Validation assurance for probabilistic systems in regulated science.
Where reproducibility is bounded, the validated state is a moving target, and conventional CSV practices were not designed for the sources of risk these systems introduce.
Britt Biocomputing builds the validation evidence that holds anyway — the plans, risk assessments, supplier qualifications, and audit trails your QMS needs before an inspector asks for them. Grounded in published frameworks that extend the FDA seven-step credibility framework, ICH Q9(R1), and the GAMP® 5 / GAMP® AI Guide lineage.
If any of these sound familiar, this is the work.
We put an LLM in front of deviation triage — and QA is asking what the validation package looks like.
There's a defensible answer. It starts with context of use, not with the model.
Our AI vendor passed SOC 2. Our auditor still wants supplier qualification evidence.
SOC 2 doesn't cover silent model changes or inherited validation. VALID Trust does.
The model is frozen. So why does the system keep behaving differently?
The failure modes live in the harness — prompts, retrieval, tools, guardrails. Those drift, and they're validatable.
Named artifacts, not advisory decks.
- →Validation plans & protocolsScoped to context of use, model, harness, and human-in-the-loop.
- →Risk assessmentsBuilt on a published failure-mode taxonomy, mapped to ICH Q9(R1).
- →Supplier qualification packagesAI vendor qualification under the VALID Trust methodology.
- →Traceability & capture-layer designSo every AI decision is inspectable after the fact.
- →Validation summary reportsThe document your inspector actually reads.
Three ways to engage: fixed-fee exposure screens and prioritized risk assessments, scoped engagements that produce inspection-ready evidence, and a fractional AI quality lead for teams that need the role before they can hire for it.
Work is sized to the consequence of error and to the maturity of your current posture.
See the full services overview →An integrated body of work, applied to your system.
Every engagement runs on frameworks that are public and citable. Your auditor can read the methodology before they read your evidence.
House of AI Trust™
Five-layer governance architectureThe umbrella framework: organizes AI controls in regulated drug development across five layers — from foundational context-of-use definition through model credibility, composite system controls, monitoring, and human accountability. The other three frameworks below operate within or alongside the House.
Read the flagship guide →Probabilistic Validation Lifecycle
Seven-step execution modelAdapts the V-model to systems where reproducibility is bounded rather than absolute. Maps cleanly onto GAMP 5 lifecycle stages while extending them for probabilistic behavior, drift, and continuous verification.
Read →VALID Trust
Four-pillar supplier qualificationA framework for qualifying AI suppliers and inheriting validation evidence in regulated environments. Extends GAMP 5 supplier qualification into non-deterministic upstream components.
Read →Probabilistic Failure Mode Taxonomy
6 × 6 classification matrixA public framework for classifying probabilistic AI failures in GxP regulated drug development by both error type and origin, mapped against GAMP 5, ICH Q9(R1), and the FDA seven-step credibility framework.
Read →Latest writing
When a Check Doesn't Run
Capture must distinguish three states — passed, failed, and did not run — because a check that never fires leaves nothing behind, and a missing record reads as nothing wrong. Recording the expected check-set per decision, and tiering the response to capture failure by action class, closes the blind spot.
The Last Deterministic Thing: The Unified Capture Layer
Determinism doesn't have to live in the model if it lives in the record of what the model did — making replay a binary pass/fail criterion an inspector can execute without trusting the sponsor.
Beyond the Weights: Harness Monitoring as the Validation Leash
Freezing the weights does not freeze the system. A five-surface taxonomy of where the harness drifts — and why each one is a change control trigger.
Balancing the Equation: Why HITL Is a Variable, Not a Constant
Every other human carrying regulatory load in a plant is qualified before they are trusted with it. The AI reviewer is the exception.
Twenty minutes to find out whether this is your problem.
A scoping call is free and specific: what you've deployed, what your QA function is asking for, and what the evidence gap actually is.
Book a scoping call Or see the full services overview →