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 develops and publishes structured frameworks for governing AI in GxP-regulated environments. The frameworks below operate together and extend the FDA seven-step credibility framework, ICH Q9(R1), and the GAMP® 5 / GAMP® AI Guide lineage into territory those standards do not explicitly address.
An integrated body of work
House of AI Trust™
The 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
Adapts 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
A framework for qualifying AI suppliers and inheriting validation evidence in regulated environments. Extends GAMP 5 supplier qualification into non-deterministic upstream components.
Read →Two-Dimensional Error Taxonomy
A 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
Working with Britt Biocomputing
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 the client's current posture.
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