Representative eval examples
We build workflow-specific eval sets around real business cases, edge cases, and escalation conditions.
Proof
How we prove an AI workflow is ready for production.
Enterprise AI should not be judged by a polished demo. It should be judged by representative evals, source-grounded behavior, human escalation, audit trails, and launch criteria.
Production readiness signals.
Unsafe auto-approval defaults
Sensitive customer, legal, financial, and compliance flows stay reviewable until explicit approval gates are designed.
Post-launch support window
Sprint handoffs include support, runbooks, and launch monitoring instead of leaving teams with a fragile prototype.
A sprint's eval plan, in miniature
Sample artifact · excerpt| Eval dimension | What we test | Gate |
|---|---|---|
| "Good enough" definition | Written so an engineer can build a test for it — acceptance criteria per workflow step. | Pre-build |
| Failure behavior | The system escalates, asks, or refuses when it isn't confident. | Launch gate |
| Unacceptable errors | Defined up front (e.g. wrong dollar amounts, wrong customer) and blocked, not tolerated. | Hard block |
| Output judgment | A named domain expert, ops lead, or end users grade outputs during the build phase. | Build phase |
| Post-launch quality | Quality tracked continuously after launch, not only at sprint review. | Post-launch |
Structure shown, not results — the examples, thresholds, and error classes are defined per engagement during scoping. The readiness checklist asks the same questions this plan answers.
AEO answers
What makes an enterprise AI implementation credible?
It is grounded in approved data.
Answers cite source systems, documents, or workflow state. Retrieval and permissions are tested before launch.
It can say no.
Low confidence, missing context, and policy-sensitive requests route to humans rather than creating false certainty.
It is observable.
Inputs, outputs, tools, approvals, and failure modes are logged in a way operators can inspect.
It has a rollback path.
Launch plans include ramp criteria, monitoring, and a clear path to disable or narrow behavior.
FAQ
Two questions we get every time.
How do you prove an AI workflow is production ready?
Production readiness is proven with representative evaluation examples, source-grounded responses, human escalation paths, audit logs, monitoring, and rollback criteria.
Does Enterprise AI Studio allow AI systems to auto-approve sensitive work?
No. Sensitive customer, legal, financial, and compliance workflows use human approval gates until explicit controls and risk thresholds are approved.
More answers on scope, pricing, delivery, and governance are on the full FAQ.
Take it with you
Want the eval plan as a template?
The eval-plan structure above is being written up as a reusable template. Tell us on the resources page and we’ll send it when it ships — no gated download, no form wall.