North of guesswork.
Done doesn’t always mean correct.
AI agents and automated workflows say the job is finished. We check whether the result is actually right, against the documents, rules and records it started from.
North of guesswork.
AI agents and automated workflows say the job is finished. We check whether the result is actually right, against the documents, rules and records it started from.
Compare what a workflow produced with what it should have produced, and trace recurring mistakes to their cause.
Available nowTurn approved examples into tests, so a new prompt, model or rule can be checked before it reaches customers.
In developmentMeasure cost and quality side by side when you are considering a cheaper model or a different setup.
In developmentA customer asks for 10 cartons of 24 items. We compare what should have happened with what the system recorded.
The order contains 10 items instead of the requested 240. Ten cartons of 24 were recorded as ten single items.
Sources: approved-requests#R-002 · approved-rules#PACK-24-v1 · receiving-export#E-002 · receiving-export#E-002/O-002
Download the full example reportA fictional example made to show the method, not a customer result.
We are taking on a small number of early projects. Each one starts small and is agreed in writing before any work begins.
A mistake that keeps coming back. We agree what “correct” means and how your data is handled.
A small sample, compared against your rules. Anything unclear stays open rather than guessed.
Written findings with sources and a practical next step, at a fixed price agreed upfront.