TL;DR: We’re pivoting - from building an AI SRE to building Cursor for pharma and medtech companies. Here’s what we’ve learned.
The past few months have been a roller coaster for Topaz T. and me, and I didn’t post as much as I’d like
We started Vespper (YC F24) from a personal pain: as a developer, I was thrown into endless on-call shifts: “Customer X is experiencing issues, what’s going on?”
When you’re on call for dozens of services, failure points are everywhere. As a result, I really appreciated teammates who jumped in with helpful insights:
- “RabbitMQ is exploding”
- “Add some-feature-id to ALLOWED_FEATURE_IDS”
When GPT-4 arrived I wondered: what if a digital on-call teammate did that - scan gazillion logs and dashboards and surface the issue?
That spark led me to meet Topaz, start a company, and join Y Combinator. YC was wild: hackathons in an Airbnb, bug-fixing, customer calls, and learning from people like Sam Altman, Tony Xu, Kyle Vogt, and Parker Conrad.
We grew fast, but after demo day we felt that something is not working. The product wasn’t creating enough value.
I've realized two things during that period. First, in B2B the product must deliver real, tangible value (save or make money).
Second is momentum. I found a great post by Erez Druk called ‘Guess It Until You Make It’, and after speaking with Erez, I learned we should decide and not wait. So we made the hard call to pivot.
Before Vespper I worked at Viz.ai and saw how much work regulatory, clinical, and quality teams do just to get to market - and stay there. After months talking with experts in pharma, medtech, and biotech, the picture became clear:
- Preparing submissions (IND, BLA, NDA, 510(k), CSR, narratives) is painfully manual. Even “simple” drafts take weeks/months.
- Timelines are extreme. INDs take months. NDAs/BLAs are 4–5x longer. For late-stage assets (e.g drugs), delay costs ~$1M/day (!).
- External search is fragmented. FDA/EMA workflows are slow and scattered.
We looked at the current tools and saw more gaps:
- Legacy tools are rigid and outdated. Hard to adapt to real data and evolving workflows.
- Current AI tools collapse on massive corpora needing retrieval, grounding, and traceability. A submission package often contains thousands of pages (~2 GB and more).
That’s the opportunity we’re tackling. In the past weeks we’ve built an AI-powered workspace for regulatory and quality teams:
- Generate first drafts of complex docs directly from data (IND, BLA, CSR, narratives, 510(k)).
- Search across internal files and external databases (FDA 510(k), MAUDE, etc.).
- Use a ChatGPT-grade copilot for anything regulatory.
It’s early, but experts already report meaningful time and cost savings versus generic AI tools.
We’re excited to share more soon. Next month we’ll be at a few conferences - details to come.
Whether you’re in life sciences curious about our product, or just want to talk AI and startups, feel free to DM me! 😊