Healthcare teams are deploying AI faster than they can govern it.
I build the systems that keep up.
I came out of Oracle Cerner solution analysis and healthcare data product management, so I know what breaks when AI touches a clinical workflow. I work with health systems, federal health agencies, and the founders building for both.
You don't have an AI problem. You have a systems problem.
Most teams:
- Use too many disconnected AI tools
- Don't know what should (or shouldn't) be automated
- Risk data privacy, compliance, and accuracy issues
- Waste time on workflows that don't scale
That's where I come in.
Safe
Aligned with compliance and real-world constraints.
Efficient
Reduce manual work without breaking workflows.
Usable
Built for how people actually work, not how AI 'should' work.
A clear process from audit to automation.
Built for teams where a wrong AI output reaches a patient, a claim, or a citizen.
Compliance, accuracy, and trust aren't optional. Neither is structure.
Orinyx. Independent verification for clinical AI, before it reaches a patient.
Orinyx is the first product out of the charmthirteen studio, built from a question the advisory work kept running into. Hospitals wanted an independent way to test clinical AI before deployment, and no one could give them one. Orinyx checks every AI assertion against authoritative sources instead of another model's judgment, so each verdict is deterministic and arrives with a citation.
- Phase 1 runs in a sandbox on synthetic data, no PHI, no EHR connection
- Deterministic verdicts with citations showing what was checked
- Findings documented for governance review
- Design partner pilots open now
Claude Loves Lovable
A founder-focused vibe coding series on shipping real product with Claude and Lovable. Five live sessions, and every one ships a Claude skill you keep.
AI Readiness & Workflow Assessment.
Most AI consultants have never watched a clinical workflow break in production.
That background is why the recommendations hold up after I leave the room.