GMP / GLP Documentation Quality Review

Quality records should be ready before someone else finds the gaps.

MJM QAAI applies regulated-environment documentation discipline to calibration packages, validation support records, vendor documentation, SOPs, and turnover files. AI assists the inspection; qualified human review controls release.

View calibration case study Inspect AI handshake draft View AI credential page n8n + Codex roles Client / vendor intake

Internal First Built for MJM controlled work in regulated environments.

Third-Party Ready Extensible as an independent quality check for GMP/GLP teams.

AI-Readable Prepared for future AI inspector bots and vendor-credential review.

Graphical Case Study

Calibration package review: from field readings to release-ready records.

MJM QAAI catches documentation problems that hide inside otherwise normal-looking calibration packages.

Field Data Handoff

Technician records are returned through a controlled handoff path. Original uploads stay preserved.

Visual + Arithmetic Review

Scanned pages, handwriting, expected mA values, error fields, signatures, and blanks are reviewed together.

Tolerance-Basis Decision

A printed form tolerance is challenged when it does not match the actual field calibration basis.

Human Review Gate

AI findings support the review, but official disposition, initials, signatures, and approval stay human-controlled.

Clean Turnover Package

Signed approved records and supporting certificates are packaged separately from internal AI working notes.

Decision Tree

Does the form tolerance match the field calibration basis?

No: classify as tolerance-basis question

Yes: continue review

Is the source certificate correct for the installed condition?

No: exclude or mark non-controlling

Yes: include as support

Are review fields and signatures complete?

No: hold certificate readiness

Yes: assemble turnover package

4instrument records reviewed

1tolerance-basis conflict caught

0AI working notes in turnover package

4.6/5self-graded workflow score

Future Web Tool

Vendor documentation intake processor.

Vendors could submit a documentation package for AI-assisted completeness review before the package enters a quality turnover record.

Vendor Upload

Vendor submits certificates, COCs, MTRs, calibration records, manuals, drawings, and supporting files into a controlled intake.

Completeness Scan

AI checks required document types, missing fields, tag/serial mismatches, revision status, dates, signatures, and source traceability.

Gap Report

Vendor and MJM receive a structured gap list: missing records, questionable sources, unresolved acceptance basis, and release blockers.

Human Approval

Approved packages can enter the quality turnover package as AI-inspected, human-approved documentation.

Draft boundary: AI may inspect, classify, and route findings in real time. Formal approval remains controlled by MJM/client quality authority.

AI Handshake / Vendor Credential Readiness

Prepare for AI inspectors before they become a vendor requirement.

There is no single universal “AI quality-program credential” standard today. The practical path is to create an MJM QAAI standard now, while aligning with emerging interoperability and provenance patterns.

Use Our Own Standard

Create a versioned MJM QAAI handshake that states scope, sources, review boundaries, approval authority, anonymization rules, and evidence package expectations.

Align Where Useful

Track A2A Agent Cards for discovery metadata, MCP/OAuth patterns for scoped access, C2PA for provenance concepts, and NIST AI RMF for AI-risk language.

Credential Package

Publish an AI-readable JSON file plus human-readable source map, quality statement, inspection scope, and vendor-intake rules.

Draft discovery path

/.well-known/mjm-qaai-handshake.json

A2A-inspired capability metadata

agent-card.json

Draft public package

ai-credential.html + ai-handshake.json + agent-card.json + vendor-intake-spec.md

Foundation Sources

Primary sources and interoperability references.

Quality foundations: FDA/eCFR, ICH Q9/Q10, ASME BPE, ASTM A967/A967M, USP water monographs, client-controlled requirements.
AI interoperability references: A2A Agent Card concepts, MCP authorization, C2PA provenance model, NIST AI Risk Management Framework.
Platform relationship: n8n is the trigger/transport layer; Codex / MJM QAAI is the processor and smart review layer.
Publication gate: customer identifiers removed, primary sources verified, human approval required before release.