Azure Computer Vision - OpenText Active Community - Trading Grid Integration and Automation

Common Integration Use Cases Between Azure Computer Vision and OpenText Active Community - Trading Grid

Azure Computer Vision and OpenText Active Community - Trading Grid complement each other well in partner-facing workflows. Azure Computer Vision can extract and classify information from images, scans, and photos, while Trading Grid provides a structured collaboration space for trading partners to resolve issues, exchange documents, and coordinate B2B activities. Together, they can reduce manual review, speed up exception handling, and improve visibility across partner ecosystems.

1. Automated document intake for partner claims and disputes

Data flow: OpenText Active Community - Trading Grid to Azure Computer Vision, then back to Trading Grid

Trading partners upload supporting documents such as delivery photos, damaged goods images, signed receipts, or invoice scans into the community. Azure Computer Vision extracts text from the documents, identifies key fields, and classifies the content by document type. The extracted data is then posted back into Trading Grid to support claim review, dispute resolution, and case assignment.

• Reduces manual data entry and document triage
• Speeds up claims validation and exception handling
• Improves consistency in partner dispute workflows

2. Photo-based quality issue reporting and triage

Data flow: OpenText Active Community - Trading Grid to Azure Computer Vision

When a supplier or logistics partner submits product or shipment photos through Trading Grid, Azure Computer Vision analyzes the images to detect defects, packaging damage, missing labels, or incorrect items. The results are used to route the issue to the correct internal team, such as quality assurance, procurement, or customer service, with supporting evidence attached in the community thread.

• Accelerates root cause identification
• Improves prioritization of quality incidents
• Creates a more reliable audit trail for partner issues

3. Automated extraction of shipment and compliance information from partner-submitted images

Data flow: OpenText Active Community - Trading Grid to Azure Computer Vision, then to downstream business systems

Trading partners often share photos of shipping labels, customs forms, certificates, or proof of delivery documents in the community. Azure Computer Vision performs OCR to extract shipment numbers, dates, reference codes, and compliance text. That data can then be shared in Trading Grid and forwarded to ERP, logistics, or compliance systems for faster processing.

• Reduces delays caused by manual document review
• Improves accuracy of shipment and compliance data capture
• Supports faster customs, receiving, and settlement activities

4. Visual evidence management for supplier performance cases

Data flow: Bi-directional

Procurement teams can open supplier performance cases in Trading Grid and attach images of nonconforming goods, damaged pallets, or labeling errors. Azure Computer Vision analyzes the images to tag the issue type and extract relevant details. The supplier can then respond in the same community thread with additional photos or corrected documentation, creating a structured collaboration loop.

• Improves supplier accountability with objective visual evidence
• Shortens resolution cycles for recurring performance issues
• Supports standardized case management across partner networks

5. Automated catalog and product content validation for trading partners

Data flow: OpenText Active Community - Trading Grid to Azure Computer Vision

Trading partners can submit product images, packaging shots, or shelf-ready artwork through Trading Grid for onboarding or content approval. Azure Computer Vision identifies objects, logos, and visible text to verify whether the submitted assets match approved product specifications. Review teams can use the results to approve, reject, or request corrections directly in the community.

• Reduces manual review of product content submissions
• Helps enforce brand and packaging standards
• Speeds up partner onboarding and catalog updates

6. Faster resolution of delivery exceptions using image analysis

Data flow: OpenText Active Community - Trading Grid to Azure Computer Vision

When a carrier or supplier reports a delivery exception, they can upload photos of the shipment condition, seal status, or warehouse receiving area into Trading Grid. Azure Computer Vision analyzes the images to detect visible damage, unreadable labels, or missing identifiers. The findings help operations teams determine whether the issue is a transportation problem, receiving discrepancy, or supplier error.

• Improves exception classification and routing
• Reduces back-and-forth with trading partners
• Supports faster claims and corrective action processing

7. Partner-submitted accessibility and content enrichment for shared assets

Data flow: OpenText Active Community - Trading Grid to Azure Computer Vision, then back to Trading Grid

Partners often share marketing images, product photos, or technical diagrams in the community. Azure Computer Vision can generate text descriptions and OCR output to enrich those assets with searchable metadata. The enriched content is then stored or referenced in Trading Grid so internal teams can quickly find and reuse approved partner materials.

• Improves searchability of shared visual assets
• Supports reuse of partner content across teams
• Reduces time spent manually tagging files

Overall, integrating Azure Computer Vision with OpenText Active Community - Trading Grid creates a practical workflow for partner collaboration, visual document processing, and exception management. The strongest value comes from automating image and document interpretation while keeping Trading Grid as the collaboration and case coordination layer.