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Diligence at machine speed

An AI-powered due diligence case study. A diligence system that reads hundreds of data-room documents per deal, organizes first-pass analysis across 17 risk categories, and surfaces potentially deal-breaking risks.

Client
$135B AUM private equity fund
Scope
First-pass diligence review
Outcome
Weeks → minutes
Status
Under NDA

Published readout

Measured outcomes

  • MinutesFirst-pass review timeFrom weeks of analyst work
  • 17Risk categories structuredPer deal
  • 100sDocuments readPer data room

Context

Hundreds of documents against the deal clock

The fund reviewed hundreds of documents per deal under tight deadlines. Risk signals were scattered across governance filings, contracts, litigation records, and financials.

A missed material issue could affect the value of a transaction. The fund needed faster first-pass analysis to support analyst judgment, with every claim traceable to its source.

Delivered

Scope completed

  1. 01 · Ingestion

    Full data-room intake

    Hundreds of diligence documents per deal structured for analysis.

    Delivered

  2. 02 · Analysis

    17-category risk framework

    Deal-risk analysis organized for review by analysts and the investment committee.

    Delivered

  3. 03 · Output

    Deal-risk matrix

    The system ranked deal-breaking issues, outlined ways to address them and drafted a recommendation for investment committee review, with conditions to meet before proceeding.

    Delivered

  4. 04 · Controls

    Source citations + red-team mode

    Every output cites its source documents. Red-team mode challenges the analysis's findings and assumptions.

    Delivered

Deployment: inside the fund's environment

The system addressed this fund's document-review bottleneck using its own documents inside its environment.

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