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 demoThe traditional pattern treats validation as a separate project that starts once engineering finishes. For AI systems that change as they are trained and retrained, waiting until the end means reconstructing a history that was never captured in the first place, which is far harder than capturing it as the work happens.
Ask a team to explain, six months after the fact, exactly which requirement a given model change addressed, which tests ran against it, and who approved the result, and you will usually get a shrug and a scramble through Slack history and old tickets. None of that makes an audit trail, no matter how thorough the scramble is.
When traceability is computed from the systems a team already works in, pull requests, test runs, model versions, tickets, the evidence accumulates as a byproduct of normal work rather than a separate effort bolted on later. A release is then a matter of compiling evidence that already exists, not generating it from scratch under deadline pressure.
Traditional software has a relatively fixed release cadence, so reconstruction after the fact, while painful, is at least bounded. AI systems can shift continuously as they are retrained, so the gap between what happened and what got documented grows faster and is harder to close after the fact.
A short walkthrough of how GxPForge keeps traceability current as your team works, instead of reconstructing it before a release.
Book a demo