Regulated documents, validated systems.
AI for Pharmaceuticals & Life Sciences
Life sciences carries the heaviest validation burden of any sector we work in. Systems must be qualified, changes controlled and every output attributable.
What makes this sector different
Validation is the schedule
GxP qualification often takes longer than the build.
Attribution is mandatory
Every generated output must be traceable to its inputs and the exact model version that produced it.
Literature volume outpaces reading capacity
Relevant publications appear faster than teams can review them.
Where it pays back
Regulatory document drafting support
Retrieval-augmented generationDraft sections from source data with full provenance and human sign-off.
Literature and evidence review
Retrieval-augmented generationScreen and summarise publications against inclusion criteria, citing every claim.
Pharmacovigilance case intake
HybridExtract structured adverse-event data from unstructured reports.
Batch record and deviation analysis
Classical MLSurface patterns across manufacturing deviations.
How the Pharmaceuticals & Life Sciences assessment differs
Life sciences assessments assume validated-system constraints and full generation provenance from the outset.
Compliance we ask about first
Data sources we expect
Questions this template pushes on
- Does this system fall in GxP scope?
- What level of generation provenance will your QA function require?
- Must inference remain inside a validated environment?
These are suggestions surfaced alongside the questions — never pre-filled answers. The assessment still asks you everything, because a score built on assumptions you never confirmed is not a score you could act on.
Scope an AI project for pharmaceuticals & life sciences
The Pharmaceuticals & Life Sciences template sharpens the questions. Ten minutes gets you a feasibility score, an architecture and a costed plan.