OpenText Active Community - Trading Grid - Azure AI Document Intelligence Integration and Automation

Common Integration Use Cases Between OpenText Active Community - Trading Grid and Azure AI Document Intelligence

1. Automated extraction of partner-submitted documents into Trading Grid workflows

Data flow: Azure AI Document Intelligence ? OpenText Active Community - Trading Grid

Trading partners often upload invoices, packing slips, certificates, and compliance forms into the community for review. Azure AI Document Intelligence can extract key fields such as supplier name, PO number, invoice total, shipment date, and document type, then pass the structured data into Trading Grid to create or update collaboration threads, route exceptions, and trigger follow-up actions.

Business value: Reduces manual data entry, speeds up document review, and improves response times for partner issues.

• Automatically classify incoming partner documents
• Populate case records or discussion threads with extracted fields
• Route exceptions to the right internal or external team

2. Exception handling for invoice and shipment discrepancies

Data flow: Azure AI Document Intelligence ? OpenText Active Community - Trading Grid

When invoices or shipping documents are processed, Azure AI Document Intelligence can identify mismatches such as missing line items, quantity variances, or incorrect totals. Those exceptions can be posted into Trading Grid as collaboration cases so procurement, finance, and logistics teams can work with the trading partner in a shared environment to resolve the issue.

Business value: Shortens dispute resolution cycles and reduces payment delays caused by document errors.

• Detect discrepancies during document ingestion
• Create partner-facing exception cases automatically
• Track resolution status across internal and external stakeholders

3. Compliance document intake and validation for trading partner onboarding

Data flow: Azure AI Document Intelligence ? OpenText Active Community - Trading Grid

During partner onboarding or periodic compliance reviews, organizations receive tax forms, insurance certificates, banking documents, and regulatory attestations. Azure AI Document Intelligence can extract and validate the required data, then Trading Grid can be used to coordinate missing items, approvals, and follow-up communication with the partner.

Business value: Improves onboarding speed while strengthening compliance control and audit readiness.

• Extract required fields from onboarding documents
• Flag missing or expired documents
• Coordinate remediation with partners in a shared workspace

4. Shared document review for contract and amendment processing

Data flow: OpenText Active Community - Trading Grid ? Azure AI Document Intelligence ? OpenText Active Community - Trading Grid

Partners may submit contracts, amendments, or supporting attachments through Trading Grid. Azure AI Document Intelligence can extract clause references, dates, signatures, and key terms, then return the structured output to Trading Grid so legal, procurement, and account teams can review changes and collaborate on approvals.

Business value: Accelerates document review and reduces the risk of overlooking critical terms.

• Capture contract metadata from uploaded files
• Support collaborative review of amendments and redlines
• Store extracted data alongside partner discussions and approvals

5. Claims, returns, and dispute packet processing

Data flow: Azure AI Document Intelligence ? OpenText Active Community - Trading Grid

In claims or returns scenarios, partners submit supporting documents such as proof of delivery, photos, invoices, and claim forms. Azure AI Document Intelligence can extract claim identifiers, dates, amounts, and reference numbers, then Trading Grid can organize the case, notify stakeholders, and maintain a complete communication history.

Business value: Improves case handling consistency and reduces time spent searching for supporting evidence.

• Extract claim details from multi-document packets
• Link documents to the correct partner case
• Coordinate resolution across customer service, finance, and operations

6. Purchase order and fulfillment document reconciliation

Data flow: Azure AI Document Intelligence ? OpenText Active Community - Trading Grid

Organizations can use Azure AI Document Intelligence to extract data from purchase orders, acknowledgements, advance ship notices, and delivery confirmations. Trading Grid can then present the extracted information to buyers, suppliers, and logistics teams for reconciliation when quantities, dates, or item codes do not align.

Business value: Increases supply chain visibility and reduces manual reconciliation effort.

• Compare extracted document data across transaction types
• Surface mismatches for partner review
• Support faster resolution of fulfillment issues

7. Centralized partner document repository with searchable extracted metadata

Data flow: OpenText Active Community - Trading Grid ? Azure AI Document Intelligence ? OpenText Active Community - Trading Grid

Documents shared in Trading Grid can be processed by Azure AI Document Intelligence to extract metadata and content fields, which are then written back to the community as searchable attributes. This makes it easier for teams to find documents by invoice number, shipment reference, supplier name, or compliance category without opening each file manually.

Business value: Improves document discoverability and reduces time spent on manual searches.

• Index partner-shared documents with extracted metadata
• Enable faster retrieval by business-relevant fields
• Support audit and operational reporting

8. Bi-directional collaboration for document-driven case management

Data flow: OpenText Active Community - Trading Grid ? Azure AI Document Intelligence

Trading Grid can serve as the collaboration layer for partner communication, while Azure AI Document Intelligence handles document extraction in the background. As new documents are uploaded or revised, extracted data can update the case record, trigger new tasks, and notify participants. If a partner submits corrected documents, the updated extraction results can refresh the same case thread and keep all stakeholders aligned.

Business value: Creates a closed-loop workflow that combines human collaboration with automated document processing.

• Keep case records synchronized with document updates
• Trigger notifications when new or corrected documents arrive
• Maintain a single operational view for internal teams and partners