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"body": "## **TLDR:** Homomorphic encryption offers much stronger privacy guarantees than anyone thought possible. Blyss is the future of privacy, available today as a [developer-focused API](https://blyss.dev/).\n\nWe are Samir and Neil, the founders of Blyss.\n\n\n\n## 😧 Problem: outside of FAANG, privacy is stuck in the ‘90s🕺\n\nPrivacy is a competitive advantage. There’s a billboard on the 101 explaining how WhatsApp can’t read your messages, millions of consumers use VPNs to secure their everyday browsing, and Apple doesn’t finish a keynote without touting new, frontier-pushing privacy practices. \n\nBut unless you’re at big tech, the most powerful new privacy tools are almost completely inaccessible. Chrome and Edge use homomorphic encryption to scan unsafe URLs and check passwords, but a startup wanting to offer similar features would need a team of PhDs to build them in-house.\n\n## 🛠️ Our solution: cutting-edge privacy, as a service\n\nThe [Blyss SDK](https://blyss.dev/) offers developers a key-value store with unusually strong privacy guarantees. Create S3-like buckets, fill them with data, and then make cryptographically secure retrievals. No entity, not even the Blyss service itself, can learn which items are retrieved from a Blyss bucket.\n\nPrivate retrieval is a simple but powerful new primitive. Using it,\n\n* Password managers can check if you’ve been exposed in a data breach, without learning anything about your passwords\n* Browsers can resolve domain names privately, without revealing every website you visit to DNS servers\n* Code scanners can safely check repos for vulnerabilities, without ever seeing customer codebases or the scan results\n\nPeople are building lots of new web apps with Blyss - try some of the best ones at the [Blyss playground](https://playground.blyss.dev)!\n\n# 🙇 The ask\n\n## 🧑💻 Developers:\n\nTry [our SDK](https://blyss.dev/) - it’s a self-serve, cloud storage service (feels just like S3), that transparently handles encryption and privacy for you. And if it isn’t quite clear how to fit privacy into your app, we’ll help you!\n\n## 🕵️ Privacy-conscious users:\n\nTell us about apps/services that feel icky because they collect too much data. It may not be obvious how to fix them, but your gut feelings on privacy are super valuable to us.\n\n## 🕴️ Bonus:\n\nIf you know companies that are trying to make privacy their competitive advantage (password managers, VPNs, etc), introduce us ([founders@blyss.dev](mailto:founders@blyss.dev))!\n\n## 👯 About us\n\nWe met eight years ago on Day 1 of freshman year at Stanford; we’ve been best friends since. Neil was previously at Apple working on ML; Samir worked at Yubico on applied security, and before that did research on homomorphic encryption with Dan Boneh. We [published](https://eprint.iacr.org/2022/368) the fastest-ever scheme for private retrieval at a top cryptography conference (Oakland). Now, we’re working to make advanced privacy technology available to everyone.",
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"title": "Vizly – Chat with your data",
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"body": "### TL;DR - Explore your Data by Asking Questions, No External APIs Required\n\nBuilt by Apple and NASA engineers, [Vizly](https://www.vizly.fyi/) is an on-premises, AI-powered data analysis tool that allows users to perform data tasks using plain English. Instead of learning complicated BI tools, customers and enterprises can use Vizly to get AI-powered data analysis, without exposing any of their sensitive data to unsecure external APIs.\n\n### **The Problem**\n\nBI tools are complicated and have a steep learning curve to use. Despite enterprises shifting to data-centric cultures, the adoption rate of BI tools in enterprise has remained at **only 15%** \\[1\\]. With more people wanting to access data, existing BI tools have not kept up. This has left data teams to pick up the slack with analysts often spending large portions of their day fighting a barrage of data questions.\n\n### **Our Solution**\n\nWith [Vizly](https://www.vizly.fyi/), we are building a dead simple BI tool that runs fully on-premises and allows anyone to perform their data tasks using plain English. Instead of needing expensive hardware to run, a Vizly instance is both powerful enough and small enough to run even on a single MacBook.\n\nA single employee can set up Vizly on their machine by simply selecting the data sources and sharing a link with their non-technical colleagues. With just a link, anyone in the enterprise can explore the shared data sources using plain English. It operates securely behind the firewall, ensuring no data ever leaves the enterprise network.\n\n# Vizly\n\nTry it out [here](https://www.vizly.fyi/).\n\n\n\nVizly makes data analysis as easy as asking a question.\n\n# **The Team**\n\nSami and I met at McGill University and have been friends for almost a decade. We previously built and sold [YouTube Party](https://chrome.google.com/webstore/detail/youtube-party/ngmncgebkdchkdokiecifpmodajbaopj) together, a project that gained over 300K weekly active users.\n\nNow we are using our expertise from NASA and Apple to build the next generation of enterprise BI tools.\n\nPlotly HQ in Montreal, 2019:\n\n\n\nSan Francisco, 2023:\n\n\n\nHere’s a little about the team (from left to right):\n\n[Sami](https://www.linkedin.com/in/samisahnoune/): Implemented data visualization libraries at Splunk and previously built critical data visualization systems at NASA Jet Propulsion Laboratory, enabling the Mars Perseverance Rover in its mission of drilling samples from Martian terrain. Previous experience at Plotly, building one of the most widely downloaded low-code data tools.\n\n[Ali](https://www.linkedin.com/in/ali-shobeiri/): Machine learning engineer at Apple, building out people-based recommendations. If you’ve ever shared a photo on your iPhone, or done autocomplete searches, you used a model Ali deployed. Previous experience at Plotly, Microsoft, and Unity Research.\n\n# **Our Ask**\n\n* Give Vizly a try at [vizly.fyi](https://www.vizly.fyi/)\n* Get in touch to tell us about your data needs at [ali@vizlylabs.com](mailto:ali@vizlylabs.com)\n* [Schedule a time to chat](https://cal.com/vizly/30min)\n\nLet us know what you think! Feel free to reach out to [founders@vizlylabs.com](mailto:founders@vizlylabs.com) anytime!\n\n\\[1\\] - ",
"url": "https://www.ycombinator.com/launches/JNS-vizly-chat-with-your-data",
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"body": "### TL;DR\n\n[Unbound Security](https://www.unboundsecurity.ai/) prevents leakage of sensitive information when employees use Gen AI applications. For example, we intervene and stop when someone pastes a code snippet that contains a secret key into ChatGPT while troubleshooting.\n\n# The problem\n\n1. **AI faces enterprise adoption roadblocks**: Despite Gen AI boosting productivity, many enterprises remain wary. Even in 2024, over 1 in 4 organizations have banned all AI apps, keeping them off-limits to employees.\n2. **AI app sprawl:** There are now well over 1,000 Gen AI apps, and many have questionable security practices. These cool “tools” continue to worry CSOs (Chief Security Officers) as employees are already using many of these to get work done.\n3. **Data privacy remains a top concern**: Over 55% of all prompts contain some form of Personally Identifiable Information (PII). This is not only a privacy nightmare, but also pushes enterprises out of compliance with multiple regulations such as PCI, SOX and HIPAA.\n\n# Our solution\n\n1. **AI app discovery:** We discover and catalog all AI apps in use by employees and help security teams understand the risk levels of each of these apps.\n\n \n\n2. **Granular application access policies:** Rather than block all apps by default, we allow enterprises to allow some trustworthy AI apps and steer users towards those. For example, if your org invested in Chat GPT enterprise, we help get the most out of that investment by encouraging people to use that instead of a random cool file summarizer on the internet.\n\n \n\n3. **Protect data leakage:** On the allowed AI apps, Unbound monitors all prompts and also blocks users from sharing sensitive information based on the configured AI usage policy.\n\n \n\n\n# Our ask and offer\n\n1. If you know a company that has blocked the usage of AI apps, we can help your friends use these apps with IT consent. Please drop us a note at [founders@unboundsecurity.ai](mailto:founders@unboundsecurity.ai).\n2. If you’d like to understand what people are using AI for within your org, and stay compliant, we are happy to offer our AI visibility platform for free to any YC company. Let’s [chat more over a meeting.](https://calendly.com/rajws/30-mins-meeting)\n\n# The team\n\n[Vignesh](https://www.linkedin.com/in/vigneshsubbiah16/) and [Raj](https://www.linkedin.com/in/rajaramsrinivasan/) worked together at Adobe on the same Engineering team for 5+ years, solving some of the most complex problems in the digital advertising space - The team built sub-millisecond latency systems that processed billions of advertising artifacts. Vignesh went on to be founding engineer at Tophatter and an early engineer at [Shogun (YC S18)](https://www.ycombinator.com/companies/shogun). Raj has built data security products for Palo Alto Networks and Imperva prior to Unbound where he observed the struggles of data security programs in large enterprises.\n\n\n\n",
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"title": "Clearly AI - Your AI Trust Team",
"tagline": "Automate security and privacy with AI that understands your software.",
"body": "**TL;DR:** [Clearly AI](https://clearly-ai.com) continuously reviews your software for security and privacy risks. We keep your compliance paperwork up to date and escalate issues that need attention so that your engineers can focus on more critical work.\n\n### The Problem\n\n* **Burn-out:** Many companies struggle to complete in-depth security reviews due to understaffed teams. The industry average is 1 security engineer per 200 software engineers. There is an overwhelming volume of software and policies to monitor and cross-check.\n* **Fragmentation:** Today, a typical enterprise security review takes 3-4 months to complete. Most of that time is spent gathering information and filling out paperwork. Trust teams operate in silos, leading to redundant questions or delayed risk identification.\n\n### Our Solution\n\n* **Triage:** Review all designs and code for security best practices and regulatory compliance.\n * **Raise the Ceiling:** Prioritize which applications need deeper review by security & privacy engineers.\n * **Raise the Floor:** For the rest, perform baseline security & privacy reviews to ensure compliance.\n* **Shift Left:** Bring the collective knowledge of legal, privacy, compliance, and security teams directly to developers from the inception of their work.\n* **Continuous Monitoring:** Real-time, automated checks instead of point-in-time reviews.\n\n\n\n### Our Ask\n\nReach out to us if:\n\n* Your security/privacy teams are understaffed and overwhelmed.\n* Security/privacy is critical to your customers (beyond SOC2 certification).\n* You operate across regions with varying privacy regulations, and you need to evaluate and comply.\n\n### Our Offer\n\nGive us your gnarliest security & privacy problems — we’re happy to do a [free consultation](https://calendly.com/emily-cg/security-privacy-consult) and lend our expertise back to the YC community.\n\n### Our Team\n\nEmily & Joe met five years ago while working on an Alexa security review.\n\n* [**Emily**](https://www.linkedin.com/in/emilycgreene/)**:** Senior engineer at Amazon and Moveworks, speaker at USENIX PEPR, expert in privacy-preserving machine learning, featured in the New York Times.\n* [**Joe**](https://www.linkedin.com/in/joseph-choi-cmu)**:** Senior engineer at Amazon, designed and secured spacecraft command software that now orbits Earth, automated data compliance with global regulations like GDPR and HIPAA.\n\n\n\n",
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"title": "Tinfoil: Verifiable Privacy for Cloud AI",
"tagline": "We host models and AI workloads on the cloud while guaranteeing zero data access and retention",
"body": "Sending your most sensitive or proprietary data to OpenAI et al?\n\nSick of deals getting stalled during enterprise or government security reviews?\n\nHate deploying on various configurations of on-prem infrastructure environments?\n\nSay less ✋\n\nWe’re [Tanya](https://www.linkedin.com/in/tanya-verma-130a23124/), [Jules](https://www.linkedin.com/in/jdrean/), [Sacha](https://www.linkedin.com/in/sacha-servan-schreiber-308341305/) and [Nate](https://www.linkedin.com/in/nate-sales/) from[ Tinfoil](https://tinfoil.sh/). What we’re building at Tinfoil sidesteps these concerns because we can host models while guaranteeing zero data access and retention.\n\nWe let you run powerful models like Llama, Deepseek R1 on cloud GPUs without you having to trust us – or the cloud provider – with your private data.\n\nSince AI performs better the more context you give it, we think solving AI privacy will unlock more valuable AI applications, just how TLS on the Internet enabled e-commerce to flourish knowing that your credit card info wouldn't be stolen by someone sniffing internet packets.\n\nhttps://youtu.be/wnwI22qT-QQ\n\n**Who we are**\n\nWe come from backgrounds in cryptography, security, and infrastructure. Jules did his PhD in trusted hardware and confidential computing at MIT, and worked with NVIDIA and Microsoft Research on the same, Sacha did his PhD in privacy-preserving cryptography at MIT, Nate worked on privacy tech like Tor, and I (Tanya) was on Cloudflare's cryptography team.\n\n We were unsatisfied with band-aid techniques like PII redaction (which is actually undesirable in some cases like AI personal assistants) or “pinky promise” security through legal contracts like DPAs. We wanted a real solution that replaced trust with provable security, and addressed our personal frustration with the false choice between exploring cutting-edge AI capabilities and personal privacy.\n\n\n\n**The magic sauce**\n\nRunning models locally or on-prem is an option, but can be expensive and inconvenient. Fully Homomorphic Encryption (FHE) is not practical for LLM inference for the foreseeable future. The next best option is using secure enclaves: a secure environment on the chip that no other software running on the host machine can access. This lets us perform LLM inference in the cloud while being able to prove that no one, not even Tinfoil or the cloud provider, can access the data. And because these security mechanisms are implemented in hardware, there is minimal performance overhead.\n\nUp until recently, secure enclaves were only available on CPUs. But NVIDIA confidential computing recently added these hardware-based capabilities to their latest GPUs, making it possible to run GPU-based workloads in a secure enclave.\n\nBelieve it when you see it, but Tinfoil has negligible performance overhead despite the strong security & privacy guarantees.\n\n**Getting Started**\n\n**The best part is you can start using confidential AI chat and inference with Tinfoil today!**\n\n**Try our private chat: [tinfoil.sh/chat](http://tinfoil.sh/chat).**\n\n[**https://www.youtube.com/watch?v=FuFszT0_vXk**](https://www.youtube.com/watch?v=FuFszT0_vXk)\n\n**Build with our API:** Drop-in replacement for the OpenAI chat-completions API with Tinfoil\n\n\n\nWe support several popular open source models like Deepseek R1 70B, Llama3.3 70B, Mistral 3.1 24B. Missing your favorite model? Just ask us to add it!\n\nTry out our free version running Llama3.3 70B in [Colab](https://colab.research.google.com/drive/1DhpUSmSWjxERnEo3U0qyAyApfcdIhvMv?usp=sharing).\n\nSee how Nate psychoanalyzes himself by sending all his iMessage chats to Tinfoil!\n\nhttps://www.loom.com/share/2381c02ba51043528c6d3bdf61270bc0?sid=9ea0e955-ae68-4ce4-9c86-d06cab18d25c\n\n**Deploy your AI Application**: Make your AI application enterprise-ready in a matter of minutes. Here's a demo of how you can use Tinfoil to run a deepfake detection service that could run securely on people's private videos:\n\nhttps://www.youtube.com/watch?v=_8hLmqoutyk\n\nWe encourage you to be skeptical of our privacy claims. Verifiability is our answer. It’s not just us _saying_ it’s private; the hardware and cryptography let _you_ check. Here’s a guide that walks you through the verification process:[ ](https://docs.tinfoil.sh/verify)https://docs.tinfoil.sh/verification/attestation-architecture.\n\n**The Ask**\n\nIf you think this sounds interesting, or too good to be true, contact us at [**founders@tinfoil.sh**](mailto:founders@tinfoil.sh)**!**\n\nWe’d love to hear from you, whether you’re a:\n\n* startup looking to sell into regulated industries\n* government agency that does everything on-prem but doesn’t trust your AI team to handle sensitive data\n* neocloud trying to secure enterprise customers in regulated industries that don’t migrate from the big clouds to you\n* company providing your employees dead-simple confidential LLM chat app\n\nYou can also learn more on our [**blog**](https://tinfoil.sh/blog), and subscribe to our [**LinkedIn**](https://www.linkedin.com/company/tinfoil/) and [**Twitter**](https://x.com/TinfoilAI) to follow updates!",
"url": "https://www.ycombinator.com/launches/NZ8-tinfoil-verifiable-privacy-for-cloud-ai",
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"created_at": "2025-05-21T14:22:18.121Z"
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"long_description": "Egoist Machines is building infrastructure for a world where your personal context belongs to you, rather than to the apps and AI companies you use. Today, every assistant and consumer app builds its own version of you. We think that architecture is backwards. Your context should live with you, persist across products, and be available to any model, app or agent you choose.\r\n\r\nAI Passport is the first layer of that infrastructure. It brings the context you generate across AI assistants and everyday apps into one portable profile you control. Tell ChatGPT something and Claude can already know it, for example. Switch to a new product and it can understand you from the first interaction, without the new company needing to own the underlying data.\r\n\r\nApps integrate through Sign in with AI Passport, requesting only the context they need for a specific purpose and duration. Users can see what is being accessed and revoke that access later. Underneath, we have built our own memory infrastructure for fast, compact per-user retrieval and permissioned access.\r\n\r\nWe are distributing AI Passport directly to consumers through first-party products, while integrating it into consumer applications with existing distribution. Every new product that uses Passport gives users another reason to maintain their context there, and every additional Passport holder makes the context layer more useful to developers.\r\n\r\nThe next step is to build a true Egoist Machine: an intelligent system whose memory, incentives and actions are governed by the individual it represents. We call it Digital Domovoy, the hardware extension of AI Passport. Instead of your most personal context ultimately living on somebody else's servers, it lives and is processed locally on hardware you control. AI Passport becomes the permission layer between that private local context and the cloud models and internet services you choose to use.\r\n\r\nOur North Star is the Egoist Machine: an intelligent system whose memory, incentives and actions are governed by the individual it represents.",
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"title": "Egoist Machines: user-owned hyper-personalisation infrastructure for Web 4.0",
"tagline": "One portable, permissioned memory and context layer for hyper-personalisation across every AI application.",
"body": "**TL;DR:** We are building AI Passport, the user-owned context layer for consumer AI. It lets people carry their personal context between products while controlling exactly what each product can access, why, and for how long.\n\nRead our manifesto for the bigger vision: \n\nCreate your AI Passport to start taking control of your data: \n\n**Ask**: if you’re working on a consumer-facing AI application, or know someone who is, we’d love to chat: [founders@ego.ist](mailto:founders@ego.ist)\n\n\n\nThe next era of consumer technology will be **hyper-personalised**. The products that understand each user from the very first interaction will create fundamentally better experiences and, we believe, the next consumer decacorns. People increasingly lack the time and patience to train every new product from scratch.\n\nThe main blocker to ubiquitous consumer AI is that every major AI assistant and consumer app is building its own private and incomplete model of the same person. ChatGPT holds one model, Claude another, while the data in health apps, calendar, shopping history, travel accounts, wearables, and financial tools remain trapped in separate silos. No product has the complete context needed to understand you, and no user has a single place from which to control it.\n\nAs a result:\n\n\\- Users get bored of repeatedly explaining themselves;\n\n\\- Companies spend money building context that already exists;\n\n\\- Personal data remains trapped inside platforms the user does not control.\n\nThe natural end state of this inefficiency is one user-owned memory and context layer that sits above individual products. Apps request the specific context they need, and users grant scoped, time-limited access instead of permanently surrendering their data.\n\n**_That is AI Passport_**.\n\n---\n\n## How AI Passport works\n\nAI Passport is a centralised, user-controlled home for personal context.\n\nYou can think of it as **Link for personal context**. Link lets people securely save their payment and shipping information once and reuse it across businesses; AI Passport does the same for _all_ your context, including preferences, interests, goals, history, health, travel, and so much more.\n\nUsers bring this context into one centralised AI Passport by connecting the apps and AI assistants they already use. They can also save anything they discover online or on social media directly to their Passport. New information enters an approval inbox, where the user decides whether it becomes _private memory_ or part of their _living, shareable public profile_.\n\nThat same Passport then becomes a plug-and-play connector for consumer apps through our **Sign in with AI Passport** OAuth system:\n\n1\\. An app requests the specific context it needs.\n\n2\\. The user sees exactly what is being requested, why it is needed, and how long access will last.\n\n3\\. The user can approve, edit, or deny the request, with the option to grant one-time, time-limited, or continuing access .\n\n4\\. Every permission can later be inspected or revoked.\n\nWe are launching AI Passport directly to consumers and through distribution and deep-integration partners collectively reaching millions of users across dining, ride-hailing, wearables, healthcare, lifestyle, finance and more.\n\nWe built a custom memory backend for AI Passport. It is optimised for compact, exact retrieval across isolated per-user memory stores and supports cryptographic access control at the memory layer. The retrieval path is constrained by the user's active permission, so the product can access only the context authorised for that purpose and period.\n\n---\n\n## Who we are\n\n\n\n**Dr David Khachaturov** holds a PhD (Uni of Cambridge) in Computer Science and is the youngest-ever Fellow at St Catharine's College, Cambridge. Previously an ML Researcher at OpenAI (PaperBench) and Lead AI Security Researcher at FrontierOne.\n\n**Erin Meryl McGurk** grew 0 to 1M followers on socials in one year. Advised Uni of Cambridge, UK Govt, OpenAI on social media strategy. Founded social enterprise Girl Economics to 15 countries, 75 ambassadors, 10+ weekly events.\n\n---\n\n## Our Ask\n\n**For users:** create your AI Passport () and tell us what you think!\n\n**For builders:** if you are building a product that could benefit from richer user context, explore our developer docs () and feel free to reach out to [founders@ego.ist](mailto:founders@ego.ist).\n\nWe would especially value introductions to teams building consumer AI, wearables, health products, commerce experiences, or agents that need bounded access to sensitive personal information. If that is you, I want to chat: ",
"url": "https://www.ycombinator.com/launches/St3-egoist-machines-user-owned-hyper-personalisation-infrastructure-for-web-4-0",
"votes": 59,
"created_at": "2026-08-18T14:58:03.221Z"
}
]
},
{
"id": 33310,
"name": "Petrarch",
"slug": "petrarch",
"former_names": [],
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"website": "https://petrarch.co/",
"all_locations": "San Francisco, CA, USA",
"long_description": "Petrarch brings internal company data to frontier labs, starting with manufacturing and industrial companies. We source data including codebases, project files, and payments from bankruptcy courts, then de-identify and prepare the data for model training. \r\n",
"one_liner": "Internal industrial company data for frontier labs",
"team_size": 3,
"industry": "B2B",
"subindustry": "B2B",
"launched_at": 1782965522,
"tags": [
"Reinforcement Learning",
"B2B",
"Privacy",
"Data Engineering",
"AI"
],
"tags_highlighted": [],
"top_company": false,
"isHiring": false,
"nonprofit": false,
"batch": "Summer 2026",
"status": "Active",
"industries": [
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],
"regions": [
"United States of America",
"America / Canada"
],
"stage": "Early",
"app_video_public": false,
"demo_day_video_public": false,
"app_answers": null,
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"url": "https://www.ycombinator.com/companies/petrarch",
"api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/petrarch.json",
"year_founded": 2026,
"city": "San Francisco",
"country": "US",
"linkedin_url": "https://www.linkedin.com/company/petrarchinc",
"x_url": "https://x.com/petrarchinc?s=11",
"fb_url": null,
"cb_url": "https://www.crunchbase.com/organization/petrarch-e3b1",
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"primary_partner": {
"name": "Vivian Midha Shen",
"url": "https://www.ycombinator.com/people/vivian-midha%20shen"
},
"founders": [
{
"user_id": 3655939,
"full_name": "Ian Lee",
"title": "Founder/CEO",
"founder_bio": "Founder at Petrarch. Previously Fellow at Meridian AI building training data and GTM infrastructure for AI-native Excel in financial services. Technical Advisor at Scale AI, working on quality filtering and annotation for agentic assistants and reasoning models. BA in Applied Mathematics and Psychology from Harvard.",
"is_active": true,
"linkedin_url": "https://www.linkedin.com/in/-ianlee",
"x_url": "",
"avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/bceb6907c6226831472da7b1d7688785c1fb3c17.jpg"
},
{
"user_id": 2670953,
"full_name": "Samuel Hahn",
"title": "Founder/COO",
"founder_bio": "Founder at Petrarch. Previously led GTM at Magier AI (Techstars '24), building agentic PII-redaction and data-compliance products. Venture analyst at Mainstreet sourcing pre-seed fintech and deeptech. BA in Economics and History from Harvard.",
"is_active": true,
"linkedin_url": "https://www.linkedin.com/in/samuelhahn9/",
"x_url": "",
"avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c4c5f0acb76bd4a03ee95588c1e5324406ca2dae.jpg"
},
{
"user_id": 3594340,
"full_name": "Sudhish Swain",
"title": "Founder/CTO",
"founder_bio": "Founder at Petrarch. Previously built computer vision software for medical equipment identification deployed in California hospitals as Head of Software Engineering at Clean Sweep Group. Research intern at Brigham and Women's Hospital analyzing spatial transcriptomic cancer datasets. BA Integrative Biology at Harvard.",
"is_active": true,
"linkedin_url": "https://www.linkedin.com/in/sudhish-swain/",
"x_url": "",
"avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/098958191b28eaf5108b38cb0d2768964c348d55.jpg"
}
],
"jobs": [],
"news": [],
"launches": [
{
"id": 105007,
"title": "Petrarch — Unlocking enterprise data for tomorrow's economy",
"tagline": "The market for specialized data locked up in enterprise companies.",
"body": "# The market for specialized data locked up in enterprise companies.\n\n[Petrarch](http://petrarch.co) is unlocking enterprise data for tomorrow's economy.\n\n\n\nFrontier labs scraped the internet dry. Then, labelers contracted subject matter experts to hand-make data. Now demand has shifted towards the bespoke data locked up within industries like construction, manufacturing, and logistics.\n\nPetrarch targets companies with valuable **operational** **data** they haven't monetized, from **legacy** **businesses** and **startups** to companies undergoing **bankruptcy** and **pivots**.\n\nPetrarch **sources** suppliers, **cleans** and **restructures** the datasets, certifies **legal clearance**, and resells to **applied AI startups and labs.** They have already transacted construction plans, autonomous driving recordings, and codebases.\n\nWith experience at Scale and Meridian AI, founders Ian Lee, Samuel Hahn, and Sudhish Swain dropped out of Harvard to unlock the next wave of training data.\n\nIf you need proprietary datasets or think you have something valuable on your hands \\\n(or recently pivoted), reach out at [founders@petrarch.co](mailto:founders@petrarch.co) or visit us at [Petrarch.co](http://Petrarch.co)!",
"url": "https://www.ycombinator.com/launches/RJf-petrarch-unlocking-enterprise-data-for-tomorrow-s-economy",
"votes": 11,
"created_at": "2026-07-13T16:11:02.837Z"
}
]
}
]