Healthcare AI Diligence for Deal Teams
I help deal teams decide whether those claims belong in the underwriting model.
18 years shipping regulated healthcare products — including AI-enabled products in cardiac, diagnostics, clinical trials, and enterprise healthcare workflows. Product, regulatory, data, and implementation risk translated into investment consequences before those issues become hold-period drag.
You are heading into IC on a target with AI at the center of the thesis and need an independent read before the memo goes out.
You closed on strong pilot results and 90 days later the implementation record is not what the sales plan assumed.
A regulatory or enterprise-readiness issue in your portfolio needs to be priced before it becomes hold-period drag.
Start with the Sale vs Scale Read. In 48 hours, you know whether the AI story belongs in the underwriting model, the risk register, or outside the investment thesis.
If the read surfaces issues worth addressing, it becomes full diligence. If you close and need to build what was promised, it becomes a Regulated AI Value Creation Blueprint. If you have multiple holdings and ongoing exposure, it becomes Portfolio AI Signal Watch.
Three offerings, one entry point. Scoped to where the deal is.
01 — Live deal diligence
The independent read before the IC memo goes out. Fixed-fee. Written for IC circulation.
What changes for you
After the Sale vs Scale Read you know three things: what role AI plays in the thesis — upside, risk, or noise; what to fix before close and what each gap costs if it runs through the hold period uncorrected; and what the operating team inherits on Day 1.
AI & Product Integrity Validation
The brief includes a dedicated section that answers five questions, each mapped to its investment and operational consequence — not a standalone technical audit.
Verdict format
Risk-prioritized findings with remediation cost estimates. IC-ready summary written to be forwarded to the deal team, operating partner, or IC without rewriting.
02 — Post-close value creation
For portfolio companies where the AI thesis needs an operating model.
Situation
Where the deal thesis assumed AI-enabled margin improvement, implementation leverage, or reduced services intensity — and nobody translated that into operating choices before the check cleared.
Or where a portfolio company is going to market and the AI narrative needs to be cleaner and more defensible before the next buyer's diligence team finds the gaps first.
Scope
A defined engagement to design the AI operating model a portfolio company needs to scale inside regulated enterprise environments. Which use cases to prioritize. Which data and workflow constraints will slow execution and by how much. What controls enterprise buyers and regulators require. What the team owns versus builds versus buys.
For portfolio companies that need a senior operator who has built and shipped regulated AI products in enterprise healthcare environments. Not one who has studied them.
03 — Portfolio monitoring
Post-close surprises usually started as diligence assumptions. This monitors whether those assumptions are holding across active healthcare AI holdings.
By the time it shows up in the numbers, you are already behind. You inherited incomplete data, fragile workflows, and governance debt. You just did not know it yet.
Quarterly check-ins tracking regulatory posture, AI architecture decisions, state law developments, and enterprise integration readiness as companies scale. A direct line when a portfolio company hits a procurement obstacle, a value creation milestone, or a regulatory development that changes what the operating team should prioritize next quarter.
One recent engagement began as a deal screen. After the preliminary read surfaced issues the data room had not shown, it converted to ongoing coverage across multiple holdings.
Best fit
Healthcare-focused investors evaluating AI-enabled, regulated, workflow-heavy businesses where product, regulatory, data, or implementation risk could affect underwriting or post-close value creation.
Less useful for
Funds without a healthcare thesis, purely financial-engineering buyers, or teams whose internal bench already covers regulated AI, product, and implementation diligence at deal speed.
For operating partners
The deal assumed AI would improve margin, accelerate implementation, or increase defensibility. Nobody translated that into operating choices before the check cleared.
Now you need to know which AI claims will hold up in operations. Which use cases to sequence first. What the implementation burden looks like when the next five customers are not the motivated early adopters who made the pilot look easy.
When you get it wrong, you spend the first year discovering what should have been priced at close.
Email directlyDiligence case
A company where quality had not been actively managed in years. A single audit cycle surfaced 84 non-conformances in three months and $2.3M in costs the board had not seen.
Found pre-close, $2.3M in quality costs is a pricing and structuring input. Found post-close, it is a floor, not a ceiling — compounding through remediation, enterprise audit delays, and the exit narrative.
Thesis read
A voice AI mental health product. The technology worked. Discovery interviews with psychiatrists showed no workflow pain, no billing hook, no urgency. Clinicians were managing around the problem — not asking for it to be solved.
The correct read: a vitamin, not a painkiller. The recommendation was to license the technology to an existing EHR platform rather than build a standalone company.
Value creation
Before a line of code was written: buyer interviews run, platform narrowed to two workflows, implementation methodology documented before the first customer signed.
The team grew from 0 to 65 across three countries. Retention held above 90% through Series A. ARR expanded 3.8x.
A live deal on the table, a portfolio company where the AI thesis is ahead of the operating model, or a regulatory exposure that needs to be priced — reach out directly. Response within 24 hours.