Every AI system has an error rate. There is no version of this that doesn't. The question that actually matters is whether the interface makes those errors cheap to catch - a wrong suggestion the user glances at and dismisses, not a wrong number posted straight into a ledger.
We build the evaluation harness before we build the feature, so "is this good enough to ship" has an answer that isn't a feeling.
What you get
An evaluation harness, before the feature
So shipping is a decision made against a measured baseline, not a guess.
Errors designed to be cheap to catch
The interface decides whether a mistake costs a glance or a ledger entry - we design for the glance.
Production systems, not demos
Real authentication, real authorization, real data models. The AI is one part of the system, not the whole pitch.
When this is not right
This is not right if a general assistant already does the job adequately. We would rather tell you that in the first conversation than build something bespoke you didn't need.
Built to be measured, not just demoed.
Let's scope the evaluation harness before we scope the feature.
Scope a build