See what a validation package looks like when it builds itself
A short walkthrough of how GxPForge keeps traceability current as your team works, instead of reconstructing it before a release.
Book a demoAn AI system that touches a GxP record or decision needs the same underlying proof as any other computerized system, evidence it does what it is meant to do. What changes is that the system's behavior can shift over time as a model is retrained, which the traditional validate once approach was never built to track.
Traditional validation assumes a system's logic is fixed once it passes testing. A rules based tool behaves the same way in year three as it did on day one unless someone changes the code. A machine learning model does not offer that guarantee. Retraining on new data can shift its outputs even if nobody touched a line of code.
A defensible approach treats each meaningful model update as a change requiring its own assessment, not a one time validation event. That means version control on the model itself, a record of what training data changed, and a way to demonstrate the updated model still performs within acceptable bounds on the cases that matter most.
Ask any AI vendor how they detect and document a shift in model behavior, and what happens to your validation status when they retrain. A vendor without a clear answer is asking you to trust a black box that even they cannot fully characterize after the fact.
Scrutiny should scale with risk, the same way GAMP 5 scales traditional software. An AI feature that drafts a first version of a document for human review carries less risk than one that autonomously approves a batch release.
A short walkthrough of how GxPForge keeps traceability current as your team works, instead of reconstructing it before a release.
Book a demo