OpenText Documentum - Azure Integration and Automation

Common Integration Use Cases Between OpenText Documentum and Azure AI Document Intelligence

1. Automated intake of regulated documents into controlled repositories

Flow: Azure AI Document Intelligence – OpenText Documentum

Incoming PDFs, scanned forms, and correspondence are processed by Azure AI Document Intelligence to extract key fields such as document type, submission date, customer or patient ID, project number, and signer details. The extracted metadata is then used to automatically classify and file the document in the correct OpenText Documentum cabinet, folder, or records category.

Business value: Reduces manual indexing effort, improves filing accuracy, and ensures regulated content is stored with the right retention and access controls from the start.

2. Invoice and payment support document processing for finance operations

Flow: Azure AI Document Intelligence – OpenText Documentum – ERP or AP workflow

Supplier invoices, credit notes, and supporting documents are captured and extracted in Azure AI Document Intelligence. Key data such as invoice number, PO number, tax amount, and vendor name is passed into OpenText Documentum for controlled storage and auditability, while also triggering downstream accounts payable workflows.

Business value: Speeds up invoice handling, reduces duplicate entry, and creates a compliant document trail for finance audits and dispute resolution.

3. Contract and agreement ingestion with metadata enrichment

Flow: Azure AI Document Intelligence – OpenText Documentum

Executed contracts, amendments, NDAs, and service agreements are uploaded or scanned into Azure AI Document Intelligence for extraction of clause dates, counterparties, renewal terms, and signature status. That metadata is used to register the document in OpenText Documentum with the correct lifecycle rules, retention schedule, and security permissions.

Business value: Improves contract visibility, supports renewal tracking, and helps legal and procurement teams manage obligations more consistently.

4. Quality and batch record capture in life sciences manufacturing

Flow: Azure AI Document Intelligence – OpenText Documentum

Batch records, deviation forms, inspection reports, and quality certificates are digitized and analyzed by Azure AI Document Intelligence to extract batch number, product code, lot details, and approval status. The resulting data is stored in OpenText Documentum as part of a controlled quality record set with retention and audit requirements.

Business value: Shortens review cycles, improves traceability, and supports compliance with GMP and other regulated manufacturing standards.

5. Records classification and retention assignment for government archives

Flow: Azure AI Document Intelligence – OpenText Documentum

Government correspondence, permits, case files, and public records are processed to identify document type, originating department, case reference, and sensitivity level. OpenText Documentum then applies the appropriate records classification, retention policy, and access restrictions based on the extracted information.

Business value: Strengthens records governance, reduces misclassification risk, and supports defensible retention and disposal practices.

6. Exception handling for incomplete or low-confidence document extraction

Flow: Azure AI Document Intelligence – OpenText Documentum

When Azure AI Document Intelligence cannot confidently extract all required fields from a document, the file is stored in OpenText Documentum and routed to a controlled exception queue for human review. Once corrected, the updated metadata is written back to Documentum and the document continues through the normal workflow.

Business value: Ensures automation does not break on poor-quality scans or unusual formats, while preserving governance and auditability for manual corrections.

7. Searchable archive creation from legacy paper and scanned files

Flow: OpenText Documentum – Azure AI Document Intelligence – OpenText Documentum

Legacy scanned archives stored in OpenText Documentum are sent to Azure AI Document Intelligence to extract structured metadata from older documents that were previously only image-based. The enriched metadata is then written back into Documentum to improve search, retrieval, and reporting across historical content.

Business value: Makes legacy archives more usable, reduces time spent searching for records, and unlocks value from previously unstructured content.

8. Compliance monitoring and analytics on document populations

Flow: OpenText Documentum – Azure AI Document Intelligence – analytics or governance tools

Document sets from OpenText Documentum, such as contracts, quality records, or case files, are sampled or exported for AI-based extraction of key attributes like expiry dates, missing signatures, or incomplete forms. The results are used to identify compliance gaps, overdue reviews, and process bottlenecks across business units.

Business value: Gives compliance, operations, and leadership teams better visibility into document health and process performance, enabling targeted remediation.