250+
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.
0
Unsafe auto-approval defaults
Sensitive customer, legal, financial, and compliance flows stay reviewable until explicit approval gates are designed.
30d
Post-launch support window
Sprint handoffs include support, runbooks, and launch monitoring instead of leaving teams with a fragile prototype.
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.