Services — Britt Biocomputing
Services

How I Work

Britt Biocomputing architects operational AI validation and governance frameworks for agentic and generative AI in pharma R&D. 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.

Context of Use Risk Tiering Eval Design Acceptance Criteria Oversight Model Monitoring Change Control

Find out where you stand

Before a regulator or auditor does.

The House of AI Governance

Free Download

Flagship guide + 25-point organizational readiness checklist. A five-layer model for building trustworthy AI in regulated science — from data governance through business ROI.

  • 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
Download the guide →

20-Minute Fit Check

Free

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 →

AI Validation Readiness Briefing

For teams that know they have gaps — and need a concrete starting point.

90-Minute Briefing + Written Assessment

$2,500

Your team. Your use cases. Your readiness score — mapped to the House of AI Governance. You leave with a written one-page readiness assessment, a prioritized gap list, and a clear recommendation for next steps — whether that's working with me or not.

  • 90-minute live session with your cross-functional team
  • House of AI Governance mapped to your specific use cases
  • Written one-page readiness assessment with scoring
  • Prioritized gap list and recommended next step
Book a briefing →

Turn your AI from "we use it" into "we can defend it."

Full engagements for organizations ready to build the evidence layer.

R&D Fit-for-Purpose Sprint

2–4 weeks

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 specification
  • Risk-tiered validation protocol
  • Error taxonomy (hallucination types, confidence miscalibration, domain-specific failure modes)
  • Acceptance criteria and test case design

Agentic & Multi-Agent Evidence Package

4–8 weeks

For organizations deploying agentic AI architectures — multi-step workflows, autonomous tool-calling, cross-system orchestration. I design the oversight model, build frozen-architecture validation, define escalation paths, and create the evidence package that covers both FDA and EMA expectations.

  • HITL/HOTL oversight architecture per context of use
  • Frozen-architecture validation (version-locked models, prompts, APIs, temperature)
  • Agent transparency and escalation architecture
  • Hallucination compounding analysis for multi-agent pipelines
  • Dual-jurisdiction evidence package (FDA + EMA + EU AI Act alignment)

GxP Validate-Launch

6–10 weeks

Full validation lifecycle for AI systems entering GxP-regulated workflows — R&D, CMC, Clinical, Quality, or Pharmacovigilance. Includes everything in the Sprint plus validation master plan, monitoring architecture, drift detection thresholds, and audit-ready documentation.

  • Validation master plan
  • Full IQ/OQ/PQ-equivalent protocol suite for AI
  • Production monitoring and drift detection architecture
  • Audit-ready documentation package

Fractional AI Validation Leadership

For organizations scaling from one use case to many.

Embedded Fractional Leader

Monthly retainer

I embed with your team as a fractional Head of AI Quality — owning the validation lifecycle across your AI portfolio, building governance frameworks, training your workforce, and translating between data science, quality, and regulatory teams. For organizations that need the role before they're ready to hire it full-time.

  • Ongoing validation oversight across your AI portfolio
  • AI governance framework development and maintenance
  • Cross-functional translation (data science ↔ QA ↔ regulatory)
  • Workforce training on AI oversight models and responsible use
  • Inspection readiness and regulatory horizon scanning

Bolt on to any engagement


CSA for Analytics Pipelines

URS → risk assessment → test scripts → traceability for NGS, PV, and HEOR data/compute lineage.

Human Oversight Design

Decision thresholds, escalation rules, reviewer checklists, and cognitive forcing functions to prevent rubber-stamping.

Vendor & Model Assessment

Rubric-based evaluations of hosted LLMs, embeddings, retrieval stacks, and agentic platforms — scored against your risk tier.

Regulatory Translation

I speak both Python and Part 11. Bridging data science, quality, and regulatory teams across FDA, EMA, and EU AI Act.

SOP Modernization

CSV/CSA alignment, monitoring-light SOPs, log-retention and access controls — updated for agentic and generative AI workflows.

Training & Enablement

Workshops for QA, IT, and R&D teams. "How to read the evidence" briefings for leadership and board stakeholders.

Tool Qualification

Risk-based Part 11/CSA checks for your validation platform — e-sig, audit trail, URS traceability, roles, and identity preservation.

QA / Validation Digital / IT R&D Informatics Data Science Regulatory Affairs CMC / Manufacturing Pharmacovigilance C-Suite / Board

Common questions


Do I need to do all of this for every LLM?
No. The rigor scales with risk. A literature search assistant needs less than a deviation categorization tool that feeds your CAPA system.
Can you work with our existing validation SOPs?
Yes. I map to GAMP 5 (2nd Edition) and CSA. If your team still files IQ/OQ/PQ, I provide a crosswalk.
What if we're just exploring?
Start with the Fit Check or the R&D Sprint. Get signal before committing to full validation.
Do you work with AI vendors or just end users?
Both. I help pharma teams validate third-party AI tools, and I support AI vendors preparing their products for life sciences customers.
What about agentic AI — multi-agent workflows, autonomous tool-calling?
That's the Agentic & Multi-Agent Evidence Package. Agentic systems introduce failure modes that single-model validation doesn't cover — hallucination compounding, tool-call transparency, permission scoping, and escalation architecture. I design the operational controls and evidence packages for these systems specifically.
How is this different from traditional CSV consulting?
Traditional CSV consultants validate software. I validate AI systems — which means accounting for probabilistic outputs, model drift, prompt sensitivity, and the human oversight layer. The validation methodology is fundamentally different when the system's behavior is non-deterministic.