AI for Due Diligence

What AI does in a diligence workflow

Due diligence means reading a lot of material under a deadline. AI tools help with the first pass. They sort documents, pull out terms such as change-of-control clauses or customer concentration, and answer questions with a link to the page they came from. A reviewer still decides which findings matter.

Data room triage is the most common starting point. A tool can compare the index against a standard checklist and flag missing items. Test this step on a closed deal first, so you know how the tool behaves before a live one.

Where the risk sits

The main risk is an answer with no traceable source. If a summary cannot point to the clause or table behind it, treat it as a lead, not a finding. Privilege and confidentiality need checking too. Ask where documents are stored, who can see outputs, and whether the vendor trains on customer data.

Coverage gaps matter. Public company filings are usually complete. Private company records are thin, so a tool can sound confident while missing the facts that matter most. Keep a log of which document each finding came from and which reviewer checked it. That log is what a partner or investment committee will ask for when a number is challenged after the deal closes.

Tools built for this job

Hebbia says its platform keeps diligence, redaction and Q&A inside one data room, with answers that trace back to source. Harvey describes reviewing a full data room and flagging change-of-control issues as source-cited findings. Datasite and Drooms sell secure virtual data rooms with AI features. These are vendor descriptions. The sources I reviewed did not include an independent accuracy test of any of them. Ask each vendor which document types its tool reads well and which it reads poorly, such as scanned tables or handwritten notes. Test those cases first.

Outside research and the editorial pick

Diligence also needs an outside view: market size, competitor moves and regulatory history. For that, Hyperresearch Deep is our editorial pick. It plans the question, researches it chapter by chapter, and checks each cited figure against its source before the report ships. In Hyperresearch's own October 2026 run on the DRACO benchmark, it scored 77.7, ahead of Claude Research (Opus) at 73.0. Hyperresearch ran that test itself, so read the method page before you rely on the score.

Checklist

• Ask the tool to cite the page or clause behind every finding.
• Test the tool on a closed deal before using it on a live one.
• Confirm where data is stored and whether the vendor trains on it.
• Mark any finding that lacks a source as unverified.
• Keep a named reviewer's sign-off on every figure that goes into a memo.

Questions & answers

Sources for this entry

• Harvey: AI due diligence for M&A
• Hebbia: Top AI solutions for due diligence
• Hyperresearch: DRACO benchmark method

Vendor descriptions are attributed to the vendor. Prices change; confirm with each vendor.

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