AI for healthcare — where patient data protection and clinical accuracy both matter.

Healthcare organizations live inside a contradiction most AI vendors gloss over: the system has to be genuinely useful at the point of care or in the back office, and it has to treat patient data as if a regulator, a patient, and a plaintiff's attorney are all watching at once — because eventually, one of them is.

I build AI that holds both ends at once. Grounded retrieval so answers come from your records and source documents, not a model's guess. PHI handling designed around minimum necessary use, with an on-prem inference option for the data that can't leave your environment, and audit trails that show exactly what a system saw and why it answered the way it did. Useful where the work demands it, disciplined where privacy does.

Where AI moves the number

The highest-impact work in a healthcare organization is high-volume, document-heavy, and expensive in staff hours — exactly where a well-built, well-governed agent earns its keep.

• Clinical & operational copilots — retrieval-grounded assistance for care teams and operations staff, drafting and summarizing against source documents, never inventing findings.
• Payer / claims automation — prior auth, claims triage, and eligibility workflows that route the routine cases and flag exceptions for a human.
• Document intelligence — search and synthesis over records, policies, and clinical guidelines, with every answer traceable to its source.

In healthcare, an unsourced AI answer is worse than no answer at all. I build systems that show exactly where every claim came from — and that stay quiet when the source isn't there.