A promising automation works in a demo but fails unpredictably, hides what changed or cannot recover cleanly.
Solution / Fix an AI system that cannot be trusted
Fix an AI system that cannot be trusted
Find why an automation or AI prototype behaves unpredictably, then add the tests, controls and recovery it needs for real use.
HOW WE APPROACH IT
Start with the work, not the technology.
We map the current workflow, find where time or information is lost and define the first change worth testing. Predictable steps use rules; AI is reserved for genuine interpretation; important decisions stay with people.
A focused repair that reproduces the failure, adds realistic tests, records important decisions and provides a clear recovery path.
Your team can stop the system, see the first real failure and choose whether to retry, correct or roll back.
TRACES · GUARDS · EVALUATIONS · ROLLBACK · READBACK
Reproduce the real failure first
Expose the first broken boundary
Recover, retry or roll back safely
READY TO REMOVE A BOTTLENECK?
Discuss your workflow →