Validation frameworks for probabilistic systems in regulated science — where reproducibility is bounded, the validated state is a moving target, and where the real sources of risk sit outside what traditional CSV machinery was built to test.

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.

01 / Frameworks

An integrated body of work

Flagship Framework

House of AI Trust™

Five-layer governance architecture

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.

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L3 is where Britt Biocomputing operates.
Four threads run through every layer: Security · Explainability · Communication · Supplier Qualification

Supporting frameworks

Britt Probabilistic Validation Lifecycle

Seven-step execution model

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.

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VALID Trust

Four-pillar supplier qualification

A framework for qualifying AI suppliers and inheriting validation evidence in regulated environments. Extends GAMP 5 Chapter 7 and the GAMP AI Guide supplier-qualification treatment for non-deterministic upstream components.

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Probabilistic Error Taxonomy

Two-dimensional matrix · 6 classes × 6 origins

Classifies probabilistic AI failures by manifestation (fabrication, misinterpretation, contextual misapplication, miscalibration, boundary violation, bias) and origin (training data, retrieval, inference, interface, orchestration, supplier).

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02 / Insights

Recent publications

JUN 2026

Context is King: Why AI Validation is Driven by Validity Context

The shift from CSV to AI validation is not a methodological upgrade but a different organizing principle. Validation now means credibility for a context of use, not conformance to a specification.

MAY 2026

A Two-Dimensional Error Taxonomy for Probabilistic AI in GxP

The first public framework for classifying probabilistic AI failures in regulated drug development by both error type and origin, mapped against GAMP 5, ICH Q9(R1), and the FDA seven-step credibility framework.

MAY 2026

VALID Trust: A Four-Pillar Framework for AI Supplier Qualification

Extending GAMP 5 supplier qualification into non-deterministic upstream components, where inherited validation evidence and silent vendor changes both fall outside traditional supplier audits.

Working with Britt Biocomputing

AI visualization and risk-tiering assessments, framework co-design for AI governance, and credibility planning for probabilistic systems in GxP contexts. Work is sized to the consequence of error and to the maturity of the client's current posture.

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