Validation services built for the
complexity of GxP AI

AI System Qualification

End-to-end GxP qualification for AI systems — from User Requirements through Performance Qualification — using our AI-extended V-model framework.

Our qualification methodology extends the pharmaceutical V-model with AI-specific documentation layers: AI Performance Requirements (APR), model training data lineage, algorithm documentation, and Ongoing Performance Monitoring plans. Every document is built to satisfy FDA CSA, Annex 11, and PIC/S requirements.

We classify each AI system by GxP impact — from direct batch release AI requiring full IQ/OQ/PQ through indirect scheduling systems requiring partial qualification — and right-size the validation effort accordingly.

Audit-Ready Architecture Design

We design and implement the agentic AI infrastructure required to satisfy regulatory inspection — immutable audit trails, human-in-the-loop gates, electronic signature, and access controls.

Most AI systems are not built with regulatory inspection in mind. When an inspector asks to trace a batch release decision, or a deviation investigation requires reconstructing every AI action that touched the record, the underlying architecture either supports it or it doesn't. We ensure it does.

Core architectural components

• ✓ Immutable audit trail — append-only log for all GxP-record-touching AI actions
• ✓ Human-in-the-loop (HITL) gates — enforced at batch release, OOS, protocol deviations
• ✓ Electronic signature binding — 21 CFR Part 11 §11.50 compliant
• ✓ Data integrity controls — input/output hash verification
• ✓ Role-based access control with least privilege enforcement
• ✓ Disaster recovery documentation and testing

Continuous Validation Monitoring

Validated AI is not a one-time event. FDA's CSA "critical thinking" standard and EU GMP Annex 11 (2011; revision in progress) both require ongoing performance monitoring. We operationalize that requirement.

Our Ongoing Performance Monitoring (OPM) service implements statistical process control for AI key performance indicators, automated drift detection with configurable thresholds, and revalidation trigger logic built into the change control workflow.

AI Change Control

Every change to a validated AI system — parameter tuning, prompt revision, model replacement — requires assessed regulatory impact. We build the change control workflow into your existing quality system.