OpenText Documentum - Steg.ai Integration and Automation

Common Integration Use Cases Between OpenText Documentum and Steg.ai

1. Automated image and document tagging for regulated content repositories

Data flow: Steg.ai ? OpenText Documentum

When new images, scanned documents, or visual assets are ingested into Steg.ai, its AI recognition engine can extract tags such as document type, product name, facility, equipment, or safety category and push those metadata values into OpenText Documentum. This improves searchability and classification inside controlled repositories.

• Reduces manual indexing effort for records teams
• Improves retrieval of critical documents during audits or investigations
• Supports consistent metadata standards across business units

2. Compliance-driven content protection for sensitive visual assets

Data flow: OpenText Documentum ? Steg.ai

OpenText Documentum can send approved or sensitive image assets to Steg.ai for content protection processing, such as classification, tagging, or security-related recognition. This is useful for controlled materials like product images, engineering diagrams, lab photos, or government records that require additional handling rules.

• Helps identify sensitive assets before broader distribution
• Supports policy-based protection for regulated content
• Improves governance over high-risk visual files

3. Controlled workflow for asset review and approval

Data flow: Bi-directional

Steg.ai can analyze incoming assets and return classification results to OpenText Documentum, which then routes the content through approval workflows based on the detected category. For example, a scanned quality inspection image may be automatically routed to compliance, quality assurance, or legal review depending on the tags assigned.

• Speeds up routing of content to the right reviewers
• Reduces misclassification and manual triage
• Improves governance for content that requires sign-off before release

4. Records enrichment for scanned archives and legacy content

Data flow: Steg.ai ? OpenText Documentum

Organizations migrating or digitizing legacy archives can use Steg.ai to analyze scanned images, forms, and photographs, then enrich OpenText Documentum records with extracted metadata. This is especially valuable for older content that lacks reliable indexing or was stored with minimal descriptive data.

• Improves discoverability of legacy records
• Reduces time spent on manual reclassification
• Supports large-scale archive modernization initiatives

5. Policy-based retention and disposition for classified assets

Data flow: OpenText Documentum ? Steg.ai ? OpenText Documentum

OpenText Documentum can trigger Steg.ai analysis when a new asset enters a retention-controlled repository. Based on the AI-generated classification, Documentum can apply the correct retention schedule, legal hold status, or disposition rule. This is useful where asset type determines how long content must be retained.

• Aligns classification with records management policies
• Reduces risk of premature deletion or over-retention
• Supports defensible compliance processes

6. Faster search and retrieval for operational teams

Data flow: Steg.ai ? OpenText Documentum

Steg.ai can identify objects, scenes, labels, or document characteristics in images and pass those tags into OpenText Documentum search indexes. Operations, engineering, legal, and compliance teams can then locate specific assets faster, such as a site photo containing a particular machine, a labeled package, or a signed form.

• Improves productivity for teams that rely on visual content
• Reduces dependency on exact file naming conventions
• Supports faster response to internal and external requests

7. Secure distribution of approved branded and product imagery

Data flow: OpenText Documentum ? Steg.ai

Marketing, product, or communications teams can store approved assets in OpenText Documentum and send them to Steg.ai for tagging and protection before distribution to downstream channels. This helps ensure only approved imagery is released and that assets are consistently labeled for reuse and governance.

• Prevents use of outdated or unapproved visuals
• Improves control over brand and product asset libraries
• Supports cross-functional collaboration between legal, marketing, and compliance

8. Audit-ready content classification for regulated inspections

Data flow: Bi-directional

In regulated industries such as life sciences, energy, and government, Steg.ai can classify inspection photos, field images, or evidence files and send the results to OpenText Documentum, where they are stored with full audit trails, version control, and lifecycle governance. This creates a defensible record for inspections, incident reviews, and regulatory submissions.

• Strengthens audit readiness and traceability
• Supports controlled handling of evidence and inspection assets
• Improves consistency across field, compliance, and records teams