AI due diligence
You are evaluating a company whose value rests on its AI, and you need to know if that value is real. This is not a separate service — it is the same technical due diligence engagement, going deeper where AI is part of the thesis, and answering the questions ordinary tech diligence is not built for.
Why AI needs its own layer of diligence
Traditional software due diligence assumes deterministic systems: clear inputs, clear IP, measurable performance. AI breaks those assumptions. A model’s value depends on where its training data came from and whether there is a right to use it, on whether results reproduce outside a demo, and on a data pipeline that has to keep working in production. Miss that, and it is easy to overpay for an impressive demo sitting on a fragile system.
Generalist tech diligence usually stops at the integration point, having confirmed the feature works. The questions below are the ones that decide whether the company owns anything.
Signature question
Real AI, or an API wrapper?
The first question I answer on any AI-core deal. A wrapper is not worthless, but it prices like a feature, and you should know which one you are buying.
What I look at
Data provenance & rights
Where the training data came from, and whether there is a legal right to use it.
Real AI vs. API wrapper
What is genuinely built, and what is rented from a third party.
Model performance & reproducibility
Does it work outside the demo, and can it be rebuilt?
MLOps maturity
How models are versioned, deployed, monitored and retrained.
Third-party API dependence
The concentration risk in the stack you are acquiring.
Regulatory exposure
Compliance obligations and legal risk in how the AI is built and used.
The AI layer never replaces the technical foundation — architecture, code quality, security, intellectual property, key-person risk and running costs are assessed on every engagement, AI or not.
Process, depth levels and fees →
Where AI risk concentrates in healthcare software and fintech.
Common questions
- How is AI due diligence different from ordinary technical due diligence?
- Ordinary technical due diligence assumes software that behaves the same way twice, with traceable inputs and measurable performance. AI satisfies none of that. AI due diligence adds training-data provenance and the legal right to use it, model performance and reproducibility outside the demo, MLOps maturity, third-party API concentration, and regulatory exposure. Every engagement here covers the technical foundation; where AI is part of the thesis it gets its own dedicated layer.
Have a deal in motion?
Tell me the target, what you need to know, and your timeline. I will tell you whether and how I can help, usually the same day.
Engagements are confidential and run under a mutual NDA.