How do you handle GxP validation for AI systems?

AI in GxP contexts is validated as a computerised system — risk-assessed, functionally specified, IQ/OQ/PQ evidenced, with change control and periodic review. We work to GAMP 5 alignment and the specific expectations the MHRA has published for AI in regulated contexts.

Can AI operate inside a regulated decision?

In GxP contexts, agents typically draft and extract — qualified humans always approve. We design the approval gates and evidence trail so the qualified person's role is preserved, not eroded.

What systems do you integrate with?

The regulated-system stack — Veeva Vault, ArisGlobal, Oracle Argus, Medidata Rave, IQVIA platforms, and the common eTMF and CTMS platforms — plus the MHRA submission channels and EudraVigilance.

Can you work within our computerised system validation framework?

Yes. We expect to operate inside your CSV framework, not around it. Every deliverable fits the validation plan — user requirements, functional specs, test scripts, traceability — rather than being bolted on after the fact.

How do you handle patient data and ALCOA+ principles?

All processing defaults to inside your validated estate, patient identifiers are minimised or tokenised, audit trails preserve attribution and contemporaneity, and every system is designed to the ALCOA+ principles from day one.

How quickly does a pharma engagement show return?

Faster than most expect on ops metrics (cycle times, case throughput, MI response times) — 8 to 12 weeks on a focused pilot. Regulatory cycle-time improvements take longer because submission cycles are long.

How do you handle GxP validation for AI systems?

AI in GxP contexts is validated as a computerised system — risk-assessed, functionally specified, IQ/OQ/PQ evidenced, with change control and periodic review. We work to GAMP 5 alignment and the specific expectations the MHRA has published for AI in regulated contexts.

Can AI operate inside a regulated decision?

In GxP contexts, agents typically draft and extract — qualified humans always approve. We design the approval gates and evidence trail so the qualified person's role is preserved, not eroded.

What systems do you integrate with?

The regulated-system stack — Veeva Vault, ArisGlobal, Oracle Argus, Medidata Rave, IQVIA platforms, and the common eTMF and CTMS platforms — plus the MHRA submission channels and EudraVigilance.

Can you work within our computerised system validation framework?

Yes. We expect to operate inside your CSV framework, not around it. Every deliverable fits the validation plan — user requirements, functional specs, test scripts, traceability — rather than being bolted on after the fact.

How do you handle patient data and ALCOA+ principles?

All processing defaults to inside your validated estate, patient identifiers are minimised or tokenised, audit trails preserve attribution and contemporaneity, and every system is designed to the ALCOA+ principles from day one.

How quickly does a pharma engagement show return?

Faster than most expect on ops metrics (cycle times, case throughput, MI response times) — 8 to 12 weeks on a focused pilot. Regulatory cycle-time improvements take longer because submission cycles are long.

AI for Pharma & Life Sciences

Pharma and life sciences live in a document economy. Every decision — a clinical trial protocol, a regulatory submission, a pharmacovigilance signal, a medical information response — is a piece of carefully written text that has historically needed a qualified human to read, interpret and respond. Agentic AI, used with discipline, can take a lot of the reading and drafting off the qualified human without taking the judgement.

We work with UK pharma, biotech and medical device companies on exactly the workflows where agents can compress cycle time without touching the regulated judgement call. That means document intelligence, case processing, and operational automation — not decisions that require a QP signature or a clinical author.

Every engagement opens with an AI Readiness Assessment that maps your regulatory operations, clinical operations, PV system, and medical information stack — because in pharma the system is the process.

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Challenges We Solve

Regulatory operations are doc-heavy and bottlenecked

Preparing a CTA, MAA or variation submission consumes regulatory writers, labelling specialists and publishing teams for months. Cycle time is the single biggest controllable cost of going to market.

Pharmacovigilance case volume outpaces hiring

ICSR volume grows with indications, geographies and digital channels. PV case processing costs scale close to linearly with volume; the economics will break for most mid-cap pharma before 2030.

Medical information response times degrade

MI queries from HCPs and patients must be answered accurately and on time. Scaling the medical information function without compromising quality is a recurring operational headache.

Clinical ops data sits in silos

EDC, CTMS, eTMF and safety systems each hold part of the clinical picture. Study teams spend more time reconciling systems than running the study.

How AI Transforms Pharma & Life Sciences

Agentic Document Intelligence and Case Processing

We build agents that own specific life-sciences workflows end-to-end, with the sign-off gates pharma demands. A regulatory document agent extracts and drafts sections of CTD modules, variations, and clinical study reports — with a medical writer reviewing and approving every output. A PV case intake agent handles ICSR triage, duplicate detection, MedDRA coding and narrative first-draft, with case processors reviewing before submission. A medical information agent drafts responses against the MI letter library with referenceable provenance. See our agentic AI approach.

Learn more about our agentic document intelligence and case processing services.

AI-Powered Clinical and Safety Data Analysis

Clinical and safety data analysis is a regulated analytics problem — the statistical rigour is non-negotiable, but the data preparation and signal-detection surround is where AI gives real lift. We build pipelines that accelerate signal detection in PV, cross-study safety monitoring, and protocol deviation analysis, all with the validation posture and 21 CFR Part 11 / EU Annex 11 alignment the MHRA expects. Our data AI patterns preserve the computerised-system-validation posture rather than route around it.

Learn more about our ai-powered clinical and safety data analysis services.

MHRA, EMA and GxP-Aligned AI Governance

AI in a GxP environment is a qualified-system problem. Validation, change control, data integrity (ALCOA+), 21 CFR Part 11 / Annex 11 alignment, and the MHRA's evolving expectations for AI all land on the same validation plan. Our AI governance framework is built for that environment — IQ/OQ/PQ artefacts, change control discipline, and the audit trail an MHRA or EMA inspection will ask for.

Learn more about our mhra, ema and gxp-aligned ai governance services.

Demand, Supply and Enrolment Forecasting

Pharma forecasting problems repeat — trial enrolment projection, supply-chain risk forecasting against manufacturing capacity, launch-demand modelling. We build forecasting pipelines that layer onto your existing forecasting process rather than replace it, with explainability your finance and commercial teams can defend. Our applied AI forecasting work translates the same patterns across operational sectors.

Learn more about our demand, supply and enrolment forecasting services.

High-ROI AI Use Cases for UK Pharma and Biotech

Pharma tends to compound value from these clusters. Each is a pilot in 8-12 weeks inside an appropriate validation wrapper.

Regulatory operations: CTD module drafting, labelling comparison, variation impact assessment, regulatory intelligence monitoring.
Pharmacovigilance: ICSR intake triage, MedDRA coding assistance, narrative drafting, literature signal detection.
Medical information: MI query triage, response drafting against the MI library, adverse-event detection in inbound queries.
Clinical operations: protocol deviation detection, site performance analytics, eTMF completeness monitoring, query management automation.
Commercial and launch: HCP engagement intelligence, field-force insight generation, payer-evidence synthesis.

Each cluster lives inside its own validation wrapper. No shortcut.

Learn more about our high-roi ai use cases for uk pharma and biotech services.

Frequently Asked Questions

How do you handle GxP validation for AI systems?: AI in GxP contexts is validated as a computerised system — risk-assessed, functionally specified, IQ/OQ/PQ evidenced, with change control and periodic review. We work to GAMP 5 alignment and the specific expectations the MHRA has published for AI in regulated contexts.
Can AI operate inside a regulated decision?: In GxP contexts, agents typically draft and extract — qualified humans always approve. We design the approval gates and evidence trail so the qualified person's role is preserved, not eroded.
What systems do you integrate with?: The regulated-system stack — Veeva Vault, ArisGlobal, Oracle Argus, Medidata Rave, IQVIA platforms, and the common eTMF and CTMS platforms — plus the MHRA submission channels and EudraVigilance.
Can you work within our computerised system validation framework?: Yes. We expect to operate inside your CSV framework, not around it. Every deliverable fits the validation plan — user requirements, functional specs, test scripts, traceability — rather than being bolted on after the fact.
How do you handle patient data and ALCOA+ principles?: All processing defaults to inside your validated estate, patient identifiers are minimised or tokenised, audit trails preserve attribution and contemporaneity, and every system is designed to the ALCOA+ principles from day one.
How quickly does a pharma engagement show return?: Faster than most expect on ops metrics (cycle times, case throughput, MI response times) — 8 to 12 weeks on a focused pilot. Regulatory cycle-time improvements take longer because submission cycles are long.

Related thinking

Frameworks we apply on engagements like this

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