Put AI to work in your regulated quality, manufacturing and lab systems — validated, governed and inspection-ready. We validate the AI you deploy, so it holds up to FDA, EMA and notified-body scrutiny instead of becoming a finding.
Why now
AI in GxP is no longer theoretical — it’s being enforced
The frameworks and the enforcement both arrived. If AI touches your specifications, records, batch decisions or quality workflows, it now needs the same lifecycle controls as any other GxP system — documented, risk-assessed and defensible.
April 2, 2026 — FDA’s first AI warning letter. The FDA cited a drug manufacturer for uncontrolled use of AI agents to generate specifications, procedures and master production records, applying 21 CFR 211.22(c): AI-generated content must be reviewed and approved by qualified personnel, and accountability cannot be delegated to a model. Uncontrolled AI in a regulated process is now a compliance concern on its own.
- GAMP 5 Second Edition, Appendix D11 sets the AI/ML lifecycle expectations — concept, project and operational phases.
- ISPE GAMP Guide: Artificial Intelligence (July 2025) elaborates the practitioner controls.
- EU GMP Annex 22 (draft) defines expectations for intended use, data governance and human oversight of AI in GMP.
- EU AI Act — high-risk AI obligations phase in by system type. AI in MDR/IVDR medical devices is on the latest track (originally Aug 2, 2027; the Digital Omnibus, provisionally agreed May 2026, moves it to Aug 2, 2028 pending adoption), with stand-alone Annex III systems at Dec 2, 2027.
- FDA CSA final guidance (finalized Sept 2025, updated Feb 3, 2026, QMSR-aligned) explicitly extends its risk-based framework to AI tools used in production and the quality system.
Scope
What we validate
From predictive analytics on the line to GenAI assistants inside your QMS — we classify each system by intended use and process risk, then right-size the assurance.
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GenAI & LLM tools in quality
Document drafting, deviation summarization, CAPA and complaint triage, RAG assistants — validated with human-in-the-loop controls and audit trails the inspector can trace.
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ML in manufacturing & QC
Predictive maintenance, visual inspection, process modeling, anomaly detection — with intended-use definition, model performance testing and drift monitoring.
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AI-enabled QMS & analytics
AI features embedded in eQMS, LIMS and BI platforms — qualified against the vendor’s evidence plus your configuration and use.
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Data integrity for AI
Training-data lineage, ALCOA++ for model inputs and outputs, retraining under change control, and treating prompts and outputs as electronic records.
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Model lifecycle & monitoring
Ongoing performance verification, drift and bias checks, retraining triggers and a defined state of control after go-live.
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AI governance & SOPs
Policies, classification procedures and review/approval workflows so AI adoption is governed by design, not by individual vigilance.
Deliverables
What you receive
- AI system inventory and intended-use / risk classification
- AI validation plan aligned to GAMP 5 D11 and Annex 22
- User requirements with model performance acceptance criteria
- Risk assessment covering data bias, drift and explainability
- Test protocols and scripted/unscripted assurance evidence
- Human-in-the-loop control design and audit-trail review
- Ongoing monitoring plan and retraining change-control SOPs
- Audit-ready validation summary report (inspection defensible)
Our standard: deliverable-level commitments with defined scope and timelines. We don’t promise regulatory outcomes — we give you defensible, inspection-ready evidence and the governance to sustain it.
How it works
From AI risk to a system that stays in control
STEP 01
Classify
Inventory AI use and classify each system by intended use and process risk.
STEP 02
Assess
Gap-assess against GAMP 5 D11, Annex 22 and your risk appetite.
STEP 03
Validate
Right-sized testing, HITL controls and data-integrity evidence.
STEP 04
Sustain
Monitoring, drift checks and retraining under change control.
Why SQA Solution
We build and operate AI under the same rigor we’d demand of yours
Current with the 2026 landscape
Our methodology reflects the FDA CSA final guidance (Feb 3, 2026, QMSR-aligned), GAMP 5 Second Edition and the Annex 22 draft — not last decade’s CSV playbook.
Risk-based, not paperwork-based
We focus assurance where patient and product risk actually sit, so you get defensible evidence without binders no one reads.
Named senior consultants
You work with experienced GxP validation practitioners who can defend the evidence to an inspector — not an anonymous offshore pool.
Pharma, biotech, device & CRO depth
15+ years validating regulated systems for life-sciences leaders including Roche and Genentech.
Free self-check
Find your AI-in-GxP gaps before an inspector does
A two-minute readiness check. Instant result, no sales call. See where your AI governance stands against the 2026 expectations.
Make AI in your quality systems inspection-ready
Tell us where AI is showing up in your regulated processes. We’ll come back with a scoped, fixed-fee path to validated, governed AI.
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