Part 0: Introduction and Series Map
This is the start of a six-part series. New parts will be linked here as they are published. Regulatory status in the series is stated as of September 2026 and will change. This series is a technical guide, not legal advice. Check the current status of any regulation with your quality and legal teams before you act on it.
This page is the beginning of a six-part technical series on architecting a Local AI Foundry for GxP-regulated life sciences. Future parts will be linked from the series map below as they are published.
Life-sciences organizations are adding AI to their products and their internal work. Most of them start one team at a time. One team calls a hosted model. Another picks a different model. A third builds its own retrieval pipeline. Within a year there are several integrations, several sets of prompts, and no single answer to a basic inspection question: which model, prompt, and documents produced this output, and who approved it?
This series describes a better way. A Local AI Foundry is one shared, self-hosted platform that provides models, retrieval, policy enforcement, evaluation, audit, and operations to every product, while keeping each product's data, permissions, prompts, and quality thresholds isolated. Product teams consume a small, stable API. The platform team owns the controls that inspectors will ask about.
The primary reader is the person accountable for standing up shared AI capability. The co-readers are the head of Quality or Validation, and the IT Security or Compliance lead.
The series assumes you know what a language model and a vector index are. It does not explain the basics. A short glossary comes at the end of the series.
Local means self-hosted, air-gap capable, and sovereign.
Regulated means pharma and medical-device rules first, other regulated industries second. The frameworks that shape the design are woven through every part — they are not an appendix.
A six-part technical guide. Parts will be linked here as they are published.
Part 1
The problem, the precise meaning of local, a map of the regulations and what each demands of the platform, the two-axis rule for classifying every AI use, six design principles, and how to qualify model suppliers.
Part 2
The control plane and execution plane, eight layers, and the request path that carries identity and authorization into retrieval before any text reaches a model. Includes the context diagram and layer diagram.
Part 3
Documents as governed data, ingestion, versioning, effective dates, hybrid retrieval, evidence thresholds, and citations, with ALCOA+ applied to the AI data path. Includes the data-flow diagram.
Part 4
How to validate a system that is not deterministic, risk-based assurance, intended use and context of use, the evaluation suite as evidence, change control for models and prompts, and continuous verification.
Part 5
The threat model, defense in depth, guardrails, staged adoption of agents, privacy duties, the EU AI Act in practice, and the registries and lifecycle rules that keep the platform under control.
Part 6
Hosting, cost and return, operations and disaster recovery, the team and operating model, a compact tooling map, and a production readiness checklist.
Every part carries its own numbered source list. The sources are regulator texts, standards bodies, official legal texts, vendor documentation, and peer-reviewed research.
Where a widely repeated claim could not be traced to a primary source, the series says so rather than repeating it.
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