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Corporate Finance & M&A · Intelligent Documents and Email

AI applied to the data room: a structured document due diligence, not a page-by-page re-read

For corporate finance and M&A teams that must extract the essentials from hundreds of documents in little time, without missing anything important.

Discuss a data room

What happens today

  • Data rooms contain hundreds of heterogeneous documents.
  • Extraction of clauses, figures and deadlines is manual.
  • Comparisons between documents and between targets take a long time.
  • The review report is rebuilt for every deal.

The workflow

  1. 01 · Inputs

    • Data room documents
    • Review checklists
    • Questions from the team

  2. 02 · Processing

    • Indexing and classification of documents
    • Extraction of key clauses, amounts and dates
    • Sourced answers to the checklist questions
    • Flagging of inconsistencies and gaps

  3. 03 · Human checks

    • Every answer cites its source
    • Sensitive points sent for senior review
    • No automatic conclusion

  4. 04 · Outputs & actions

    • Structured summary by theme
    • Register of points of attention
    • List of missing documents
    • Review reporting

What changes

Today

With the system

Exhaustive manual reading.

Structured extraction, targeted reading by the team.

Inconsistencies detected late.

Inconsistencies flagged at indexing.

Reporting rebuilt for every deal.

Reporting generated from the same structure.

Example

The client contracts of a data room are indexed; the system lists the change-of-control clauses with their source and flags two contracts with no identifiable clause for review.

Illustrative example of how the process can work.

Intelligent Documents and Email

Incoming emails and documents → structured data and created tasks

Read more

What drives the economics of the project

No savings are guaranteed. The variables below are the ones we measure in Discover.

01

Number of deals per year and documents per deal

02

Current review time per deal

03

Share of checklist questions answerable by extraction

04

Confidentiality requirements

Any estimate shows its assumptions: current senior and junior time, hourly cost, automatable perimeter.

Implementation

  1. 01

    Discover

    Test on a past, anonymised data room, definition of the checklist.

  2. 02

    Data & access

    Secure environment, access to documents.

  3. 03

    Build

    Indexing, extraction, sourced answers, reporting.

  4. 04

    UAT

    The team compares the generated summary with its manual review.

  5. 05

    Go-live & RUN

    Use on subsequent deals, monitoring, adjustment.

Security and privacy

Maximum confidentiality: dedicated environment, named access, logs, providers and regions documented and validated with you before any processing of real documents.

Frequently asked questions

Do documents leave our environment?

It depends on the architecture agreed with you. We document every flow before production.

Does the system draw conclusions?

No. It extracts, cites and flags; the analysis stays with the team.

Compatible with virtual data rooms?

Depending on the exports and access available. Confirmed after assessment.

How long?

Estimate after Discover.

Let's discuss a data room

Describe your checklists and your volumes. We'll tell you what can be structured and where senior review remains essential.

Discuss a data room