Enterprise AI

What separates enterprise AI from individual use is not the model but the layers around it: who can reach what, where an answer came from, how consumption is metered, and what can be shown in an audit.

None of those layers relate to model quality. Even the best model cannot be used in an enterprise setting without access control, because the real risk is not a wrong answer but a right answer to the wrong person.

An enterprise AI evaluation is therefore a governance evaluation, not a model comparison.

The first layer is identity and role management. KobiGPT defines four roles — super_admin, admin, manager, and viewer — and those roles determine which assistant can be reached.

The second layer is scope separation: each assistant sees only the documents attached to it. This is the actual mechanism enforcing access control, working together with the role model.

The third layer is traceability. Showing which document passage produced each answer is what makes audit questions answerable; with a fine-tuned model that is not possible.

Enterprise requirements do not belong exclusively to large organisations. A 40-person company in a regulated sector also needs access control and auditability; what differs is budget and deployment capacity.

That grey area is where product selection most often goes wrong. Enterprise products are too heavy for this profile, while consumer products lack the necessary governance layers.

A sound evaluation starts by writing requirements down individually and separating the genuinely mandatory ones. Is SSO required, is audit logging obligatory, is data residency in the contract? Once those answers are clear, the option set narrows.