Healthcare AI and founder-led product engineering
Scrubs Co-Pilot
I founded and built an ambient clinical AI platform that turns clinician-patient conversations into structured medical documentation.
- Role
- Founder and CTO
- Team
- Led a team of 4–5 engineers and designers
- Status
- Built from zero to a production MVP with clinicians
- Stack
- OpenAI, Whisper, RAG, Supabase
Outcome
The platform reduced clinician documentation time by about 80% by focusing the AI system on the practical work of turning conversations into usable medical documentation.
- ~80%less clinician documentation time
- 0 → MVPbuilt end to end, validated with clinicians
- RAG + LLMreal-time transcription into structured notes
What I owned
Owned the product and engineering work from clinical workflow discovery through application architecture, infrastructure, and production readiness.
Built the workflow across ambient listening, transcription, retrieval, structured note generation, and the interfaces clinicians use to review information.
Set technical direction while balancing product speed with data security, clinical workflow quality, and dependable delivery.
System at a glance
Ambient audio from the clinical conversation enters a transcription workflow designed around clinical conversations.
Key decisions
- Designed around clinician workflow validation, because an impressive model output is not enough if it does not fit the way care is delivered.
- Treated retrieval and structured generation as a product system with security and review needs, not as a model demo.
- Kept ownership end to end so product tradeoffs, infrastructure choices, and clinical feedback could move together.
How I approached reliability
- Used real healthcare-professional feedback to guide output quality and workflow decisions.
- Built security and production readiness into the roadmap rather than treating them as a post-MVP concern.
- Structured outputs made it practical to review and improve documentation workflows over time.
Technology focus
- OpenAI
- Whisper
- RAG
- Supabase
- pgvector
- React
- Node.js
- Twilio Voice
- Docker
- AWS