Industries
Healthcare & life sciences
Patient data that cannot move, clinical safety cases, and a hard line between supporting a decision and making one.
What makes this sector different
The clinical decision boundary
A system that routes and summarises is a different regulatory object from one that recommends. Keeping that boundary real requires design, not disclaimers.
Clinical safety cases
Safety argumentation needs evidence about failure modes, including omission, which is expensive to measure and easy to skip.
Patient data residency
Data protection and information governance requirements make on-premises or sovereign deployment the default rather than the exception.
Validation in regulated manufacturing
In life sciences, computerised system validation expectations shape release process as much as the software does.
Workloads that come up
- Clinical correspondence triage and routing
- Referral and letter summarisation for a clinician
- Coding and documentation support
- Literature and protocol retrieval
- Adverse event narrative drafting for human review
Frameworks in play
Two patterns we deploy here
Designed refusal boundary
Anything ambiguous or out of scope passes through untouched to the existing manual queue, not summarised with a caveat. The decline behaviour is tested as a first-class requirement.
Evidence-alongside-summary
Source passages presented inline with the summary so a clinician reads both, rather than trusting one over the other.
Start with the constraint.
Most of these projects are shaped by what you cannot do rather than what you want. Data that cannot leave the estate, a model you cannot host with a third party, a decision somebody has to justify to a regulator. Tell us yours and we will say honestly whether we can work inside it.