Healthcare AI consulting for teams where a wrong output is a clinical event.
Most AI consulting in healthcare stops at the pilot. The dashboard ships, the press release goes out, and six months later nobody on the clinical floor is using the model. We work the other direction. Strategy, systems, and implementation built around the people who will actually press the button.
For hospitals, payers, digital health startups, and clinical AI vendors. HIPAA-aware, audit-ready, and honest about what AI should and should not be doing inside a regulated workflow.
Healthcare AI projects don't fail in production. They fail before they ever get there.
A clinical team gets handed a tool. The vendor demo was crisp, the leadership memo was confident, and the pilot was approved with a 90-day window. Then three things happen in this order:
- Compliance review surfaces questions nobody scoped for. PHI handling, model logging, vendor BAAs, FDA classification, state-level rules. The legal lift triples.
- The clinical workflow turns out to be three workflows in a trench coat. The model fits one of them and breaks the other two.
- The people who were supposed to use the tool never got training that respected how they actually work. Adoption flatlines at 12 percent.
The fix is not a better model. It is a better system around the model. That is what we do.
Built for teams where AI decisions touch a patient.
Four ways we work with healthcare teams.
We do not just consult on clinical AI safety. We built one.
Orinyx is an independent pre-deployment verification platform for clinical AI. Phase 1 runs in a sandbox on synthetic data, with no PHI and no EHR connection, and every AI assertion is checked against authoritative sources instead of another model's judgment. Each verdict is deterministic and arrives with a citation showing what was checked, and findings are documented for governance review.
Building it forced us to live the same questions our healthcare clients live. What does HIPAA actually require versus what people assume it requires. Where the FDA SaMD line really is. How to keep a model fast enough to use at the point of care without keeping PHI at rest. The patterns from Orinyx show up in every healthcare engagement we run.
Learn more about Orinyx ↗Things we will tell you not to do.
- Put a general-purpose LLM in front of a clinician with no verification layer.
- Buy an AI scribe before the documentation workflow it is replacing is actually understood.
- Sign a BAA you have not had reviewed by counsel who knows healthcare specifically.
- Move PHI into a vendor environment to save two weeks of integration work.
- Pilot a model in a high-acuity unit because that is where the data is most interesting.
- Ship anything that cannot be audited line by line after the fact.
Healthcare AI consulting, plainly answered.
How is this different from a Big-Four AI consulting practice?
Big firms staff with generalists and bill for change management. We staff with operators who have built clinical AI in production and only take engagements where we can hand back a working system, not a deck.
Do you work with hospitals or only startups?
Both. Most engagements have one foot in each. Hospitals need help adopting AI safely. Vendors need help selling into hospitals without tripping every compliance wire on the way in.
Can you sign a BAA?
Yes. We default to zero-PHI engagements where possible. When PHI is genuinely required, we sign a BAA and scope the data access narrowly. In our experience zero-PHI is almost always achievable.
Do you build the AI or just advise on it?
Both. The Implementation engagement includes hands-on build of the workflow, prompt systems, integrations, and audit logging. We do not subcontract delivery.
What does a typical engagement cost?
A Readiness Audit runs four to six weeks at a fixed fee. System Design and Implementation are scoped after the audit. Vendor Advisory is monthly. The Vibe Check is free and is the right place to start.