Clinical biometrics teams are under increasing pressure to deliver faster while managing growing study complexity and maintaining confidence in every result.
In this short demonstration, you’ll see how Verify Generate uses a metadata-driven AI approach to independently generate validation outputs, compare them with production results, and provide complete explainability and traceability throughout the validation process.
For decades, independent double programming has been the gold standard for validating statistical Tables, Figures, and Listings (TFLs). While highly effective, it is also one of the most resource-intensive steps in clinical development, often requiring nearly twice the original programming effort and creating a significant bottleneck before submission.
Verify Generate was designed to modernize this process.
Using Statistical Analysis Plans (SAPs), specifications, mock shells, production TFLs, and CDISC ADaM datasets, Verify Generate independently produces validation outputs using a metadata-driven AI approach. Every generated output is compared against production results while providing complete visibility into the underlying metadata, AI assumptions, statistical methods, derived calculations, and supporting evidence.
Unlike traditional “black box” AI, Verify Generate is built around transparency. Every validation output is accompanied by a comprehensive metadata report, confidence and gap analysis, explainable AI reasoning, and a complete audit trail. Subject matter experts remain in control through a human-in-the-loop workflow that enables study-specific refinements while preserving full traceability.
Designed to integrate seamlessly into existing clinical programming workflows, Verify Generate helps biometrics teams reduce repetitive quality control work, improve consistency, and accelerate submission readiness while maintaining the transparency and regulatory confidence required for modern clinical development.
