Pharma and Life Sciences

Assured AI for GxP environments.

Pharmaceutical manufacturing is the hardest environment in which to prove AI is safe and compliant. That is why we built AAP for it first.

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Why this market is different

Consequence, data integrity and a validation culture that already exists.

Consequence is direct and irreversible

An incorrect output that influences batch release, deviation classification or a specification decision can reach a patient.

Data integrity is a regulated discipline

ALCOA+ expectations apply to AI inputs and outputs, not only to the underlying records.

Validation culture already exists

The challenge is extending computerised system validation, ICH Q9(R1) and GAMP 5 thinking to systems whose behaviour is harder to specify and explain.

The regulatory frame we design for

Five layers apply at once.

Draft Annex 22 indicates that dynamic, probabilistic and generative models should not be used in critical GMP applications that affect patient safety, product quality or data integrity, while explicitly contemplating generative AI in non critical applications with a qualified human in the loop. AAP is built around that boundary.

Layer	Instruments
Horizontal AI law	EU AI Act
Sector quality regulation	EU GMP, Annex 11 and its proposed revision, draft Annex 22 on Artificial Intelligence, revised Chapter 4
Regulator AI guidance	FDA draft guidance on AI to support regulatory decision making; EMA reflection paper on AI in the medicinal product lifecycle; joint EMA and FDA principles of good AI practice
Quality and validation	ICH Q9(R1), GAMP 5 Second Edition, 21 CFR Part 11
Promotional compliance	ABPI Code of Practice and equivalent codes

Use cases

Where AI does useful work on the non critical side of the line.

Function	AI use	How AAP assures it
Quality Assurance	Deviation investigation preparation	Retrieves related deviations, SOP versions and specifications; the investigator classifies and owns the CAPA
Quality and Operations	SOP and knowledge retrieval	Answers grounded only in current approved versions, with provenance recorded
Regulatory Affairs	Regulatory intelligence and change impact	Comparisons and impact analyses checked and tiered for expert review
Validation	Document drafting and comparison	First drafts grounded and rule checked before entering controlled review
Training	Learning content generation	Content generated from approved sources and validated against encoded rules
Medical and Commercial	Assured AI assistants and avatars at congresses and events	Every response grounded in an approved content pack; off label claims blocked; clinical and adverse event enquiries excluded at platform level. See AI Avatars

A worked example

Move the AI one step back from the decision.

A quality team wants AI to support deviation investigations. The tempting version is a critical application. The defensible version moves the AI one step back. Most of the productivity benefit, a fraction of the validation burden, and a clear answer for the inspector.

✕ Critical · should not be used

"The AI classifies the deviation and proposes the CAPA"

INPUT

Deviation raised

Classifies and proposes CAPA

OUTPUT

Decision recorded

✓ Non critical · human in the loop

"The AI prepares, the investigator decides"

INPUT

Deviation raised

Retrieves history, SOP versions, spec

AAP

Records what was shown

HUMAN

Investigator decides, owns CAPA

Built for GxP first.

Book a pharma briefing and we will walk through your highest value, lowest consequence use case.

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