Turn recurring failures
into lasting improvements.

Recurring failure rate0%Example data

Improvement identified2 sec ago

Refund failures share one cause: missing tool verification. A prompt change is ready for review.

Find the shared cause behind failed journeys. Draft targeted fixes, test them before release, and measure whether customers get better outcomes.

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Case study Case study

Finding the failure is only half the job.

Teams collect issues without a clear fix or owner. The same failures repeat, and nobody knows whether the last change helped.

Different conversations keep failing for the same reason.

Your team cannot tell whether the prompt, knowledge, or tools need to change.

Fixes ship without a clear link to customer outcomes.

Fix the root cause.
Measure the result.

01Prioritize

Find the changes worth making first.

Cluster failed journeys by root cause. Rank improvements by affected customers, resolution impact, effort, and risk so the team has a focused backlog.

Illustrative example

Journey analysis

Recurring refund failures point to one missing verification step.

Category breakdown

Prompt changes

Tool fixes

Improvement review

64% Validated36% In review

Recurring failures

151

Track the same failure after the fix

02Propose fixes

Turn human recovery into better AI behavior.

Use successful human resolutions to draft prompt and knowledge changes and identify missing tool or workflow capabilities. Your team reviews what changes.

Illustrative example

What should we change first?

Chat

Proposed fix for human review:

Cause: tool result not checked

Change: verify refund confirmation

Test: refund and timeout scenarios

Measure: resolution after release

03Verify impact

Keep the improvements that work.

Test proposed fixes in simulations, then monitor outcomes after release. Compare resolution, handoffs, customer effort, and regressions to confirm the impact.

Illustrative example