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Document analysis and extraction

Pull named fields out of contracts, invoices or reports into a table with verbatim evidence, locations and confidence, plus a list of red flags.

You'll need: The document as text or an uploaded PDF · A list of the fields you need
  1. 1List the fields you need

    Write one field per line with a short definition, for example 'Notice period: days either party must give before terminating'. Definitions stop the model guessing which clause you mean.

3 steps and 1 ready-to-use prompts are in the full version

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Example input

Document type: software services agreement (14 pages). Fields: Term, Auto-renewal, Notice period, Liability cap, Governing law, Data processing terms.

Example output

| Field | Value | Verbatim evidence | Location | Confidence | | Term | 24 months | "an initial term of twenty-four (24) months" | §9.1 | High | | Auto-renewal | Yes, 12-month periods | "shall renew automatically for successive twelve-month periods" | §9.2 | High | | Notice period | 90 days before renewal | "no less than ninety (90) days prior" | §9.2 | High | | Liability cap | NOT FOUND | | | | Red flags - §12.4 excludes liability for data loss "howsoever caused", which may undercut the security commitments in Schedule 2. Missing - No overall liability cap is stated.

Troubleshooting

  • Values look plausible but no evidence quote is given: the model may be inferring. Re-run with 'Leave the row as NOT FOUND unless you can quote it.'
  • Page numbers are wrong on uploaded PDFs: ask for section or clause numbers instead, which survive text extraction better.
  • Tables in the document are garbled: paste that table separately or convert the PDF to text first.

Known limitations

  • This is not legal or financial advice; a qualified person should review anything you rely on.
  • OCR quality on scanned documents limits accuracy, and handwriting is often missed.
  • Do not upload confidential documents to a tool your organisation has not approved.

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