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 copilot drafting a document, summarizing a batch record, or suggesting a next step is still a system influencing a GxP outcome. The fact that a human reviews the output before it becomes official does not remove the need to understand what the tool is doing and how reliably it does it.
A common assumption is that if a human signs off on the final output, the tool generating the draft carries no validation burden. That is not quite right. A tool that consistently omits a certain type of detail, or introduces a subtle pattern of errors, can slip past a reviewer who has grown to trust its drafts, especially under deadline pressure.
Ask how the tool was tested against the specific document types your team uses it for, not just general capability claims. Ask whether the tool cites where a claim in its draft came from, so a reviewer can actually verify it rather than taking it on faith. Ask what happens when the underlying model changes.
Human review is a real control, but only if the reviewer has a reasonable chance of catching an error. A one line skim of a fifteen page draft is not meaningful review. The workflow around the copilot matters as much as the copilot itself.
A short walkthrough of how GxPForge keeps traceability current as your team works, instead of reconstructing it before a release.
Book a demo