Generative AI: Life Sciences and Pharma

Generative AI in Life Sciences and Pharma

Generative AI that a QA team can sign off. We build writing, safety, quality and medical information assistants that cite their sources, keep a qualified person in the loop and come with the validation evidence your inspectors will ask for. Language models draft and organise; people with the right qualifications decide. Senior engineers own the design, AI coding agents carry the volume, and every line is reviewed by a human.

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Key highlights

The use cases that pay back first in pharma are regulatory and medical writing support, pharmacovigilance case intake, quality documentation and medical information.
Every output cites the source document, table or SOP it came from, so a reviewer can verify it in seconds.
Generative AI is placed in human reviewed, non critical roles, consistent with the draft EU GMP Annex 22, which excludes generative models from critical GMP use.
The model version, prompts and retrieval settings are treated as configuration items under change control, and the evaluation harness becomes validation evidence.
A proof of concept usually takes about 4 weeks; a validated production release takes longer and is planned with your QA team from the start.

Why pharma AI projects stall, and what is worth solving

Life sciences teams have plenty of documents for AI to work on. Projects stall at the same point: nobody can explain how a probabilistic model fits a validated, inspected process.

Validation nobody knows how to write. Computer system validation teams know how to test deterministic software. A language model that can phrase the same answer two ways leaves them without a test strategy, so the project waits for QA indefinitely.

Regulatory writing at document scale. Clinical study reports, CTD Module 2 summaries and briefing documents pull from protocols, statistical outputs and earlier submissions. Writers spend most of their time finding and reconciling sources, not writing.

Adverse event intake that never shrinks. Case information arrives by email, call centre notes, literature and partner reports. Safety teams key it into the safety database by hand against strict reporting clocks.

Deviations and CAPA that repeat. Deviation reports are written from scratch, similar past events are hard to find, and root causes recur because the history sits in free text inside the quality system.

Medical information answers that must be approved. Healthcare professional enquiries need answers from approved standard response documents. A generic chatbot that paraphrases freely creates off label and promotional risk.

Confidential data that cannot leave. Unpublished trial results, patient level data and manufacturing know how cannot go to a consumer AI tool. Without an approved hosting model, legal and privacy stop the project early.

Use cases

Generative AI use cases we build for life sciences

Each use case is grounded in your controlled documents and systems, tested on a golden dataset your experts approve, and reviewed by a qualified person before anything becomes a record.

Regulatory and medical writing copilot

RAG over protocols, statistical analysis outputs, prior submissions and style guides drafts sections of clinical study reports and CTD summaries. Every sentence links back to its source table or document, and a traceability report shows what the writer accepted or changed.

Pharmacovigilance case intake

Document extraction pulls patient, product, reaction and reporter details from emails, call notes and literature into ICH E2B(R3) fields, suggests MedDRA terms and flags possible duplicates. A safety specialist reviews every case before it enters Argus, ARISg or Veeva Vault Safety.

Quality and SOP agent

Drafts deviation, CAPA and change control records from investigation notes, finds similar past events, and answers operator questions from the current approved SOP version with a citation. QA approves every record in Veeva Vault QMS or your quality system.

Medical information assistant

Answers healthcare professional enquiries only from approved standard response documents and labelling, logs each enquiry, routes adverse events and product complaints to safety, and sends anything outside approved content to a medical information specialist.

Literature and evidence agent

Search agents over PubMed and ClinicalTrials.gov screen abstracts against your criteria, extract study details into structured evidence tables and support literature surveillance for safety. Reviewers confirm inclusion decisions.

Trial master file classification

Classifies incoming trial documents against the TMF Reference Model, extracts key metadata such as site, date and version, and flags missing or misfiled documents so the eTMF stays inspection ready.

How we deliver

From one document type to a validated service

01
Use case discovery and data readiness
With your business owner and QA we define intended use, GxP impact and risk, sample real documents and confirm the hosting model. Output: a use case brief, risk assessment and validation approach.
02
Evaluation harness and prototype
A golden dataset approved by your subject matter experts with acceptance criteria, then a prototype tested against two or three candidate models, including self hosted options, on accuracy, consistency and cost.
03
Guarded pilot with human in the loop
Run alongside the existing process so every AI output is compared with expert work. A qualified reviewer accepts or corrects each output, and the evaluation results form part of the validation evidence.
04
Production rollout with monitoring
Release under change control with the model version locked, audit trails on, ongoing performance monitoring against the golden set, and a defined revalidation path when a model or prompt changes.

AI governance for life sciences

Regulators are clear that responsibility stays with the company and its qualified people. Our job is to make AI outputs traceable, controlled and easy to verify.

GAMP 5 Second Edition FDA 21 CFR Part 11 EU GMP Annex 11 Draft EU GMP Annex 22 (AI) ICH E6(R3) GCP ICH E2B(R3) HIPAA and GDPR EU AI Act

Generative AI stays out of critical GMP decisions. The draft EU GMP Annex 22 on artificial intelligence, published for consultation in 2025, limits critical GMP applications to static, deterministic models and excludes generative AI and large language models from them. We place generative AI in non critical, human reviewed roles and document that classification for your QA team.

Validation follows intended use and risk. Following GAMP 5 Second Edition, which includes guidance on AI and machine learning, we define intended use, assess risk, lock the model version, prompts and retrieval settings as configuration items, and use the evaluation harness as repeatable test evidence. Model upgrades go through change control and are re run against the golden set.

Records, signatures and data protection by design. Where outputs become GxP records, Part 11 and Annex 11 controls apply: audit trails, electronic signatures and access control. Patient data is pseudonymised before prompts where the task allows, and models run under enterprise terms that exclude training, in your chosen region, or self hosted when data must not leave your network.

Models and tooling we use for life sciences AI

Portable language models with a self hosted path, retrieval over controlled documents, and adapters for the validated platforms you already run.

Claude, GPT-5 and Gemini Llama 4 and Qwen (self hosted) Azure OpenAI and AWS Bedrock LangGraph pgvector with versioned indexes AWS Textract MedDRA coding support ICH E2B(R3) XML Veeva Vault APIs Oracle Argus integration PubMed and ClinicalTrials.gov APIs Langfuse with retained traces

FAQ

Common questions

How much does a generative AI project in pharma cost?

It depends on scope and validation effort, so we quote from a written estimate rather than a price list. Most teams start with a fixed price proof of concept for one use case, then move to a validated production release or a dedicated engineer. Model API or GPU costs are passed through at cost. Our pricing page lists current entry points.

Can generative AI be used in a GxP environment?

Yes, in the right roles. Drafting, summarising, classifying and extracting with a qualified person reviewing every output is a common and defensible pattern. Making critical GMP decisions autonomously is not, and the draft EU GMP Annex 22 excludes generative models from critical GMP applications.

Is patient and trial data safe with an LLM?

Yes, when it is designed for. We use enterprise model APIs that contractually exclude training on your data, in a region you approve, pseudonymise patient data before prompts where possible, and can run open weight models on your own infrastructure when data must not leave your network.

How do you handle hallucinations in regulated documents?

By grounding, citation and review. The model drafts only from retrieved source documents, every statement links to its source, automated checks compare numbers against the source tables, and a qualified reviewer approves the output. Accuracy is measured on an expert approved golden set before release and monitored afterwards.

How long does a first pharma AI project take?

Usually about 4 weeks for a proof of concept. A validated production release takes longer, typically several months, depending on GxP impact and how your QA team runs validation. We plan the validation deliverables with QA in week one.

Should we buy an AI module from our platform vendor or build our own?

Use the vendor's AI where it covers the task inside one platform, such as within your safety database or quality system. Build where the task spans systems, where you need your own sources and style, or where data must stay on self hosted models. We often extend vendor platforms through their APIs rather than replace them.

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