AI quote extraction

    Convert vendor quote PDFs into structured line items with a deterministic-first pipeline, explicit fallbacks, and evidence-first review — built for procurement workflows that must stay auditable.

    Extraction approach

    Deterministic-first parsing

    Prefer deterministic extraction when the document structure is strong. This keeps outputs explainable, repeatable, and easier to validate.

    • Structured row/column inference
    • Math tolerance checks and sanity constraints
    • Configurable thresholds (per agent/workflow)

    LLM fallback for messy documents

    When deterministic parsing is insufficient (e.g., scans, inconsistent tables), fall back to LLM extraction with budget guards and explicit uncertainty.

    • Fallback is explicit, not silent
    • Uncertainty routes to review
    • Hard limits to avoid runaway OCR/vision costs

    Structured line items for downstream workflows

    Output line items in a structured form so teams can compare quotes and prepare award-ready packs without rekeying.

    • Line item rows (qty, unit, price, totals)
    • Header/metadata extraction (terms, dates, vendor)
    • Evidence is preserved and linkable

    Controls and auditability

    Evidence-first review

    Extraction is useful only if it stays reviewable. Keep evidence attached so reviewers can validate outputs quickly and consistently.

    • Review queues for exceptions
    • Diff-friendly drafts (draft → reviewed → approved)
    • No silent overwrite of source documents

    Fail-closed for commitments

    Quote extraction can run fast, but money and commitments must remain controlled: approvals, policies, and audit logs.

    • Approvals for award decisions and PO issuance
    • Explicit autonomy per action (assist/execute/approve)
    • Tenant-safe execution with traceability

    Safe reruns

    Rerun safely when inputs change. Make retries and reprocessing deterministic and auditable rather than manual guesswork.

    • Retry without duplicating records
    • Idempotent processing patterns where applicable
    • Audit trail of what ran and when

    FAQ

    Clear answers for teams evaluating governance and runtime design.

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