OpenText Core Content - Metadata - Google Document AI Integration and Automation

Common Integration Use Cases Between OpenText Core Content - Metadata and Google Document AI

1. Automated metadata extraction from incoming documents

Flow: Google Document AI ? OpenText Core Content - Metadata

Use Google Document AI to extract key fields from invoices, contracts, claims, forms, and correspondence, then push the extracted values into OpenText Core Content metadata fields. OpenText validation rules and controlled vocabularies can standardize the data before the content is stored or routed.

Business value: Reduces manual indexing effort, improves metadata accuracy, and speeds up content classification for downstream search and workflow automation.

2. Intelligent document classification and routing

Flow: Google Document AI ? OpenText Core Content - Metadata

Document AI can identify document type and extract supporting attributes, such as vendor name, policy number, customer ID, or case reference. OpenText Core Content then applies metadata-driven rules to classify the document and route it to the correct repository, team, or approval workflow.

Business value: Improves intake processing for shared services, AP, claims, HR, and legal operations by reducing misfiled content and accelerating handoffs.

3. Metadata enrichment for searchable enterprise content

Flow: Google Document AI ? OpenText Core Content - Metadata

When legacy or scanned documents are ingested, Document AI extracts text and structured data that can be mapped to OpenText metadata fields. This enriches content records with searchable attributes such as dates, amounts, parties, and document status.

Business value: Enhances search relevance and retrieval accuracy across large content repositories, especially where documents were previously stored with limited or inconsistent indexing.

4. Compliance-driven records tagging and retention support

Flow: Google Document AI ? OpenText Core Content - Metadata

Document AI can extract compliance-relevant details from regulated documents, such as contract terms, expiration dates, signatures, or policy references. OpenText metadata rules can then tag records with retention categories, review dates, or legal hold indicators.

Business value: Supports records governance, audit readiness, and policy enforcement while reducing the risk of missed retention actions or incomplete classification.

5. Exception handling for low-confidence extraction

Flow: Google Document AI ? OpenText Core Content - Metadata

When Document AI returns low-confidence values or cannot confidently classify a document, OpenText can apply metadata flags such as needs review, incomplete, or exception required. These items can then be routed to a business user for validation before final metadata is committed.

Business value: Creates a controlled human-in-the-loop process that improves data quality without slowing down the overall intake pipeline.

6. Metadata governance for AI-processed content

Flow: Bi-directional

OpenText Core Content defines the approved metadata schema, controlled vocabularies, and validation rules that Document AI outputs must follow. In return, Document AI supplies extracted values that are checked against those rules before content is accepted into the repository.

Business value: Ensures AI-generated metadata remains consistent with enterprise standards, reducing downstream cleanup and improving trust in automated classification.

7. Operational reporting on document processing performance

Flow: Google Document AI ? OpenText Core Content - Metadata

Document AI processing results, such as document type, extraction confidence, processing status, and exception counts, can be stored as metadata in OpenText. This enables reporting on intake volumes, processing accuracy, turnaround times, and backlog by department or document category.

Business value: Gives operations teams visibility into document processing performance and helps identify bottlenecks, recurring error patterns, and automation opportunities.

8. Contract and case file assembly from extracted document data

Flow: Google Document AI ? OpenText Core Content - Metadata

For complex business files made up of multiple documents, Document AI can extract identifiers and key attributes from each file component. OpenText Core Content can then use metadata to group related documents into a single contract package, claim file, or case record.

Business value: Improves file completeness and traceability for legal, procurement, insurance, and customer service teams that rely on accurate document grouping.