Where due diligence actually burns time
PE deal teams spend much of due diligence reading rather than judging. They work through CIMs, data room folders, similar-looking contracts and five years of board minutes to find the sentence that changes the model.
AI is well suited to this work. It does not replace investment judgement. It shortens the time between opening the data room and knowing which questions deserve attention.
At funds where we have deployed AI due diligence workflows, work that took an associate days now takes hours. Quality also improves because a model does not lose concentration on page 400.
For value creation and operating partners, the benefit extends beyond a faster investment decision. A structured view of contracts, systems and operational risks gives the post-close team a better starting point for the value creation plan.
Three workstreams AI handles well
CIM and IM summarisation
Most CIMs follow a familiar sequence: business overview, market opportunity, growth thesis, financials and management team. A model configured around your internal memo template can extract the facts your fund cares about, put them into the screening format and flag unusual points within 10 minutes per CIM.
Speed is only part of the gain. Every opportunity is assessed against the same criteria. The second deal of the week does not receive a weaker review because the associate was fresher for the first.
Data room Q&A
Once the data room opens, a retrieval system connected to the full document set can answer questions in plain English. An associate might ask, "What is the customer concentration, and which contracts are up for renewal in the next 12 months?" The answer should include direct links to the relevant source documents.
This is where generic consultancy demos often fall short. The retrieval layer must return precise information without invented numbers. Citations need to be correct so the team can verify them, and the interface needs to fit the way deal teams already work. In practice, that often means a Slack channel or Notion page rather than another app.
Red flag detection
Contracts, financials and compliance filings contain recurring issues that matter to an investment case: change-of-control clauses, MAC clauses, unusual customer concentration, related-party transactions and the vintage of fixed asset additions. A model can search the full set for these patterns and put the relevant documents in front of a person.
The target is not full automation. By day three of DD, the partner should have a list of issues to investigate that was generated in the first 24 hours, rather than waiting until week two for the manual review to reach them.
What we have seen in practice
On a recent engagement, we built a document AI pipeline to process high volumes of policy documents, contracts and forms. It reached 98% extraction accuracy, cut costs by 65% and moved three FTEs from manual entry into higher-value work. A PE data room uses the same extract, validate and route architecture, although the documents and review criteria differ.
The origination numbers are larger. A multi-LLM classification engine we built for a fund screens 10,000 plus opportunities in 24 hours against its mandate. It operates at roughly 25% of the cost of the existing market alternative and achieves classification accuracy above 95%.
What to watch for
Two mistakes repeatedly undermine AI DD projects.
Treating the tool as a research project
If the workflow sits on a separate laptop and only one associate uses it, adoption will fade. It needs to sit inside the deal team's normal process and become the default route for the work.
Leaving the output vague
"Summarise this CIM" is too loose to produce a reliable investment output. A useful instruction is specific: "Summarise this CIM into our fund's 12-section memo template, flag where information is missing and list the questions we should send before the management meeting."
Specific outputs improve the quality of the work and make it easier for senior reviewers to trust what they receive.
What to build first
Start with one workstream. CIM summarisation is usually the sensible first choice because the inputs are relatively structured and the benefit appears quickly. Once the deal team uses it consistently, add data room Q&A and red flag detection over the following quarter.
Most funds we work with put the first workstream into use within two to four weeks of kick-off. Rolling out all three workstreams usually takes 6 to 12 weeks, sequenced around the live deal pipeline rather than attempted in one release.
Next steps
Squirrel AI is an AI value creation partner to PE funds and their portfolio companies. We go into the portcos and build, rather than advising from the sidelines, and everything we build is aimed at the P&L. We were investors ourselves before this, so we cover the commercial side as well as the technical.
If you sponsor deals, lead value creation or run an Investment Committee, we can identify the highest-ROI starting point in a 30 minute call. We are UK-based, with active engagements in India and the UAE. Our founders previously worked at 3i, Apax, Arcus Infrastructure Partners and Synthesis Capital, so we know the deal process from the inside.
Book a call or read the PE/VC hub at /for-pe-vc.