AI in pharma and biotech.
We build AI in pharma and biotech for the teams that keep a product compliant: quality assurance, regulatory affairs, pharmacovigilance and the lab. Each build comes with the test evidence and change control your validation lead will ask for.
Where the work gets stuck
Controlled documents are hard to search
SOPs, work instructions, batch records and regulatory correspondence run into the thousands. Finding the effective version of the right paragraph takes time, and people rely on colleagues who remember where things are.
Quality records take long to write
Deviations, CAPAs and change controls need careful write-ups that pull from batch data, previous events and procedures. Backlogs build up in quality teams and slow batch release.
Lab and process data stays in silos
Instruments, LIMS, ELN and manufacturing systems each produce data in their own formats. Scientists export to spreadsheets to answer questions that a proper pipeline could answer daily.
What we build for pharma and biotech
Each one is scoped around your systems and rules, and each one keeps a person in charge of the decisions that matter.
Controlled document assistant
Staff ask questions about procedures and get answers drawn only from effective SOPs and work instructions, each with the document number and version. Superseded and draft documents are excluded from retrieval.
Deviation and CAPA drafting
An agent gathers batch data, similar past deviations and the relevant procedures and drafts the investigation write-up in your eQMS. The quality owner reviews, edits and approves the record.
Literature screening for pharmacovigilance
Journal articles and abstracts are screened for possible adverse events involving your products, with the relevant passages highlighted. A safety scientist assesses each flagged case and decides whether it is reportable.
Regulatory intelligence search
Guidelines, agency questions and past submissions become searchable by meaning, so regulatory affairs can find precedent across dossiers and markets. Results link back to the source document and section.
Lab and manufacturing data pipelines
Data from LIMS, instruments and MES flows into a structured store with lineage, ready for trending and reporting. Scientists stop assembling datasets by hand and the audit trail is kept intact.
Evals as validation evidence
We turn expected behaviour into eval sets tied to your user requirements, and run them on every change. The results become part of the validation package your QA team reviews.
Built around the rules
What we design for from the first week. These notes are general information and are not legal advice, and your legal and compliance people keep the final word.
GxP and GAMP 5
We follow a risk-based approach in line with GAMP 5: user requirements, risk assessment, testing traceable to requirements and change control. Your QA organisation keeps ownership of validation decisions.
Data integrity and audit trails
Records are attributable, time-stamped and kept with their history, following ALCOA+ principles. Where systems support US filings, we design electronic records and signatures around 21 CFR Part 11 expectations.
Clinical and patient data under the GDPR
Trial and safety data often contain health data. We pseudonymise where possible, keep processing in the EU and document flows for the data protection impact assessment.
EU AI Act
Most quality, regulatory and lab support tools fall outside the AI Act high-risk categories. The AI literacy duty in Article 4 applies to every organisation using AI, and the Article 50 transparency duties apply when a tool talks to people or generates content for them. We record intended use so the classification can be checked and revisited when scope changes.
Works with what you run
If a system has an API, a database, an export or an inbox, we can build on it. These are the systems common in this sector.
- Veeva Vault (QualityDocs, RIM, Safety)
- LabWare LIMS
- LabVantage
- Benchling
- MasterControl
- SAP S/4HANA
- Oracle Argus Safety
- Medidata Rave
- SharePoint and Microsoft 365
Where to start
SOP assistant for one site's quality team
An assistant that answers questions from effective controlled documents for a single site, delivered with a validation package sized to its risk. It shows how AI in pharma can be used under GxP before it touches batch records or safety data.
Talk it throughWhat it includes
- Connector to Veeva Vault or SharePoint
- Cited answers from effective versions only
- Eval set built from real QA questions
- Risk assessment and test evidence
Guides
AI vendor security questionnaire: what to ask before you buy or build
The questions to ask an AI vendor, or your own team, about data, model providers, access, logging, quality, incidents, GDPR and exit, and how to score the answers.
8 min read
AI readiness checklist: what to answer before your first agent goes live
The questions to answer before your first AI agent handles real work, from process and data to approvals, monitoring and ownership.
10 min read
Further reading
LLM evals: testing AI before every release
A practical approach to LLM evals: build a test set from real cases, combine code checks with model grading, and block releases that regress.
5 min read
How to evaluate RAG: retrieval metrics, faithfulness and golden sets
How to do RAG evaluation properly: separate retrieval from answers, check faithfulness claim by claim and build a golden set you can trust.
4 min read
RAG vs fine-tuning: which one do you need?
RAG gives a model your knowledge at answer time; fine-tuning shapes its behaviour. How to choose, when to combine them and what each costs.
4 min read
Common questions
Yes, when its intended use is clearly defined and its risk is assessed. Validation focuses on the process around the model: fixed versions, eval sets tied to requirements, human review of outputs and change control for prompts and models.
Yes. Veeva Vault and the common LIMS platforms have APIs we build against, and we respect their permission models. For older instruments or systems we use exports or middleware, which we check during discovery.
Your safety scientists do. The software screens and highlights, and every flagged item goes to a qualified person who assesses it in your safety system.
In the EU or in your own cloud environment, using model providers under contracts that exclude training on your data. Confidential R&D data can stay entirely inside your tenant.
We agree the validation approach with them at the start and produce the deliverables they need in their templates. They review and approve, and we fix findings before release.