Your AI Changes. Your Documentation Doesn't Keep Up.
Every model update needs a defensible answer to one question: how do you know it still performs safely? Most teams answer that with a blank PCCP template and a best guess at "acceptable performance criteria." When the model drifts, the paperwork doesn't notice until an auditor does.
The AI Finds What Might Be Wrong. A Person Decides What's True.
Speed is the AI's job. It scans every output, fast, and flags what it isn't sure about that's the gap most tools hide and we surface on purpose. Judgment stays human. A qualified reviewer makes the actual call, and that decision is what an auditor can trust, because a person can be held accountable in a way a model can't.
We also tell you what we don't know yet. Drift detection needs 30+ days of data. Pattern detection varies by domain. An evidence package that hides its limitations doesn't survive an audit so ours doesn't hide them.
From Measured Performance to Submission-Ready Evidence
Four steps, shown as a numbered list or 4-icon row:
-
Score - Every AI output is scored for Completeness, Consistency, and Validity.
-
Review - A human reviewer confirms or escalates uncertain scores, building a real audit trail.
-
Pattern - Confirmed findings become a wisdom pattern a traceable, reusable unit of evidence.
-
Evidence - Patterns are mapped directly into your FDA PCCP and ISO 42001 documentation.
Compliance That Starts From Evidence - Not Blank Forms
This is a real excerpt from a generated evidence package device details redacted. Every ISO 42001 control maps to a specific artifact in your quality data, not a checkbox someone filled in from memory. When an auditor asks "how do you know," there's an answer already attached.
Not a Black Box
Every compliance claim traces back to a specific, inspectable pattern your team can review, challenge, or reject. Nothing is auto-approved without a human in the loop.