AI in pharma and biotech — GxP compliant

Assistant for research and regulatory documentation

Knowledge and documents

Problem: Searching research and regulatory documentation eats up the scientific team’s time

How it works: The assistant answers only on the basis of selected documents, with metadata filtering (project, version, status), and shows the sources of every answer

What you gain: Scientists get an answer with a citation from the right version of the document instead of searching repositories — and see straight away when the base lacks a basis

Standardising validation and quality reports

Quality and standardisation

Problem: Validation reports and quality documentation follow a repetitive template

How it works: Answers in a fixed structure (structured answers), identical for local and cloud models, and a central, versioned prompt register

What you gain: Every report has the same structure regardless of who prepared it and on which model — fewer QA corrections and easier validation

Searching charts, diagrams and tables

Knowledge and documents

Problem: Charts, diagrams and tables in publications cannot be searched

How it works: Advanced OCR describes images, charts and tables in a way the model understands, so they become searchable like text

What you gain: A result from a chart in a years-old publication can be found with one question, not by browsing PDFs

Comparing answers from several models

Quality and standardisation

Problem: Teams compare conclusions from different models by hand, copying answers between windows

How it works: The model council sends one question to 3–5 models at once, and a split view lets you compare the results and prepare a synthesis

What you gain: An interpretation of the result based on several perspectives in one view — without the risk that one model missed an important limitation

Sovereign AI for research data and IP

Security and sovereignty

Problem: Research results and intellectual property must not leave the laboratory

How it works: Queries go only to models running locally; in the variant cut off from the network (Sovereignty Level L4), the solution works offline

What you gain: The team uses AI on research data, and the organisation can document that no part of its IP left its infrastructure

Auditable, GxP-compliant analysis

Compliance and oversight

Problem: A GxP auditor asks who approved an analytical conclusion and on what data

How it works: An immutable, cryptographically signed log of every call with retention, and human approval gates for critical actions

What you gain: The answer for the GxP auditor is ready in the system: who, when, on what data and with which model — without reconstructing history from emails

Where may generative AI be used in a GMP environment?

The draft Annex 22 allows it in non-critical applications, under human oversight, and excludes it from quality-critical applications. Until the final text is published, working with documentation, summaries and search with citations is safe

How do you validate an AI tool and maintain data integrity?

By defining its intended use, testing it on reference data and keeping a full trail: who processed data, when and with which model (the WORM log in PROXY:AI)

How do you find out where employees are already using AI without your knowledge?

Through an inventory and a single gateway to models. Traffic routed through PROXY:AI shows who uses AI and for what