Where generative AI fits, industry by industry.
- HealthcarePatient data rules decide where a model can run and what may be logged.
- FintechEvery automated decision needs an audit trail that a reviewer can follow.
- SaaSThe work ships inside a product, so latency and cost per request matter as much as accuracy.
- E-commerceCatalogs change daily, so retrieval has to stay current without retraining.
- LegalA wrong citation is costly, so answers need visible sources and human review.
- Enterprise softwarePermissions in the source systems must carry through to what the model can see.
What every industry shares
The details differ, but the same five questions come up in each. Each industry page lists the ones that matter most there. We have not published a case study in these industries yet; the case studies page lists the work we have.
Who can see what
Access rules in the source systems have to carry through to what the model can retrieve.
Where the data goes
Whether the model runs hosted or inside your own environment is decided before the build.
A record of each answer
Logging and audit trails let a reviewer see what the system relied on.
A person accountable
Wherever a wrong answer is costly, someone reviews the output before it counts.
Testing on real cases
A fixed set of your examples scores every change, so behavior cannot shift unnoticed.
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