The method behind every system in these case studies. Users catch, we trace, the rule lands, the case is frozen forever.
AI systems drift. A fix that lives only in a prompt can be undone by the next edit, and nobody notices until a user does.
The bigger the system grows, the more places an old mistake can quietly come back.
A verification loop that turns user testing into a permanent asset. When a user catches a wrong answer, the figures are traced back to the exact rows that produced them. The cause is fixed at the layer that produced it, not patched over in a prompt.
Then the case joins an automated suite that runs on every version. Once a mistake is in the suite, it cannot quietly return.
140 checks at the start of user testing. 379 today. Not one fixed defect has recurred.
Every wrong answer a user has ever caught is still being checked on every version, and will be on every version to come.
The system gets safer every time it is wrong, which is the opposite of how most AI deployments age.
Want an AI system that holds up under real users? We build the verification loop in from the start.