AI in Biotech and Life Sciences
Regulatory documents, literature and lab data, inside a validated environment.
Biotech has the strongest technical case for AI of any sector and the heaviest constraints on deploying it. The data is unusually valuable, the regulatory environment treats software in the quality system as a validated system, and the intellectual property is the entire company. All three point the same way: the model runs where you control it.
Book a free consultationWhat makes this hard
- Regulatory submissions run to tens of thousands of pages assembled largely by hand under deadline
- Relevant literature grows faster than any team can read, and missing a paper has real consequences
- Lab and instrument data is siloed by system, so cross-experiment questions cannot be asked
- Proprietary sequence, compound and process data is the company's entire value and cannot be sent anywhere
Where AI earns its place
Regulatory Document Assembly
Draft and assemble submission sections from source study reports and prior submissions, with every statement traceable to the document that supports it.
Submission assembly measured in daysLiterature Synthesis
Monitor and synthesise relevant publications and patents against your own programme areas, surfacing what changes a decision rather than everything that mentions a keyword.
Relevant findings, not a keyword feedLab and Experiment Retrieval
Ask questions across notebooks, instrument output and study reports in natural language, so an experiment run three years ago in another team is findable.
Cross-programme questions answerablePharmacovigilance Case Intake
Extract and structure adverse event narratives from unstructured intake, routing to a safety professional for assessment.
Intake backlog cleared, assessment untouchedFigures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.
The rules that shape the build
These are the constraints that decide the architecture, usually before anyone has picked a model. This is general information about the regulatory landscape, not legal advice on your obligations.
21 CFR Part 11 (electronic records and signatures)
Software creating or modifying GxP records needs validation, audit trails and change control. A model whose weights change under you cannot be validated, which is a decisive argument for self-hosting a pinned open-weight model.
GxP computerised system validation
Validation is against intended use, so scope the intended use narrowly and deliberately. A broadly-scoped assistant is far harder to validate than a system that does one defined thing.
Intellectual property and trade secret protection
Sequence, compound and process data is the asset. Sending it to a third-party model is a disclosure decision the board would want to have been asked about.
If it touches the quality system, it is a validated system
The distinction that decides scope in this sector is whether the AI sits inside the quality system or beside it. Literature synthesis and internal retrieval sit beside it and can move quickly. Anything producing content for a regulatory submission or a GxP record sits inside it and needs validation, change control and version pinning, which a cloud model that silently updates cannot provide. Senteras scopes that line first, because getting it wrong is discovered at inspection.
Where this applies
The same core systems, with the differences that matter in each setting.
- Pharmaceutical developers
- Regulatory document assembly carries the clearest return, measured against submission timelines rather than headcount.
- Medical device manufacturers
- Design history file assembly and post-market surveillance; ISO 13485 rather than GMP, same validation logic.
- Contract research organisations
- Study report drafting across many sponsors, where sponsor data separation is the hard requirement.
- Diagnostics
- Literature and evidence synthesis for assay development, plus regulatory submission support.
- Agricultural and industrial biotech
- Same retrieval and document patterns, generally outside the GxP validation burden.
Common questions
Does AI in a GxP environment need validation?
If it creates or modifies a GxP record, yes, and against a narrowly defined intended use. That is the strongest practical argument for a self-hosted open-weight model: you can pin the version. A cloud model that updates on the provider's schedule cannot be validated in any meaningful sense, because the thing you validated is no longer the thing running.
Can AI write regulatory submissions?
It can draft and assemble sections from source study reports with every statement traceable to its source. A regulatory professional reviews and owns the submission. The time saved is in assembly and cross-referencing, which is most of the work.
Will our sequence and compound data leave our environment?
Not in anything we build. That data is the company. We have not scoped a biotech engagement that ended anywhere other than on-premise or in the client's own tenancy.
Where should a biotech start?
Literature synthesis and internal retrieval, because they sit outside the quality system and can move immediately. Establish value there while the validation work for anything GxP-facing runs in parallel.
How we build it
Local & On-Prem LLM Deployment
The most powerful AI models, running entirely on your hardware.
Model Fine-Tuning & Integration
Models that speak your industry's language, trained on your data.
Custom AI Agents & Automation
AI that doesn't just answer questions. It gets things done.
Document Processing
Extraction, classification and validation across formats you do not control.
Internal Knowledge Base
Answers from your own documents, under your existing permissions.
AI Data Privacy
Keeping regulated data out of models you do not control.
Find out what this looks like for your organisation
A 30-minute call. We will tell you plainly whether AI is the right tool for the problem you have, including when it is not.
Book a free consultation