Azure Computer Vision - OpenText Core Content - Metadata Integration and Automation

Common Integration Use Cases Between Azure Computer Vision and OpenText Core Content - Metadata

1. Automated metadata enrichment for digital asset ingestion

Flow: Azure Computer Vision ? OpenText Core Content - Metadata

When images, scans, or videos are ingested into OpenText Core Content, Azure Computer Vision can analyze the content and return tags such as objects, scenes, text, logos, and image attributes. Those results can then be mapped into governed metadata fields in OpenText Core Content, reducing manual indexing effort and improving consistency across the repository.

• Business value: faster asset onboarding and better searchability
• Operational benefit: fewer manual tagging errors and lower content operations workload
• Typical users: DAM administrators, content librarians, marketing operations teams

2. OCR-driven document classification and metadata population

Flow: Azure Computer Vision ? OpenText Core Content - Metadata

For scanned contracts, invoices, forms, and certificates, Azure Computer Vision can extract printed text through OCR and identify key text elements. OpenText Core Content can then use that extracted text to populate structured metadata fields such as document type, reference number, customer name, or date, supporting controlled classification and downstream workflow routing.

• Business value: improved document retrieval and faster processing of scanned content
• Operational benefit: reduced rekeying and better compliance with metadata standards
• Typical users: records management, shared services, compliance teams

3. Controlled vocabulary mapping from AI-generated tags

Flow: Azure Computer Vision ? OpenText Core Content - Metadata

Azure Computer Vision may generate broad or descriptive labels that are useful for discovery but not always aligned to enterprise taxonomy. OpenText Core Content can enforce controlled vocabularies by mapping AI-generated labels to approved terms, such as converting generic object detection results into business-approved categories like product line, campaign theme, or content region.

• Business value: consistent metadata across teams and repositories
• Operational benefit: better governance without losing AI automation benefits
• Typical users: taxonomy managers, DAM governance teams, content stewards

4. Brand and compliance review workflow for visual content

Flow: Azure Computer Vision ? OpenText Core Content - Metadata

Azure Computer Vision can detect logos, text, and potentially sensitive visual elements in uploaded assets. OpenText Core Content can store the resulting metadata and trigger review workflows when content matches restricted brands, unapproved logos, or regulated text patterns. This helps teams route assets for legal, brand, or compliance approval before publication.

• Business value: reduced brand and regulatory risk
• Operational benefit: automated review triggers based on content characteristics
• Typical users: legal, brand governance, compliance, marketing approval teams

5. Accessibility metadata generation for publishing workflows

Flow: Azure Computer Vision ? OpenText Core Content - Metadata

Azure Computer Vision can generate image descriptions and identify key visual elements that support accessibility requirements. OpenText Core Content can capture this information as governed metadata, such as alt-text, image summary, or accessibility status, so publishing teams can reuse approved descriptions across websites, portals, and digital channels.

• Business value: improved accessibility compliance and content reuse
• Operational benefit: less manual writing of alt-text and faster publishing cycles
• Typical users: web content teams, accessibility specialists, digital publishing teams

6. Customer-submitted image intake with structured quality classification

Flow: Azure Computer Vision ? OpenText Core Content - Metadata

Organizations that receive customer-submitted photos for claims, warranty, service, or quality review can use Azure Computer Vision to detect image attributes, text, and objects. OpenText Core Content can then classify the submission using metadata such as issue type, product category, location, or case priority, enabling faster triage and routing to the correct business team.

• Business value: quicker case handling and better customer response times
• Operational benefit: standardized intake and improved case categorization
• Typical users: customer service, claims operations, quality assurance teams

7. Product image catalog enrichment for commerce and DAM

Flow: Azure Computer Vision ? OpenText Core Content - Metadata

For e-commerce and product content teams, Azure Computer Vision can identify products, attributes, and visible text in product imagery. OpenText Core Content can store these attributes as structured metadata to support product catalog management, variant grouping, and search filters, improving asset reuse across commerce and marketing channels.

• Business value: better product discoverability and richer catalog data
• Operational benefit: faster merchandising and reduced manual catalog maintenance
• Typical users: e-commerce operations, product information management teams, DAM teams

8. Metadata governance feedback loop for model tuning and taxonomy refinement

Flow: Bi-directional

OpenText Core Content can provide governed metadata standards, validation rules, and approved taxonomies that guide how Azure Computer Vision outputs should be interpreted. In return, Azure Computer Vision results can be reviewed against those standards to identify gaps, such as missing terms, inconsistent classifications, or recurring false positives. This creates a feedback loop that improves both metadata governance and AI tagging accuracy over time.

• Business value: stronger metadata quality and more reliable automation
• Operational benefit: continuous improvement of taxonomy and tagging rules
• Typical users: content governance, data stewardship, platform administrators