AI for due diligence: How private equity analysts summarize data rooms in minutes
A new data room has been submitted, and an analyst begins by making sense of it. There are three versions of the same P&L. Excel documents have more #REF cells than answers.
This is where AI due diligence comes into play: classifying documents, extracting key information, surfacing red flags, and generating investment insights in minutes, freeing the analyst to focus on higher-leverage work.
Today, we’ll explain how AI summarizes entire data rooms in minutes, which capabilities matter most to private equity firms, and how modern deal teams use AI for investment due diligence to accelerate their decision-making timelines with greater conviction.
Why traditional due diligence slows down deals
Traditionally, PE analysts execute the same tedious, laborious tasks when beginning due diligence on a new opportunity.
They’re slowed down by:
- Volume of data: Data rooms include financial statements, legal contracts, SOPs, audit reports, compliance documents, SOC reports, internal memos, and more. Analysts first need to identify what’s necessary for their analysis and which documents can be left out.
- Document fragmentation: Diligence spans multiple categories—financial, legal, commercial, and technology—each in its own folders and formats. Parsing these documents manually is time-consuming and low-value work.
- Delayed time-to-conviction: Due to these highly manual administrative tasks, decisions take longer to reach a conclusion. In the meantime, sellers are in early conversations with other buyers, and their banker is on the phone, pressing you for an update.
These headwinds create serious friction in an analyst’s early diligence — before even moving on to deeper analysis and a potential term sheet.
What AI due diligence really means in private equity
AI due diligence software is a purpose-built, vertically trained system designed to excel at the same tasks analysts and associates perform every day, particularly in complex financial analysis.
Behind the scenes, a coordinator agent breaks a question prompt into component tasks: retrieving files from the data room, extracting structured data, reconciling conflicting versions, interpreting contracts, understanding spreadsheet logic, performing calculations, and synthesizing insights. Specialized sub-agents then execute each task using tools designed specifically for messy, unstructured finance data.
This includes:
- Proprietary spreadsheet-understanding encoders that compress and normalize large Excel models for cell-level extraction
- Extraction engines that pull quantitative and qualitative signals from PDFs with audit-grade traceability
- Retrieval systems that assemble all relevant data points across the data room for each analytical task.
- Summarization and analysis layers generate financial trends, customer KPIs, contract obligations, operational risks, and early-stage investment narratives — each tied back to exact source pages or cells.
The result is a system that converts fragmented, unstructured data rooms into reliable, structured insights — so analysts can validate outputs rather than manually assemble them.
How AI summarizes data rooms for PE firms
The right AI due diligence platform is built to augment your analyst’s capabilities, freeing up their time to focus on higher-value tasks. It serves as a sidekick, performing rich financial analysis and synthesizing qualitative context to ensure that opportunities align with your investment thesis and risk appetite.
Here’s what you can expect through the lifecycle of a deal review:
Step 1: Ingesting and organizing the data room
When a new data room is imported into your AI workspace, your agents begin ingesting entire folder hierarchies across nested data room structures and automatically classify documents based on their content and context.
The platform detects file types, groups related documents, and resolves any duplicative versioning issues. It distinguishes between:
- Historical three-statement financials, forecasts, and other relevant financial documents
- Customer contracts and vendor contracts
- HR files and compliance policies
- Litigation files and general legal documents
- Operational SOPs vs. financial audits
- Target company debt schedule
Your agents will also detect missing files by comparing the uploaded documents against expected diligence checklists — for example, identifying absent customer lists, missing tax returns, or missing monthly reporting packets before analysis begins.
An analyst can immediately begin chatting with their AI agent in natural language, prompting about the status and contents of the data room.
For example, “What materials am I missing to create an LBO model?”
Step 2: Extracting and structuring the information
Once the entire data room is classified and organized, agents begin extracting quantitative and qualitative information from the dataset across document types, including spreadsheets, PDFs, presentation decks, and more.
Financial data is converted into investor-ready insights
An advanced spreadsheet understanding layer parses large financial statements with near-perfect precision, turning messy data into structured financial insights across:
- Multi-year margin trends
- Cash conversion patterns
- Seasonality analysis
- Working capital changes
- EBITDA adjustments
Extraction agents also flag unmatched line items or updated values across any document format.
Customer data is surfaced and synthesized across documents
Task agents retrieve and extract key quantitative and qualitative context, cite it precisely, and make it ready for review and interpretation by the analyst. For example, an analyst can ask the agent to pull reports together that identify:
- Top customers
- Concentration risk
- Churn
- Retention
- Pricing changes
Contract terms are identified and interpreted
Contracts are often written in various formats, making it difficult for analysts to quickly scan and identify key clauses.
Not only can the right AI platform identify critical contract terms, but it can also provide citations back to the exact clause or footnote where the information was sourced. F2's Audit Mode provides expert-level auditability throughout the memo on terms such as:
- Renewal windows
- Termination rights
- Indemnity
- Escalation clauses
- Payment terms
- Dependency risk
Step 3: Automated risk identification and ranking
The platform systematically detects (and cites) risks — both quantitative and qualitative — that analysts are paying attention to:
- Sudden margin compression
- Increasing customer churn
- Litigation exposure
- Key-person dependency
- External macro factors
Step 4: Generating screeners and IC materials
After the data room has been fully normalized and analyzed, the platform creates review-ready investment materials, including complete analysis across necessary summaries and calculations:
- Business overview
- Financial summary
- Customer analysis
- Contract obligations
- HR insights
- Early risks and mitigants
Analysts can regenerate or rewrite specific memo sections — turning summaries into tables, restructuring narratives, or adjusting tone — using simple natural-language prompts.
Step 5: Enabling team collaboration and review
When investment materials are ready to share, team members can review and collaborate on the documents in real time, audit each output, revise sections, and ask the agent to investigate certain claims or perform additional financial analysis.
How PE firms should choose an AI due diligence software
Your AI due diligence software needs to be custom-built for private equity workflows and prioritize the features that matter for your team.
Factors such as robust financial acumen, accuracy in processing large context windows, and rigorous security measures are necessary for platform partners operating in highly sensitive industries.
Look for platforms that:
Handle massive, unstructured data rooms at scale
Data rooms can contain thousands of pages spanning different formats, and a trustworthy platform must be able to parse deeply nested folder structures, resolve version conflicts, and identify missing materials needed for accurate analysis.
Analyze granular contract terms
Hidden contractual terms can turn into a disaster for an acquiring firm. The right AI due diligence software must extract key contract terms with precise auditability.
Map customer-level data into KPIs
An investment team must consider nuanced customer-level activity data and its impact on the business’s viability. These metrics are often scattered across the data room, making it important for an AI platform to normalize and cross-reference data from different sources in its analysis.
Integrate with Excel and IC workflows
Arguably, the most important factor in an AI due diligence platform is its financial analysis capabilities, including integration with large Excel workbooks and cross-workbook analysis.
Provide auditability and versioning
To avoid any doubt about hallucinations, the AI platform you use should provide complete traceability for every output it generates. The more precise the better — as paragraph, or even, footnote-specific citations enable you to confirm the accuracy of its analysis quickly, so you can begin analyzing the information knowing its factual accuracy.
Prioritize data security and privacy
Your partnership with the right AI platform relies on trust, which is critical in handling sensitive information. Look for platforms that enforce strict safeguards, such as zero-day retention agreements with their LLM providers, maintain SOC/ISO accreditation, and are willing to complete robust DDQs upon request from a prospect.
Next steps for PE firms adopting AI due diligence
If you’re ready to unlock greater efficiency in your due diligence process, here’s how you can get started:
- Audit the current diligence process: Analysts know where they spend most of their time. Document the top time-consuming tasks and which responsibilities are most error-prone or require templated work.
- Identify 2–3 opportunities that can be AI-assisted: Choose the tasks with the highest time requirements and lowest leverage. If the bottleneck is administrative or monotonous and doesn’t require nuanced human judgment, it’s a perfect candidate to test out on an AI platform.
- Book a demo with F2: Let our team walk you through the exact workflow you can use to automate your analysis when the next opportunity comes in.
- Share your existing outputs: Our team will ask for past examples of IC memos, presentation decks, and any other deal materials you’ve created for past deals. We’ll train your workspace to replicate your firm’s preferred formatting, so all outputs map to your existing workflows.
- Roll out across your team and measure workflow improvements: Once you test the AI platform on an initial deal, you’ll see how others have accelerated their deal reviews by up to 75% with better accuracy and polished IC materials.
Conclusion
AI due diligence software enables analysts to focus on evaluating businesses rather than searching through PDFs.
Firms adopting AI in their due diligence workflows today find a faster path to decision-making, spot risks earlier, and win more competitive processes.