Google Document AI - OpenText Product Traceability Integration and Automation

Common Integration Use Cases Between Google Document AI and OpenText Product Traceability

1. Automated extraction of supplier and batch data from inbound documents

Data flow: Google Document AI - OpenText Product Traceability

Google Document AI can extract structured data from supplier invoices, packing slips, certificates of analysis, and shipping documents, then pass key fields such as lot numbers, quantities, expiration dates, and supplier IDs into OpenText Product Traceability. This reduces manual entry at receiving and helps operations teams create traceability records faster and with fewer errors.

2. Traceability record enrichment from quality and compliance documents

Data flow: Google Document AI - OpenText Product Traceability

Quality certificates, regulatory declarations, and inspection reports can be processed by Google Document AI and linked to the corresponding product, batch, or shipment record in OpenText Product Traceability. This gives quality, compliance, and audit teams a complete traceability file without searching across shared drives or email inboxes.

3. Exception handling for missing or inconsistent product identifiers

Data flow: OpenText Product Traceability - Google Document AI

When OpenText Product Traceability detects incomplete batch data, mismatched lot numbers, or missing supplier documentation, it can send the related documents to Google Document AI for reprocessing or deeper extraction. This supports exception resolution by helping teams recover data from scanned or semi-structured documents before shipments are delayed or records are closed incorrectly.

4. Automated onboarding of supplier documentation into traceability workflows

Data flow: Google Document AI - OpenText Product Traceability

New supplier packets often include multiple document types such as product specifications, compliance statements, and origin certificates. Google Document AI can classify and extract information from these documents, then route the results into OpenText Product Traceability to create or update supplier and material master records. Procurement and quality teams benefit from faster supplier onboarding and better documentation control.

5. Recall readiness through document-to-lot linkage

Data flow: Bi-directional

OpenText Product Traceability can provide the affected lot, shipment, or ingredient context, while Google Document AI extracts supporting evidence from related documents such as receiving records, production forms, and distribution paperwork. Together, they help trace impacted products more quickly during recall investigations and reduce the time needed to assemble an audit-ready evidence trail.

6. Digitization of paper-based production and distribution records

Data flow: Google Document AI - OpenText Product Traceability

Plants and warehouses still relying on paper logs can scan production sheets, dispatch notes, and hand-signed transfer forms into Google Document AI for extraction. The structured output can then be loaded into OpenText Product Traceability to maintain a digital chain of custody across manufacturing, warehousing, and logistics teams.

7. Audit support with searchable traceability documentation

Data flow: Bi-directional

OpenText Product Traceability can identify the relevant product history, while Google Document AI can index and extract content from supporting documents such as certificates, transport records, and inspection forms. This makes it easier for compliance teams to respond to customer audits, regulatory inspections, and internal reviews with complete and searchable evidence.

8. Automated discrepancy resolution between documents and traceability records

Data flow: Google Document AI - OpenText Product Traceability

Google Document AI can extract shipment quantities, product codes, and dates from source documents and compare them with records in OpenText Product Traceability. When discrepancies are found, the integration can flag them for review by supply chain or quality teams, helping prevent inventory errors, incorrect lot assignments, and downstream reporting issues.