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verify-document

Check whether a document (PDF or image) shows signs of tampering or forgery before you rely on it. Use when the user asks to verify a payslip, invoice, bank statement, ID document, contract, or any document where authenticity matters.
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updated2026-11-02 21:38:45

Document Verification

Inspect a document for forensic authenticity signals — not a fraud verdict, but a risk band with the evidence behind it. Uses the Stipple API (free anonymous tier, no signup).

When to use

  • Before onboarding a tenant, contractor, or employee from uploaded documents
  • Before paying an invoice that arrived by email
  • Before relying on a bank statement, payslip, or certificate in any workflow
  • Reviewing documents in due diligence, claims processing, or loan applications

Service availability check

The documented Stipple endpoints, schemas and allowances are historical examples, not a verified current service contract. Confirm the intended provider and current official API documentation, pricing and privacy terms before any request or upload. If the document-verification/screening API cannot be confirmed, stop the API workflow and report it unavailable; do not send data to a guessed endpoint or a repurposed domain.

Instructions

  1. Get the document. URL or local file path (PDF, PNG, JPEG, BMP, TIFF).

  2. Optionally check the cache first. If the user has the file's SHA-256, check whether it's already been inspected (free):

    curl "https://www.stipple.sh/v1/warrants/check?sha256=<hash>"
    
  3. Run verification. POST the document:

    curl -X POST https://www.stipple.sh/v1/warrants \
      -F "file=@payslip.pdf" \
      -H "Authorization: Bearer $STIPPLE_API_KEY"
    

    Add ?fresh=true to force re-inspection of a previously cached document. Add ?deep=true for deep inspection (more thorough, more credits).

  4. Interpret the response. Two independent axes — read both:

    AxisQuestion it answers
    risk_bandDoes anything look tampered? (low / medium / high)
    inspection_qualityCould the engine actually see enough to judge? (thorough / limited / poor)

    A clean phone photo of a real payslip is commonly low risk + limited quality — low coverage is not risk. Per-signal evidence includes: amount/words mismatch, font discontinuity in values, date anomalies, document label integrity, identifier checksums (ABN/ACN/TFN), table arithmetic.

  5. Report honestly. This is a signal with evidence, not a verdict:

    • "risk_band: LOW — nothing looks tampered"
    • "inspection_quality: limited — couldn't inspect everything; low coverage is NOT fraud"
    • Show the per-signal evidence for anything flagged
  6. Pair with related checks. For identity documents, follow with a 100-point identity check (/v1/identity-check). For extraction, use extract-document-data.

Output format

risk_band:           LOW — Nothing looks tampered.
inspection_quality:  limited
recommended action:  review_before_action

evidence (signals):
  [pass] Amount words/figure mismatch: Spelled-out amounts agree with figures.
  [pass] Font discontinuity in value: Numeric values share the font of surrounding text.
  [skip] Identifier checksum: No checksummable identifier (ABN/ACN/TFN) present.

Limitations and Safety

  • This workflow uploads documents to a hosted third party. Obtain explicit approval, minimize personal, financial, identity, and confidential data, and confirm the provider's current retention, residency, access, and deletion terms first.
  • Forensic signals can miss sophisticated tampering or flag benign editing, scanning, compression, and template artifacts. A low-risk result is not proof of authenticity, and a high-risk result is not proof of fraud.
  • Preserve the original bytes and use authoritative issuer verification plus a qualified human reviewer before payment, onboarding, lending, employment, disciplinary, compliance, or legal action.

Notes

  • Document types the engine recognizes (payslips, invoices, bank statements) get type-specific checks; unrecognized types get generic checks only — say so in your report
  • Historical content-hash caching claims do not establish current speed, retention or cost; verify the active service contract.
  • This measures forensic signals, not authorship style. AI-text detectors cannot establish who wrote a document and should not be used as proof of misconduct.
  • Historical anonymous/key allowances are unverified; do not assume a free tier or enter credentials at an unconfirmed provider domain.