AI for Private Equity and Venture Capital
Two distinct engagements live here: making the fund's own process faster, and finding out whether a target's AI is real. The second is where we are most often useful and it is priced so the conclusion is not for sale.
A meaningful share of targets describing themselves as AI companies have a thin wrapper over a third-party API, no proprietary data, and no defensibility. Establishing which is which takes about two weeks and it is the highest-value thing we do for funds.
AI for private equity and venture capital covers deal sourcing and screening, diligence document review and data room analysis, portfolio company monitoring and reporting automation, and independent technical due diligence on targets making AI claims.
AI due diligence, which is what funds actually need
When a target claims AI capability, the questions that determine whether it is defensible are specific and answerable in a fortnight. They are also questions a management team will answer very differently depending on who is asking and how.
| Question | What a weak answer looks like | Why it matters |
|---|---|---|
| What is proprietary? | "Our models are proprietary" with no data moat | A wrapper over a public API has no defensibility |
| Whose data trained it? | Customer data used without a clear contractual basis | A material liability and a diligence red flag |
| How is performance measured? | Demo quality, no held-out evaluation, no baseline | Frequently means nobody has measured it |
| What is the exception rate? | Accuracy quoted, exception rate unknown | Exception rate determines whether customers save time |
| What does inference cost? | Not tracked per unit | Gross margin at scale is unknown, which is a valuation input |
| What happens if the provider changes? | No abstraction, no evaluation harness | Provider drift is an existential dependency |
| Who actually built it? | One departed engineer | Key-person risk hiding inside a technology claim |
Priced independently of the deal
We charge a fixed fee for the assessment and we take no fee contingent on the transaction proceeding, precisely so that "this does not stand up" remains an available conclusion. A meaningful share of these assessments do reach it. This mirrors how we price AI due diligence generally, and it is the reason the output is worth having.
The fund's own process
Deal sourcing and screening
Screening inbound and identifying targets against a thesis is a well-shaped problem where the data — filings, funding records, hiring patterns, product signals, web presence — is largely external and obtainable. The output is a ranked list for a partner to work, not a decision.
Diligence document review
Data rooms contain hundreds of contracts with change-of-control provisions, unusual termination rights, exclusivity and non-standard indemnities. Extraction against a checklist finds the clauses that matter far faster than a first-pass read, and the citation makes verification quick. See contract analysis.
Portfolio monitoring and reporting
Collecting, normalising and reporting portfolio company KPIs is chronic manual work at every fund, made worse because each company reports differently. Structuring it is unglamorous and it recurs every quarter forever.
Do not model returns
Fund-level return prediction on a few dozen investments over a decade is not a modelling problem. Anyone offering it is fitting noise, and we will say so rather than build it.
Working with portfolio companies
- The value creation plan usually overstates AI. We will tell you which items are real, which are ordinary automation, and which are aspirational, before the money is committed.
- Data quality is the constraint more often than modelling. Most portfolio AI plans stall on data that was never fit for the purpose. See data quality.
- Shared capability across the portfolio can work. Where several companies share a problem shape, one build serving many is genuinely more efficient than five separate ones.
- Beware the AI narrative premium at exit. A buyer's technical diligence will ask the same questions we ask, and an unsupported claim discovered then is expensive.
- Regulatory exposure is inherited. A portfolio company doing credit scoring or insurance pricing carries Annex III obligations from December 2027 that become a diligence item at exit.
How an engagement runs
Diligence engagements are two to four weeks and priced independently of the transaction.
Scope and document review
What is claimed, what documentation exists, and what evidence would settle each claim.
Technical assessment
Architecture, data rights, evaluation practice, exception rates, unit economics and key-person concentration.
Management sessions
Specific questions to the people who built it, with the gaps between claim and evidence probed directly.
Findings
What is real, what is a wrapper, what is aspirational, what it would cost to make defensible, and what the risks are.
Post-investment support
Building the capability that diligence showed was missing, priced and scoped separately.
What you receive
A clear read on whether the technology claim holds, in the time a deal allows.
Claim-by-claim assessment
Each AI claim tested against evidence, with the gap stated plainly.
Defensibility analysis
What is proprietary, what is a wrapper, and what a competitor could replicate in a quarter.
Data rights review
What the models were trained on and whether the contractual basis holds.
Unit economics
Inference cost per transaction and what gross margin looks like at scale.
Key-person and dependency risk
Who built it, who maintains it, and what a provider change would do.
Remediation estimate
What it would cost and take to make the claim true, if it currently is not.
Is this the right starting point?
Worth being direct. There are situations in private equity and venture capital where custom AI work is the wrong spend, and those are listed rather than buried.
Worth doing if
- A target is making AI claims you cannot evaluate internally.
- You need technical diligence completed inside a deal timetable.
- A portfolio company's value creation plan depends on AI you want assessed independently.
- Portfolio reporting is manual, inconsistent and consumes the finance team every quarter.
- Diligence document review is the bottleneck on deal throughput.
Do something else if
- You want return prediction on fund performance. There is not enough data; it would be noise.
- You want confirmation of a decision already made. We price so that is not what you get.
- The target will not permit technical access or management sessions. Then nothing can be verified.
- Deal flow is too small for sourcing models to beat a partner's network.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
How long does AI due diligence take?
Two to four weeks including management sessions, which fits most deal timetables. Some of it can compress if the target has real documentation, and the absence of that documentation is itself a finding — a company with genuine capability can usually show you an evaluation harness and a performance history within a day of being asked.
What are the questions that most often expose a weak AI claim?
What is proprietary beyond the prompt; who owns the data the models were trained on and under what contractual basis; how performance is measured on held-out data against a baseline; what the exception rate is in production; what inference costs per transaction; and what happens when the model provider changes something. Weak answers cluster together, and the pattern is usually clear within a week.
Can you predict which investments will succeed?
No, and we will not pretend otherwise. A few dozen investments over a decade with a handful of outcomes is not a dataset — a model built on it fits noise and produces confident nonsense. What we can do is tell you whether a specific technology claim in a specific target holds up, which is a question with an answer.
Do you take a fee contingent on the deal closing?
No. We charge a fixed fee for the assessment and take nothing contingent on the transaction, precisely so that "this does not stand up" remains available as a conclusion. A meaningful share of our assessments reach exactly that, and the value of the exercise depends on it being possible.
Should we build shared AI capability across the portfolio?
Sometimes, and the test is whether several companies share a genuine problem shape rather than a category label. Where three portfolio companies all need document extraction over similar documents, one build serving all three is materially more efficient. Where they share only the word "AI" in their plans, a shared platform becomes a cost centre nobody owns.
Related verticals
Organisations in private equity and venture capital usually share data, buyers or regulators with these. All fourteen are listed on the Financial Services & Insurance page.
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