Getting models into production and keeping them there. Evals you trust, a cost and latency budget agreed up front, a human in the loop where it matters, and an honest list of the things we would rather not automate.
Built from your real cases, not a generic benchmark. If a change makes the eval score worse, that has to mean something is actually worse.
We decide what a request is allowed to cost and how long it is allowed to take before we pick a model, not after it is already live and the bill has arrived.
We design the review step before we design the automation, so the failure mode is a slower answer, not a wrong one that nobody caught.
We will tell you plainly which parts of the process should not be handed to a model yet, even if that is not the answer you came in hoping for.
A pilot works well in a demo and nobody can say why it sometimes fails in front of a real customer.
Inference cost is climbing faster than anyone budgeted for and nobody owns bringing it down.
Leadership wants "AI in the product" and engineering wants a plan that will not embarrass anyone in six months.