How Private Equity Firms Use AI in Due Diligence

Private equity firms use AI at every stage of a deal: to screen targets, read the CIM and the data room, support financial and quality-of-earnings work, draft the investment committee memo, and monitor portfolio companies after close. Kepler, which we build for deal teams, runs that whole cycle on one model of your firm: every file in the data room read in full, your firm’s definitions applied at every stage, and every figure computed in code and cited to its page. AI saves the most time on reading and organizing large document sets, and it fails where a tool samples the data room, misses a clause buried in a schedule, or writes a number itself instead of pulling it from a source.

Each stage below notes what that looks like on Kepler, and the last section shows how we run the whole cycle.

Adoption is broad. In Bain’s 2025 M&A report, more than 60% of the private equity firms Bain interviewed were using at least one AI tool to improve sourcing, screening or diligence. A Deloitte survey of 1,000 US corporate and private equity leaders, published in October 2025, found 86% had integrated generative AI into their M&A workflows. And in the Bain and StepStone GP outlook for 2026, GPs most often named due diligence and deal sourcing as where generative AI delivers the highest return inside the firm.

This guide walks the deal cycle stage by stage, then covers what breaks and how to test a tool before it touches a live deal. For a ranked list of products, see the best AI tools for private equity due diligence.

Where AI fits in the deal cycle

Stage	What AI does	What to check	On Kepler
Sourcing and screening	Builds target lists, profiles companies, matches theses	Whether private-company facts carry a source	Every target checked against your firm’s past deals and notes
CIM and teaser review	Summarizes the business, pulls KPIs, drafts first questions	Every figure against the CIM page it came from	Every figure pulled with its CIM page; adjusted EBITDA recomputed on your definition
Data room review	Reads contracts, financials and corporate records; finds clauses and exceptions	Whether every document was read, or a sample	Every file read at ingest; complete lists with clause text
Financial diligence and QoE support	Bridges EBITDA, tests add-backs, analyzes revenue and working capital	Formulas, inputs and your firm’s definitions	EBITDA bridge, cohorts and working capital computed in code, formula shown
Commercial and legal diligence	Synthesizes customer and market work; reviews contracts and litigation	Complete lists of clauses, with the clause text	Every clause returned with its text and page
IC memo	Drafts the memo in house style from the work above	Every number cited; conflicts between sources shown	Drafted in your template, every number cited, conflicts shown side by side
100-day plan and monitoring	Tracks KPIs, reads monthly packs, flags variances	Definitions match the deal model; alerts carry sources	Same ontology and definitions carry into monitoring; agents read each pack

AI use cases in private equity, stage by stage

Sourcing and screening

Deal teams use AI to turn a thesis into a target list: companies in a niche, by size and ownership, with a short profile of each. Bain describes Hg using generative AI to help its portfolio companies’ management teams mine a large database of companies for sales prospects and M&A targets, and estimated in 2024 that generative AI can cut screening time per company from a day to an hour. The useful test is whether each fact in a profile (revenue range, headcount, owner, last round) links to where it came from. A screening tool that also knows your firm’s history, which companies you’ve looked at, why you passed and who met management, saves the most time, because the first question on any new name is “have we seen this before?” On Kepler, that history sits in your firm’s ontology, so every new name arrives with the firm’s earlier work on it attached.

CIM and teaser review

The teaser is a one- or two-page synopsis; the CIM is the seller’s full marketing document, often 50 pages or more. AI gives an associate a fast first read: the business model, segment and customer mix, the KPIs the banker chose to show, and the adjustments behind adjusted EBITDA. The risk is the numbers. A model that summarizes a CIM can transpose a figure, mix fiscal and calendar years or round a margin, and each error flows into the screening model. Ask for every figure with its page, and spot-check ten. On Kepler, every CIM figure arrives with its page, and the banker’s adjusted EBITDA sits beside the same figure on your firm’s definition, with each adjustment cited. Our guide to reviewing a CIM with AI covers what to extract and the red flags to screen for.

Data room review

This is where AI saves the most time and where tools differ most. A data room holds the second-round diligence material: customer and supplier lists, labor and purchase contracts, debt terms, leases and pension contracts, alongside board minutes, monthly management accounts, tax filings and scanned schedules. Typical questions:

• Every customer contract with a change-of-control, anti-assignment or termination-for-convenience clause.
• Every supplier agreement with exclusivity or minimum purchase commitments.
• Litigation, regulatory correspondence and related-party transactions mentioned anywhere in the room.
• Revenue by customer, reconciled to the management accounts.

The first three are list questions. A tool that retrieves the top passages for a query returns the closest matches, which is fine for a summary and wrong for a list. Before you trust a list, ask how the tool read the room: every page of every file, including scans and spreadsheet tabs, or a sample chosen by a search step. On Kepler, extraction models we train read every file at ingest, scans and spreadsheet tabs included, and index each clause, so a list question returns every match with its page.

Financial diligence and quality of earnings support

The accounting firm’s quality of earnings report remains the authority on earnings quality. A sell-side QoE sets out the seller’s add-backs to adjusted EBITDA and the net working capital the business needs. AI helps the deal team keep up with it and test it: rebuilding the EBITDA bridge from the management accounts, tagging each add-back by type, checking whether a “one-time” cost recurs every year, analyzing customer cohorts and concentration, and tracing working capital by month to set the peg. These are calculations, so they should run as code over sourced inputs, with the formula visible. On Kepler, each calculated cell opens to its formula and the page behind every input, so the deal team can walk its bridge against the QoE line by line.

Commercial and legal diligence

Commercial work leans on customer calls, market studies and expert networks; AI helps synthesize interview notes and compare management’s market claims with third-party data. Legal diligence is contract review at scale, and legal AI tools such as Harvey are built for the law firms that run it. The deal team still needs the findings in its own terms: which issues change the price, the structure or the 100-day plan. On Kepler, call notes and counsel’s findings sit in the same ontology as the financials, so a problem clause links straight to the customer and the revenue it puts at risk.

The investment committee memo

The IC memo pulls everything together in the firm’s format: thesis, business overview, financials, returns, risks and mitigants, and diligence findings. AI drafts it well when it has three things: your template and past memos, the analysis above, and a citation for every number. The most useful single feature at IC is a conflict flag: when the CIM, the management accounts and the QoE give three different EBITDA figures, the memo should show all three and say which one the model uses. On Kepler, all three figures appear with their sources and the conflict is flagged before the memo reaches IC. Our guide to writing an IC memo with AI covers the structure and what partners should still write themselves.

The 100-day plan and portfolio monitoring

After close the documents keep arriving, now as monthly reporting packs, board decks and lender compliance certificates. A workable value creation plan starts with a 100-day plan that has clear owners and KPIs. AI reads each pack, tracks those KPIs and flags variances. That works only if the KPI definitions match the ones in the deal model, so the work done in diligence should carry straight into monitoring. On Kepler, agents read each monthly pack as it lands and track the 100-day KPIs on the definitions the deal team used. For the tools built for this stage, see our list of AI portfolio monitoring tools.

What breaks: the risks of AI in due diligence

Most AI failures in diligence come from a handful of causes, and each one has a test.

Sampling. Most AI tools answer by retrieving the passages that best match a query and reasoning over those. That works for “what does the CIM say about pricing?” It fails for “every contract with a change-of-control clause”, because the answer is a list and retrieval returns the top matches. The fix is reading every document at ingest and indexing what it says, so a list question becomes a lookup over facts already extracted.

Missed clauses. The terms that change a deal hide in schedules, amendments, side agreements and scanned exhibits. A tool that skips images, can’t read a scanned page or treats an amendment as unrelated to the agreement it changes will miss them. Ask how the tool handles scans, spreadsheet tabs and amendment chains.

Numbers the model wrote. A language model generates text, digits included. It can transpose a figure, mix up fiscal periods, drop a scale (“in thousands”) or do arithmetic in its head. On FinanceBench, a benchmark of questions about public-company filings, GPT-4-Turbo with a retrieval system answered incorrectly or refused 81% of questions in 2023. Models have improved a great deal since, and on the Vals AI Finance Agent benchmark of entry-level analyst tasks, the top-ranked model scored 67.85% as of October 2026. Every number that reaches an IC memo should be pulled from a source and, if derived, computed in code with its formula shown.

Whose definition. Adjusted EBITDA in the CIM, in the QoE and in the credit agreement are three different numbers, and your firm has a fourth. A general model doesn’t know which one you mean unless you tell it each time. The firm’s definitions should live in the system and apply automatically. Our guide to what an ontology is explains how firms encode them.

Sources that disagree. The CIM says one thing, the management accounts another, and the data room has two versions of the same customer schedule. A tool that silently picks one leaves the deal team defending a number it never saw challenged. Look for one that shows both values with sources and flags the conflict.

Data that moves. Sellers add and replace files throughout a process, so last week’s answer may rest on a superseded version. The tool should know which version each fact came from and re-read new uploads.

Confidentiality and injected instructions. Data rooms come under NDA, and clean-team rules can restrict who sees what. A seller-prepared document can also contain text that tries to steer an AI tool. Check that the tool enforces deal walls on every read, logs every query, and never treats document text as an instruction.

Legal research shows how costly unchecked output can be: a Stanford study found leading legal research AI tools hallucinated between 17% and 33% of the time, and in Mata v. Avianca a federal judge fined lawyers $5,000 for a brief citing cases that didn’t exist.

How to test an AI tool before a live deal

1. Run it on a closed deal. You already know what the data room contained and what diligence found. Compare the tool’s lists against yours.
2. Ask a list question and count. “Every contract with a change-of-control clause.” If you know there are 23 and it returns 10, the tool samples.
3. Click ten numbers. Each should land on the page, line or cell it came from, showing the period and units.
4. Ask twice. The same question on the same documents should give the same number.
5. Test your definitions. Ask for adjusted EBITDA your way, and for a portfolio company by its internal code name.
6. Plant a conflict. Put two versions of the same figure in the test set and see whether the tool notices.
7. Check the walls. Confirm that people and agents see only the deals they’re cleared for, and that every query is logged.

Our guide on how to check AI-generated numbers goes deeper on the citation and arithmetic checks.

How Kepler does this

We built Kepler so each stage above runs on one foundation, from the first screen to the last monitoring report.

Before the CIM arrives. Our engineers build your firm’s ontology from your own deal history: past IC memos, models, CRM notes, portfolio reporting and the definitions your partners use. Every company, executive, lender, adviser and document resolves to one record, so a new target is checked against everything the firm already knows about it, its competitors and its management. Public data is loaded on day one, including 26M+ SEC filings and 40M+ investor relations documents and earnings call transcripts, so public comparables for 14,000+ companies come with every multiple traced to a line item.

When the data room opens. It connects read-only, and extraction models we train read every file at ingest: contracts, scanned schedules, board minutes and every tab of every workbook. Each fact, party, date and clause is filed beside its passage. A question such as “every customer contract with a most-favored-customer or price-protection term” returns the full set with the clause text. Agents re-read each new upload overnight with the same access as the person who set them up, ignore any instruction buried in a seller’s document, and report what changed with the exact quote.

Financial diligence and the memo. Code computes every figure: the EBITDA bridge on your definition, customer concentration, cohort retention, working capital by month. Each calculated cell opens to its formula and the page, line or cell behind every input, and a citation that doesn’t match its source is stripped while the answer is written. When the CIM, the management accounts and the QoE disagree, Kepler shows all three and flags the gap. Your IC template, diligence checklists and house style live in Kepler as the firm’s playbook, so the memo fills in Word with every figure cited and the model lands in Excel.

After close. The deal’s ontology becomes the portfolio company’s. Monthly packs are read as they arrive, KPIs on your definitions are tracked against the 100-day plan, and alerts carry the exact quote and source.

Wherever the deal team works. The same answers reach Excel, PowerPoint, Word, Claude, ChatGPT and Slack, and your own systems through the Kepler API. Each task goes to the model best suited to it: compact models we train read the room, frontier models reason over it and draft the memo, and we swap in better ones as they ship, so the deal team never has to chase model releases.

Throughout, walls between deals, one permission check on every read and a log of every query and calculation keep the work defensible (security). Our forward-deployed engineers built this layer before at Palantir. In the first week they connect your sources and build your ontology alongside your deal team.

Questions deal teams ask Kepler

• “Have we looked at this company or any of its competitors before? Show the IC memos and why we passed.”
• “Every supplier agreement in the data room with exclusivity or a minimum purchase commitment, with the clause text.”
• “Compare EBITDA in the CIM, the management accounts and the QoE for each of the last three years, and show where they differ.”

Open app.kepler.ai to try Kepler on public filings, or request a demo to watch it work through a data room. For how other tools compare, see Rogo alternatives and our Compare page.

Frequently asked questions

How do private equity firms use AI in due diligence?

Across the deal cycle: building and screening target lists, summarizing CIMs, reviewing data-room contracts for change-of-control and other clauses, supporting quality-of-earnings work with EBITDA bridges and cohort analysis, drafting the IC memo in house style, and tracking KPIs against the 100-day plan after close. Kepler, which we build, runs every stage on one model of the firm, with every data-room file read in full and every figure computed in code and cited to its page. The biggest time savings come from reading large document sets, and the biggest risks come from sampled documents and numbers a model wrote itself.

Can AI replace a quality of earnings report?

No. The QoE comes from an accounting firm that tests the numbers against the books and stands behind its findings. AI helps the deal team rebuild the EBITDA bridge, tag add-backs, test working capital and find issues to raise with the QoE provider, faster and on the firm's own definitions. Every figure it produces should be computed from cited inputs so it can be checked against the QoE line by line. On Kepler, each bridge figure opens to its formula and the page behind every input.

What are the risks of using AI for due diligence?

The main ones are incomplete reading (retrieval returns the closest passages, so lists of clauses come back short), numbers the model generated instead of pulling from a source, the wrong definition of a metric, conflicting sources resolved silently, stale versions of data-room files, and confidentiality. Test any tool on a closed deal, count what it misses, and click through its citations.

Is ChatGPT good enough for private equity due diligence?

It's useful for a first read of a CIM, drafting questions and structuring a memo, and ChatGPT for Financial Services adds licensed data and connectors. For figures that reach an investment committee, a frontier model on its own still writes the numbers itself and doesn't know your firm's definitions or past deals. Kepler runs inside ChatGPT and Claude and computes and cites those figures from the data room.

Which AI tools do private equity firms use for due diligence?

Common choices include Hebbia and V7 Go for document analysis, Datasite's Blueflame AI inside Datasite rooms, Rogo for banker-style output, AlphaSense for expert calls and research, Harvey for legal review, and Claude or ChatGPT as general assistants. Kepler is built for firms that want one model of their own deals and data with every number computed and cited. Our list of the best AI tools for PE due diligence compares them.