AI/ML and GenAI system validation
Using AI in a GxP quality, manufacturing or lab process? Validate the AI used in regulated quality, manufacturing and lab systems. Assurance is scaled to intended use and process risk.
The challenges this solves
AI is already in our quality workflows
GenAI drafts documents and summarizes deviations, but nobody has validated it.
Accountability cannot sit with a model
AI output needs review and approval by qualified people, with audit trails.
The rules are new and still moving
GAMP 5 D11, draft Annex 22 and the EU AI Act all apply on different timelines.
What we deliver
- GenAI and LLM tools in quality: document drafting, deviation summaries, CAPA and complaint triage, RAG assistants
- ML in manufacturing and QC: predictive maintenance, visual inspection, process modeling, anomaly detection
- AI features in eQMS, LIMS and BI platforms, qualified against vendor evidence plus local configuration
- Data integrity for AI: training-data lineage, ALCOA++ for inputs and outputs, prompts and outputs as electronic records
- Model lifecycle monitoring: performance checks, drift, bias and retraining under change control
- AI governance: policies, classification procedures and review and approval workflows
- How we use AI in our own validation work: predictive analytics, test case generation from requirements, and risk-based test prioritization
4 steps, clear deliverables
- 01
Classify
Inventory AI use and classify each system by intended use and process risk.
You receive: AI system inventory and risk classification
- 02
Assess
Gap-assess against GAMP 5 D11, Annex 22 and your risk appetite.
You receive: AI validation plan and risk assessment (bias, drift, explainability)
- 03
Validate
Run right-sized testing, with human-in-the-loop controls and data integrity evidence.
You receive: Test protocols, assurance evidence and validation summary report
- 04
Sustain
Monitor performance and drift, and retrain under change control.
You receive: Monitoring plan and retraining change-control SOPs
Want to know what this would take for your systems? Start with a short scoping call.
Reducing CSV time with AI-powered predictive analytics during an IBM Maximo implementation
CSV timeline, a 30% reduction
Less manual test-case authoring
Critical post-deployment incidents
Field-level anomalies detected
Who we do this for
- Pharma & biopharma
- Biotech
- Medical devices
- CROs & clinical
Related services
Questions we hear most
Which guidance do you validate AI against?
GAMP 5 Second Edition Appendix D11, the ISPE GAMP Guide: Artificial Intelligence, draft EU GMP Annex 22 and the FDA CSA final guidance.
When do EU AI Act high-risk obligations apply?
Stand-alone Annex III systems: December 2, 2027. For AI in MDR/IVDR devices, the Digital Omnibus would move August 2, 2027 to August 2, 2028, pending adoption.
How does SQA Solution use AI in its own validation work?
We use predictive analytics on historical data to flag likely failures. We generate test cases from requirements and past testing, and prioritize execution by risk.
Who is accountable for AI-generated content?
Qualified personnel. AI output that touches specifications, records or batch decisions must be reviewed and approved by people, with audit trails.
Do you promise a regulatory outcome?
No. Our commitments are deliverable-level, with defined scope and timelines.
Related work
Make AI in your quality systems inspection-ready
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