SURGERi.ai is an AI and data strategy advisory for healthcare and life sciences — bridging the gap between AI ambition and the disciplines where it actually has to deliver: HEOR, clinical outcomes, finance, operations, and supply chain.
Across healthcare and life sciences, the AI conversation has overtaken every other conversation — but the real strategy work is downstream of a deeper question: what does the organization actually do with its data, across the disciplines where decisions get made?
SURGERi.ai is a boutique AI and data strategy advisory built for that question. The work spans six disciplines that used to be separate practices — HEOR and real-world evidence, clinical analytics and surgical registries, healthcare finance and reimbursement, operations and supply chain, AI readiness and adoption, and data engineering — and that increasingly are not.
Most consulting firms hand over a deck and a stack of recommendations. We work alongside your team — embedded, hands-on — to build the artifacts your decisions actually depend on: defensible evidence models, registry builds, AI roadmaps with named owners, cost-attribution analyses, operating-model redesigns. You don't get a report. You get a partner who delivers them with you.
What ties the disciplines together is methodological. The same panel models, causal inference, and rigorous statistical work that survive peer review are the methods that withstand a payer review committee, a CFO's questioning, or a board's scrutiny. AI doesn't replace that rigor. It accelerates it.
Healthcare and life sciences transformation rarely fails because of weak technology. It fails at the translation points — where data science meets clinical reality, where strategy meets evidence, where AI meets the room where decisions actually get made. The practice is built to live in those intersections.
Engagements are typically scoped over six to twelve weeks, principal-led, and sized for a specific decision — not for a billing cycle. Every engagement runs through an AI and data strategy lens, regardless of where it lands functionally.
Study design, causal inference, claims and EHR analytics, and economic modeling. Evidence built to stand up at ISPOR, in front of ICER, and in payer review committees — and to support market access, value demonstration, and Health Affairs–grade publications.
Registry design, outcomes measurement, and analytics platforms across cardiac, vascular, orthopedic, and oncology surgery — built with specialty societies, device manufacturers, and FDA-cleared programs. A decade of scaling clinical-registry infrastructure globally.
DRG- and charge-level cost analytics, social-risk-adjusted reimbursement modeling, value-based care financial design, and hospital financial performance — grounded in population-scale discharge data and panel-data methods that surface the patterns aggregates hide.
Healthcare delivery system design, pharmaceutical supply chain resilience, vertical and horizontal integration analysis, and value-based care operating models — drawing on doctoral research into post-pandemic operations and dynamic capability theory.
A focused 60-day engagement: current-state audit, prioritized roadmap, executive briefing. Built for medical affairs, clinical, finance, and operations teams under pressure to operationalize AI without compromising scientific rigor or regulatory standing.
Data architecture, source rationalization, governance, and modern data stack design — from rationalizing fragmented multi-source legacy environments to launching production analytics products at enterprise scale. The foundation every AI strategy quietly depends on.
A boutique advisory deliberately structured around a specific kind of problem — and a specific kind of buyer.
Every deliverable is designed to survive scrutiny — by a payer, by a regulator, by a peer reviewer, or by a CFO. Methods are explicit, assumptions are stated, sensitivity analyses are run. You leave the engagement with an artifact you can defend.
We work in your tools, on your data, with your people in the room. The output is what your team can actually use the next day: working economic models, registry schemas, AI roadmaps with named owners, evidence dossiers, cost-attribution analyses, code that runs. The deck is a byproduct, not the deliverable.
No pyramid. No bait-and-switch between the pitch team and the delivery team. The person who scopes the engagement is the person who does the work — which is why engagements are sized for what we can deliver, not what we can staff.
If you are attending either conference and would like a working session on a real problem — not a sales meeting — please get in touch in advance.
For pharma, biotech, and life sciences teams building AI and evidence capabilities. Available for working sessions on AI readiness, RWE strategy, medical affairs analytics, and supply chain resilience.
For HEOR, RWE, market access, and health policy teams. Working sessions on study design, econometric methods, and the operational realities of building evidence programs that scale.
The fastest way to find out if there's a fit is a 30-minute conversation about a real problem — not a generic capabilities walkthrough. No deck. No pitch. Just whether the approach applies.