Healthcare AI consulting

The clinical line comes first

We draw a hard line between administrative and clinical AI. Administrative AI handles the paperwork of care: scheduling, prior authorization, coding and billing, claims, and ambient documentation that drafts a note from a visit for a clinician to check. Clinical AI informs or makes a care decision: triage, diagnosis, treatment. We build and integrate the administrative kind, and for anything that touches a clinical decision we keep a clinician accountable and in the loop, because that is both the safe design and, often, the regulatory line. Software that makes or drives a clinical decision can fall under FDA oversight as a medical device, which is a different project with a different owner, and we will tell you when you have crossed into it.

This page is general information about our services, not legal, medical, or clinical advice.

HIPAA and PHI boundaries

Protected health information sets the boundaries for everything. Before any model sees data we map what is PHI, where it lives, which vendors touch it, and what a business associate agreement has to cover, under HIPAA. Often the safe design keeps PHI inside your environment and never sends it to a third-party model at all. We build to least-privilege access as a default, and the assessment says plainly what data each part of a system is allowed to see.

Where the honest wins are

The administrative half of healthcare is heavy with manual, repetitive work, and that is where AI pays off soonest: drafting clinical notes for a clinician to approve, cutting the time on prior authorization and coding, answering routine patient questions, and moving data between systems. Physicians are already adopting this. The American Medical Association found two in three physicians using health AI in its latest survey, up 78% from 2023. What we add is the read on which of these fits your systems and your risk posture, and which will not survive an audit.

How an engagement works

Everything starts with the assessment: fixed scope, 3–6 weeks, $20,000 to $80,000 (where in that range depends on scope: how many systems and teams we assess, company size, and regulatory exposure). You get a plan with the use case, the PHI boundaries, the governance work, and costs attached. Sometimes the recommendation is not yet. Build work follows only if it earns it; published 2026 ranges put a scoped production build at $50,000–$250,000. And that band is a single scoped build; multi-system, multi-year programs run seven figures and beyond, and scale like that is work we take. The governance side, policies, access rules, and HIPAA-aligned controls, is our AI governance and AI risk assessment work, usually the first thing we stand up.

Who does the work

The senior people at Tillerbridge are Nick Major, an engineer, and Isaac Major, an operator. Backgrounds are on the about page. We are a young firm, so there are no hospital logos here and we will not borrow any; what we show instead is published pricing and a fixed-scope process. The wider enterprise realities are on enterprise AI consulting, more of what is working is in our AI use cases, and this is the healthcare shape of our broader AI consulting.