Release Faster, Miss Less: Verified Extraction for CoAs, CoCs, and Quality Packets

Quality and supply chain teams don’t read documents for fun. They read them because the business depends on:

• lot traceability
• spec conformance
• expiration control
• release authorization
• audit readiness

Certificates of Analysis (CoAs), Certificates of Conformance (CoCs), inspection reports, and batch documents are high-volume and high-stakes.

The problem isn’t that the data is unavailable. It’s that verification is slow and error-prone:

• lot numbers are copied incorrectly,
• units get misread,
• tolerance ranges are missed,
• and auditors ask “show me where that was stated.”

Citation-backed extraction is high value here because quality decisions require proof, not just values.

The quality packet reality

A “release decision” often depends on a packet, not a single PDF:

• CoA (test results)
• CoC (conformance statement)
• purchase order / spec sheet
• receiving inspection record
• supplier change notices (sometimes)
• MSDS/SDS (sometimes)
• batch/lot history (internal)

If these aren’t reconciled, you get holds, rework, and occasionally a costly recall risk.

What to extract (high-impact fields)

Traceability

• supplier name
• material/product name
• part number / SKU
• lot/batch number (critical)
• manufacturing date (if present)
• expiration / retest date (critical)
• quantity shipped

Test results (CoA)

Per analyte/test:

• test name
• result value
• unit
• specification limit(s): min/max or range
• pass/fail (if stated)

Conformance & standards (CoC)

• standards referenced (e.g., ISO, ASTM)
• statement of conformance language
• revision/version of spec (if present)

Exceptions and notes

• deviations
• “informational only” disclaimers
• test method notes

These “notes” are where bad surprises hide. Citations help reviewers see the exact disclaimer language quickly.

Why citations matter in quality workflows

Quality isn’t just “what’s the result?” It’s “what’s the result and what does the document actually say?”

Citations let you:

• click a test result and highlight it in the CoA,
• verify the units and spec range,
• and capture evidence for release sign-off.

That reduces both human keying errors and the time spent proving decisions during audits.

Practical workflow: CoA automation with verification

1) Ingest the supplier packet

Attach CoA/CoC to PO line items, internal item master references (where available), and receiving records.

2) Extract test tables with evidence

CoAs are table-heavy. The output should include:

• row-level extracted values,
• citations for each row (or each critical cell).

3) Normalize and validate

Normalize:

• units (mg/L vs ppm; % vs fraction)
• decimal formats
• scientific notation
• “<LOD” style results

Then validate:

• compare results to spec ranges
• flag out-of-spec or missing tests
• flag mismatched lot numbers across documents

4) Route exceptions to QA review

When something fails, show:

• the test row highlighted,
• the spec row highlighted (if in a spec sheet),
• and the mismatch clearly.

5) Store release record + evidence

Store:

• extracted results
• citations
• reviewer approval / rejection
• timestamp and signatory

Now audits become: “click and show,” not “dig and hope.”

Schema sketch: CoA extraction

{
  "schema": {
    "supplier_name": { "type": "string" },
    "product_name": { "type": "string" },
    "part_number": { "type": "string" },
    "lot_number": { "type": "string" },
    "manufacture_date": { "type": "date" },
    "expiration_date": { "type": "date" },
    "tests": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "test_name": { "type": "string" },
          "result_value": { "type": "string", "description": "Keep as string to preserve <, ND, LOD formats" },
          "unit": { "type": "string" },
          "spec_min": { "type": "string" },
          "spec_max": { "type": "string" },
          "pass_fail": { "type": "string" }
        }
      }
    },
    "conformance_statement": { "type": "string" }
  },
  "options": { "confidence_threshold": 0.85 }
}

Note the choice to keep some numeric-like fields as strings. In quality docs, “<0.01” is semantically important and easily lost if forced into a float.

What to measure

• time-to-release per lot
• % of packets auto-cleared (no exceptions)
• exception review time
• rate of “data entry corrections” found in QA
• audit request response time

Quality teams don’t want AI summaries. They want faster, provable conformance checks.

Evidence-backed extraction turns CoAs into structured, auditable release inputs.