❌ The Problem**\n\nMost businesses rely on a combination of rules, traditional ML models, and manual review to stop fraud. This creates three major pain points. These systems:\n\n1. Adapt slowly to new fraud trends\n2. Are designed for point-in-time review and not continuous analysis of risk\n3. Result in a high number of false positives when patterns change, leading to lost revenue\n\nAs AI makes it easier to commit fraud, these gaps are leading to an explosion in losses.\n\n# **🐭🐱 Our Solution**\n\nMouseCat is dedicated to building the best AI platform for fraud investigation.\n\nMouseCat's agents conduct fraud investigation like a human would. They review internal case information, search for additional details from databases, APIs, etc., refer to prior cases for context, and distill the most important evidence needed to make a decision. MouseCat uses its insights to identify fraud rings and propose mitigations with new backtested rules/features.\n\nWith MouseCat, companies get:\n\n1. **Continuous Improvement:** Agents that continuously improve based on past cases and analyst feedback.\n2. **Continuous Review:** Post-transaction analysis to spot and mitigate fraud trends faster.\n3. **Custom Integration and Data Ownership:** On-prem deployments that integrate with customers’ existing data sources and APIs.\n\nPlus, MouseCat handles production hurdles like evaluation, monitoring, distributed failures / retries, and more.\\\n\\\nImagine if your best analyst could investigate every transaction...\n\n![uploaded image](/media/?type=post&id=98387&key=user_uploads/339225/c8837552-a055-4144-825d-68178cefa4b5)\n\n# **🙏 Our Ask**\n\nIf you answer “yes” to any of these, we’d love your help:\n\n* Can you introduce us to folks on fraud teams at banks / fintechs / marketplaces?\n* Are you leading a fraud team? You can book a demo at: [mousecat.ai](http://mousecat.ai).\n\n👉 Contact us at [hello@mousecat.ai](https://hello@mousecat.ai)\n\nYour help can make a huge difference. **Thank you!**\n\n# 👥 **Our Team**\n\nNick and Joe have been friends for six years and co-founded MouseCat earlier this year. They bring deep expertise in both AI and fraud detection systems.\\\n\\\nNick Aldridge (CEO) is one of nine Core Maintainers of the Model Context Protocol (MCP), and was a Principal Engineer at Amazon where he led products like Bedrock Knowledge Bases and AgentCore. Nick earned his BA/MS in Mathematics and Computer Science from the University of Chicago.\\\n\\\nJoseph McAllister (CTO) worked on ML infrastructure and fraud detection models at Coinbase, and he sold his first startup while studying CS at Cornell University.\n\n![uploaded image](/media/?type=post&id=98387&key=user_uploads/339225/9b2c6773-24cd-4502-806c-2776e434a183)\n\n", "url": "https://www.ycombinator.com/launches/Pat-mousecat-ai-powered-fraud-investigations", "votes": 20, "created_at": "2026-03-03T07:13:58.567Z" } ] }, { "id": 36086, "name": "Hesper AI", "slug": "hesper-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/22aa0e43c8572c7233989f7b9c4d18841280c577.png", "website": "https://gethesperai.com", "all_locations": "New York City, NY, USA", "long_description": "Hesper AI builds AI agents that investigate P&C insurance claims end to end.\r\n\r\nHesper’s agents read the claim file, detect tampered documents, verify facts, check coverage, flag inconsistencies, and produce an audit-ready case file in minutes, so insurers can check every claim. \r\n\r\nWe work with Claims and SIU orgs at Carriers, MGAs and TPAs,", "one_liner": "AI Claims Investigator for Insurance", "team_size": 2, "industry": "Fintech", "subindustry": "Fintech -> Insurance", "launched_at": 1788805829, "tags": [ "Insurance", "Fraud Detection", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "Fintech", "Insurance" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/hesper-ai", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/hesper-ai.json", "year_founded": 2026, "city": "New York City", "country": "US", "linkedin_url": "https://linkedin.com/company/hesperai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/22aa0e43c8572c7233989f7b9c4d18841280c577.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/5ca8ebd84e7156b5e441163d33d15b54026132e9.png" ], "primary_partner": { "name": "Nicolas Dessaigne", "url": "https://www.ycombinator.com/people/nicolas-dessaigne" }, "founders": [ { "user_id": 675827, "full_name": "Pankaj Dhariwal", "title": "Founder/CEO", "founder_bio": "Previously Engineering Manager at Nanonets(YC W17), where he built Flow (Accounts Payable product) from scratch to $200K ARR and scaled the engineering team from 7 to 25. Earlier, first engineer on the Export-Import team at Shipsy, building the global container tracking and ETA prediction system from the ground up. \n\nDual degree in Electronics & Economics from BITS Pilani.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/pankaj-dhariwal/", "x_url": "https://x.com/Pankaj_Dhariwal", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5fd48563dab99d3645e4c2b77229cca94318288e.jpg" }, { "user_id": 559900, "full_name": "Nitish Badu", "title": "Founder/COO", "founder_bio": "Nitish Badu is the Co-founder/COO of Hesper AI.\n\nPreviously, he scaled revenue at Nanonets (YC W17) to $4M in ARR, where he built out the GTM motion, outbound and partnerships. He has approximately 4 years of experience driving go-to-market and revenue for B2B AI companies.\n\nBefore that, he was GTM Lead at Super.AI. He holds a degree from BITS Pilani.", "is_active": true, "linkedin_url": "https://linkedin.com/in/nitish-badu-529b28165/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/69238018c926d7c71b158727d1a9b0265d6fe175.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 36347, "name": "Latent", "slug": "trylatent", "former_names": [ "latent" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/21bc57f59a4d430a3377808350ec1df0915386dd.png", "website": "http://runlatent.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Latent catches wrong or unsupported LLM answers before they reach your users. Instead of running a second model to check every output, it reads the model's own internal state as it generates, so every request is verified with no added latency, no extra inference cost, and nothing leaving your deployment. Teams running LLMs in production, in finance, legal, healthcare and customer support, get a review queue of the answers most likely to be wrong, calibrated to their own model and traffic, with a plain explanation of why each one was flagged.", "one_liner": "Catch wrong LLM answers before your customers do", "team_size": 1, "industry": "B2B", "subindustry": "B2B -> Security", "launched_at": 1791243197, "tags": [ "Fraud Detection", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "B2B", "Security" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/trylatent", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/trylatent.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/runlatent", "x_url": "https://x.com/runlatentai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/21bc57f59a4d430a3377808350ec1df0915386dd.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/c1b0002c0ecf5d467cc3f45b070da5afc1a5ae12.png" ], "primary_partner": { "name": "Harj Taggar", "url": "https://www.ycombinator.com/people/harj-taggar" }, "founders": [], "jobs": [], "news": [], "launches": [] } ]