Azure Computer Vision - OpenText Core Case Integration and Automation

Common Integration Use Cases Between Azure Computer Vision and OpenText Core Case

Azure Computer Vision and OpenText Core Case complement each other well in case-driven operations where visual content must be analyzed, classified, and attached to an investigation, claim, review, or compliance workflow. Azure Computer Vision automates extraction of insights from images and scanned documents, while OpenText Core Case provides the structured case environment to manage tasks, decisions, evidence, and outcomes.

1. Automated evidence intake for claims and investigations

When a customer, employee, or field agent submits photos or scanned documents, Azure Computer Vision can extract text, detect objects, and classify the content before it is attached to an OpenText Core Case record. This reduces manual triage and speeds up case creation for insurance claims, incident investigations, and service disputes.

• Data flow: Azure Computer Vision to OpenText Core Case
• Business value: Faster case initiation, less manual indexing, improved evidence consistency
• Example: Auto-read a damaged vehicle photo set and OCR a repair estimate, then populate the claim case with key details and supporting attachments

2. OCR-based document extraction for compliance and review cases

OpenText Core Case can route compliance or audit cases that include scanned forms, IDs, invoices, or letters. Azure Computer Vision extracts text from these documents and passes structured data into the case so reviewers can validate information without reading every file manually.

• Data flow: Azure Computer Vision to OpenText Core Case
• Business value: Reduced review time, better accuracy, improved audit readiness
• Example: Extract policy numbers, dates, and signatures from scanned onboarding documents and attach them to a compliance case for verification

3. Image classification for case routing and prioritization

Azure Computer Vision can identify the type of visual content submitted with a case, such as a product defect, damaged shipment, safety incident, or identity document. OpenText Core Case can use that classification to assign the case to the correct team, set priority, and trigger the right workflow.

• Data flow: Azure Computer Vision to OpenText Core Case
• Business value: Better triage, fewer misrouted cases, faster resolution
• Example: A logistics team receives package damage photos, and the system automatically routes the case to the freight claims team with high priority

4. Customer-submitted photo analysis for service and warranty cases

For product support or warranty claims, Azure Computer Vision can analyze customer-submitted images to detect product type, visible damage, missing parts, or brand logos. OpenText Core Case then stores the findings alongside the customer issue, enabling support agents and adjudicators to make quicker decisions.

• Data flow: Azure Computer Vision to OpenText Core Case
• Business value: Faster warranty decisions, improved agent productivity, more consistent outcomes
• Example: A consumer electronics company uses photo analysis to confirm screen damage before approving a replacement case

5. Visual evidence enrichment for investigations and fraud review

In fraud, loss prevention, or internal investigation cases, Azure Computer Vision can detect objects, text, and scene details in submitted images to enrich the case file. OpenText Core Case can combine this visual evidence with notes, tasks, and related documents to support investigator review and decision-making.

• Data flow: Azure Computer Vision to OpenText Core Case
• Business value: Stronger evidence handling, improved investigator efficiency, better case consistency
• Example: A claims investigator receives accident photos and OCR results from a police report, all consolidated in one case view

6. Accessibility and case summarization for document-heavy workflows

Azure Computer Vision can generate text from images and support alt-text or descriptive metadata for visual attachments stored in OpenText Core Case. This improves accessibility for case workers and helps teams quickly understand the content of image-based evidence or scanned records.

• Data flow: Azure Computer Vision to OpenText Core Case
• Business value: Better accessibility, faster case comprehension, reduced dependence on manual review
• Example: A legal or compliance team reviews a case with dozens of scanned exhibits and uses OCR-generated summaries to locate relevant evidence faster

7. Closed-loop case feedback for image quality and submission validation

OpenText Core Case can send case outcomes back to Azure Computer Vision-driven intake processes to improve submission quality rules. For example, if cases are repeatedly delayed because photos are blurry, incomplete, or missing required angles, those patterns can be used to refine capture guidance and validation rules before case submission.

• Data flow: OpenText Core Case to Azure Computer Vision, with operational feedback loops
• Business value: Higher-quality submissions, fewer rework cycles, lower handling costs
• Example: If warranty cases are rejected due to unreadable serial numbers, the intake app can prompt users to retake the image before the case is created

8. Multi-team case collaboration with visual asset tagging

Azure Computer Vision can tag and categorize images attached to a case so that different teams, such as operations, legal, compliance, and customer service, can quickly find relevant visual evidence in OpenText Core Case. This is especially useful in complex cases that move across departments and require a shared understanding of the same visual records.

• Data flow: Azure Computer Vision to OpenText Core Case
• Business value: Better cross-team visibility, faster handoffs, improved case governance
• Example: A product safety case includes photos of packaging, labels, and defects, all automatically tagged for easy retrieval by quality assurance and legal teams

Overall, integrating Azure Computer Vision with OpenText Core Case helps organizations turn unstructured visual content into actionable case data, improving speed, accuracy, and accountability across claims, compliance, support, and investigation workflows.