AI governance for probabilistic systems in regulated science
I make frontier AI systems inspectable, defensible, and audit-ready for GxP-regulated environments. Every engagement follows the same lifecycle — the depth scales with your risk.
Most "AI validation" today uses AI to accelerate traditional CSV. This is the inverse: validation built for the AI system itself — for probabilistic outputs, drifting validated state, and failure modes that sit outside what CSV machinery was designed to test.
For the past several years I've been inside the training loops of frontier models — executing high-complexity RLHF for scientific domains, finding the failure modes, and teaching the models to do better. Before that, I was a Validation Engineer at Kite Pharma (Gilead), owning GMP compliance for cell therapy manufacturing.
Most AI consultants offer generic strategy. I offer technical assurance — I don't just tell you a model works, I provide the audit-ready evidence required to deploy it in a regulated environment.
A five-layer model for building trustworthy AI in regulated science — from data governance through business ROI. The self-assessment maps your current readiness across all five layers; the control layer shows where governance turns into evidence.
- Five-layer governance architecture with layer-by-layer breakdown
- 25-point readiness checklist with scoring rubric
- FDA / EMA / EU AI Act regulatory timeline (2024–2026)
- Control layer deep dive — where governance becomes defensible
I'll walk through your current setup, tell you where you are on the validation lifecycle, and recommend the smallest next step that creates the most clarity. No pitch. Just a map.
Book a fit check →Most teams don't have a validation problem yet — they have a prioritization problem. In one focused session, I inventory every AI, LLM, and agentic workflow you have in production, in pilot, or on the roadmap, and risk-tier each one against regulatory standards and my own frameworks, mapped to the House of AI Trust™. You leave knowing exactly where to focus, and what "good" looks like, before spending a dollar on validation.
- Full inventory of your AI, LLM, and agentic workflows — live, piloted, and planned
- Each use case risk-tiered against regulatory standards and my own frameworks
- A written, prioritized use-case map: what to validate first, why, and what the first artifact for each must cover
- A clear on-ramp to the Build tier — or a defensible plan you can run yourself
Choose this if: you have one AI tool or LLM deployment in R&D, you need a validation strategy scoped to its actual risk, and you'd rather start with one done right than boil the ocean.
For a single AI tool or LLM deployment. I define Context of Use, build a risk-tiered testing protocol, develop an error taxonomy, and deliver an acceptance-criteria package your QA team can run with. You get a validation strategy scoped to the risk — not a 200-page template.
- Context of Use and intended-use definition
- Risk-tiered testing protocol and acceptance criteria
- Biomedical error taxonomy for the deployment
- QA-ready evidence package
Choose this if: your system chains multiple models, tools, or agents — where the output of one step becomes the input of another. Traditional validation assumes deterministic outputs at each stage. This is built for when that assumption breaks.
For multi-step agentic and LLM workflows where outputs aren't deterministic. Builds the evidence and oversight layer that regulatory standards and my own frameworks expect.
- Frozen architecture design — locked models, prompts, and tool versions
- Multi-agent error-propagation analysis
- Human-on-the-loop oversight framework
- Transparency and traceability architecture
Choose this if: an AI system is entering a GxP environment and needs to survive inspection — not just internal QA review. You need the full lifecycle through Monitoring and Change Control, packaged for an auditor.
End-to-end validation for an AI system entering a GxP environment — the full lifecycle, from Context of Use through Monitoring and Change Control, delivered as an inspection-ready package.
- Full-lifecycle validation across all seven stages
- Complete audit-ready evidence package
- Oversight, monitoring, and drift-detection design
- Aligned to your QMS and change-control
An embedded, ongoing partnership across your AI portfolio — governance, oversight, and inspection readiness held by someone who has done the validation and the model work.
- Ongoing validation oversight across your AI portfolio
- AI governance framework development and maintenance
- Cross-functional translation (data science ↔ QA ↔ regulatory)
- Workforce training on AI interaction, skill narration, and oversight models
- Inspection readiness and regulatory horizon scanning
On continuity: Engagements are structured so that frameworks, artifacts, and decision logs live in your QMS — not in my head. Your team owns the evidence trail from day one.
Talk about a fractional role →Not sure where to start?
Download the guide. Score yourself. If you land below 20, book the fit check — I'll tell you exactly which layer to fix first.
Download the House of AI Trust™ →