Prodigy - OpenText Core Signature Integration and Automation

Common Integration Use Cases Between Prodigy and OpenText Core Signature

Prodigy and OpenText Core Signature complement each other in enterprise workflows where AI model development requires controlled review, approval, and auditability. Prodigy supports the creation and refinement of high-quality labeled datasets, while OpenText Core Signature can formalize sign-off on labeling standards, dataset releases, and compliance approvals.

1. Formal approval of labeling guidelines before annotation begins

Data flow: Prodigy to OpenText Core Signature

AI teams can use Prodigy to define labeling instructions, class taxonomies, and edge-case examples for a new model initiative. Once the labeling guide is finalized, it is routed through OpenText Core Signature for approval by data science leads, legal, compliance, and business stakeholders. This ensures that annotation rules are formally approved before large-scale labeling starts.

Business value: Reduces rework, prevents inconsistent labeling, and creates an auditable approval trail for model governance.

2. Signed acceptance of training dataset releases

Data flow: Prodigy to OpenText Core Signature

When a dataset is prepared in Prodigy for model training, the exported dataset package can be sent to OpenText Core Signature for sign-off by the responsible data owner, model owner, and quality reviewer. The signed approval confirms that the dataset meets quality thresholds and is ready for use in training or validation.

Business value: Improves accountability for dataset readiness and supports controlled release processes for regulated or high-impact AI use cases.

3. Compliance approval for sensitive data annotation workflows

Data flow: Prodigy to OpenText Core Signature

For projects involving personal data, medical records, financial documents, or confidential customer content, Prodigy can be used to prepare the annotation workflow while OpenText Core Signature manages compliance approvals. Before annotators access the data, required documents such as data handling agreements, privacy acknowledgments, or project authorization forms can be signed electronically.

Business value: Helps organizations enforce privacy and security controls before sensitive data is exposed to labeling teams.

4. Human review and sign-off on model feedback labels

Data flow: Prodigy to OpenText Core Signature

Prodigy?s active learning workflow often surfaces uncertain or high-value samples for expert review. In enterprise settings, the final decision on disputed labels or critical edge cases can be routed to OpenText Core Signature for formal approval by subject matter experts. This is useful when label decisions affect downstream model behavior, such as fraud detection, claims processing, or quality inspection.

Business value: Creates a controlled escalation path for ambiguous annotations and strengthens label quality for high-risk models.

5. Contracted labeling vendor approvals and work authorization

Data flow: OpenText Core Signature to Prodigy

Organizations that outsource annotation work can use OpenText Core Signature to execute vendor agreements, statements of work, confidentiality agreements, and project-specific work authorizations. Once signed, the approved documents and project terms can trigger access provisioning or project setup in Prodigy for the external labeling team.

Business value: Speeds vendor onboarding while ensuring legal and operational approvals are completed before work begins.

6. Audit-ready approval of model training evidence packages

Data flow: Prodigy to OpenText Core Signature

For regulated industries, teams can export evidence from Prodigy such as labeling instructions, reviewer comments, dataset versions, and quality metrics. OpenText Core Signature can then be used to obtain formal sign-off on the evidence package from governance, risk, or compliance teams before the model is promoted to production.

Business value: Supports audit readiness, model governance, and traceability across the AI lifecycle.

7. Cross-functional approval of annotation policy changes

Data flow: Bi-directional

When annotation policies change, such as adding new classes, revising acceptance criteria, or updating review thresholds, Prodigy can capture the updated workflow and OpenText Core Signature can route the change request for approval. After signatures are collected, the approved policy can be pushed back into the Prodigy workflow as the active standard.

Business value: Ensures controlled change management and keeps annotation operations aligned with business and compliance requirements.

8. Sign-off on AI project milestones and go-live readiness

Data flow: Prodigy to OpenText Core Signature

At key milestones, such as dataset completion, validation completion, or pre-production readiness, Prodigy can provide the supporting project artifacts and OpenText Core Signature can manage milestone approvals. This is especially useful for enterprise AI programs that require formal gates before moving from experimentation to deployment.

Business value: Improves governance across AI delivery teams and provides a clear approval record for project progression.