Workflow
    Procurement
    AI

    Quote extraction workflow

    A production-safe workflow for converting vendor quote PDFs into structured line items without losing auditability. Deterministic-first, explicit fallbacks, review queues, and approval gating for commitments.

    1

    Ingest quote documents

    Store vendor quote PDFs as evidence and link them to the RFQ or vendor quote record. Evidence should be immutable and referenced by downstream drafts.

    2

    Deterministic-first extraction

    Prefer deterministic extraction when structure is strong. Validate totals and enforce minimum structure checks before accepting outputs.

    3

    Explicit fallback for messy PDFs

    When deterministic parsing fails (scans, inconsistent tables), fall back explicitly and label uncertainty. Budget guards prevent runaway OCR/vision usage.

    4

    Review queue for exceptions

    Route low-confidence fields, missing units, and ambiguous lines to a review queue. Review should show evidence next to extracted fields for fast validation.

    5

    Quote comparison (like-for-like)

    Use structured line items to compare quotes across vendors. Surface mismatches and missing evidence without forcing spreadsheet re-entry.

    6

    Approval-gated commitments

    Award decisions and PO issuance are commitments. Keep these behind explicit approvals with an audit trail tied to an action identifier (ai_task_id).

    Design checks

    Use these checks to spot fragile “AI demo” designs before they hit production.

    • Can operators re-run extraction without duplicating records?
    • Is uncertainty explicit, and does it route to review?
    • Do approvals cover commitments (award, PO issuance, payments)?
    • Is evidence attached at the point of decision (not just stored somewhere)?

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