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Use OpenText Directory Services as the authoritative source for user identities and group membership, then sync approved users into Prodigy so data scientists, annotators, and reviewers receive access automatically. This reduces manual account setup, speeds onboarding, and ensures only active employees or contractors can access labeling projects.
Integrate directory groups with Prodigy roles so users inherit permissions based on their department or project assignment. For example, annotators can label data, reviewers can approve labels, and ML leads can manage datasets and workflows without separate permission administration.
When an employee leaves or a contractor assignment ends, OpenText Directory Services can trigger immediate deactivation of the corresponding Prodigy account. This prevents former users from accessing sensitive training data, proprietary labeling guidelines, or active machine learning projects.
Sync directory groups into Prodigy to automatically assemble project teams for specific AI initiatives, such as computer vision quality control or NLP classification. Business units can manage membership in OpenText Directory Services while Prodigy reflects the latest team structure without manual updates.
Use OpenText Directory Services to manage temporary identities for external annotators or managed service providers, then provision limited Prodigy access only for approved projects. This supports controlled outsourcing of labeling work while maintaining enterprise identity governance.
Combine directory records from OpenText Directory Services with Prodigy user activity to create a clear mapping of who accessed which annotation projects and when. This is useful for regulated industries that need traceability for model training data creation and reviewer accountability.
For organizations training models on confidential data, such as healthcare, legal, or financial records, OpenText Directory Services can segment users by department or clearance level and push those rules into Prodigy. This ensures teams only see datasets they are authorized to label.
In organizations that run repeated labeling campaigns, directory-based automation can reassign returning users to the correct Prodigy projects each cycle. This is especially useful for seasonal data labeling, model retraining, or continuous active learning programs where teams change frequently.