Prodigy - OpenText Extended ECM - Records Management Integration and Automation

Common Integration Use Cases Between Prodigy and OpenText Extended ECM - Records Management

1. Declare approved training datasets as controlled records

Flow: Prodigy to OpenText Extended ECM - Records Management

When a labeling project reaches a quality threshold and the final training dataset is approved, Prodigy can send the dataset, labeling guidelines, reviewer sign-off, and model version context to OpenText Extended ECM for formal records declaration. This creates a compliant audit trail for regulated AI development in healthcare, financial services, or government.

Business value: Preserves evidence of how training data was created and approved, supports audit readiness, and reduces risk of disputes over model provenance.

2. Retain annotation evidence for regulated model development

Flow: Prodigy to OpenText Extended ECM - Records Management

Prodigy can export annotation logs, reviewer comments, label changes, and active learning decisions into OpenText Extended ECM as managed records. This is useful when organizations must demonstrate how sensitive datasets were curated, especially for models used in claims processing, clinical document classification, or fraud detection.

Business value: Provides defensible documentation for internal governance, external audits, and legal discovery.

3. Trigger retention policies for source data used in AI projects

Flow: OpenText Extended ECM - Records Management to Prodigy

OpenText can notify Prodigy when source documents, images, or case files are subject to retention, legal hold, or disposition rules. Prodigy can then restrict labeling, pause active learning on affected content, or flag datasets that must not be modified or deleted.

Business value: Prevents accidental use of restricted content, aligns AI workflows with records governance, and reduces compliance exposure.

4. Maintain traceability from business record to labeled training sample

Flow: Bi-directional

Prodigy can store the record identifier from OpenText for each labeled item, while OpenText can retain the link back to the exact annotation set and model training batch. This creates end-to-end traceability from the original business record to the derived AI training asset.

Business value: Enables lineage tracking, supports model explainability efforts, and helps teams answer which records influenced a specific model release.

5. Govern human review of sensitive content before annotation

Flow: OpenText Extended ECM - Records Management to Prodigy

For content classified as confidential, protected health information, or legally privileged, OpenText can provide classification and retention metadata to Prodigy before annotation begins. Prodigy can use that metadata to route items to approved reviewers only, apply special handling rules, or exclude content from certain labeling projects.

Business value: Improves data handling controls, limits exposure of sensitive records, and supports role-based review workflows.

6. Archive completed labeling projects with full governance metadata

Flow: Prodigy to OpenText Extended ECM - Records Management

At project completion, Prodigy can transfer the final dataset, project configuration, label taxonomy, reviewer approvals, and quality metrics into OpenText as a complete project record. This is especially valuable for organizations that need to preserve the evidence behind AI-assisted decisions for years after model deployment.

Business value: Simplifies long-term retention, reduces storage sprawl in operational systems, and creates a single governed archive for project history.

7. Support legal hold and eDiscovery for AI training artifacts

Flow: OpenText Extended ECM - Records Management to Prodigy

If a legal hold is placed on a source record or a training dataset, OpenText can send hold status to Prodigy so the related annotation project is frozen. Prodigy can prevent deletion, editing, or retraining on affected samples until the hold is released.

Business value: Protects organizations from spoliation risk, ensures legal compliance, and keeps AI development aligned with litigation requirements.

8. Create a governed feedback loop for model improvement using retained records

Flow: Bi-directional

Prodigy can send misclassified examples, edge cases, and newly labeled samples to OpenText for retention as governed business records. OpenText can then supply approved historical records back to Prodigy for future retraining cycles, ensuring only authorized and retained content is reused.

Business value: Enables continuous model improvement while maintaining strict control over what data can be reused, archived, or disposed of.