Know where your AI fails.
And why.


The AI marked the refund resolved. The customer returned because no refund was issued.
Monitor every customer-facing AI interaction. Connect agent actions to real outcomes, uncover silent failures, and see what needs to change.
A closed conversation is not a resolved customer.
Automation rates can look healthy while customers repeat themselves, abandon tasks, or ask a human to finish the job.
The agent reports success. The customer still needs help.
Failed tools, wrong answers, and broken handoffs go unnoticed.
Your team sees the failure, but not which change would fix it.
See the failed outcome.
Find the cause.
Measure resolution, not just activity.
Connect conversations, agent steps, and customer outcomes across chat, voice, and workflows. Find false resolutions, repeat contact, and avoidable handoffs.


Customers return after the AI reports a successful resolution.
Customers returning after AI support
Learn what the human did differently.
Compare failed AI journeys with successful human resolutions. Trace the gap to knowledge, prompts, models, tools, routing, or policy.
Why did the refund journey fail?
ChatHuman recovery reveals the gap:
AI: marked the request resolved
Tool: refund action never ran
Human: checked and issued refund
Fix: verify refund before closing
Give every failure a next step.
Group recurring failures by cause and customer impact. Send the evidence to the right owner and track whether the fix improves resolution.

Refund failures share a missing tool check. Review the affected journeys and route the fix to the agent owner.
Connect the agent to the customer outcome.
What it monitors
The full journey, including what happens after the AI responds.
What it detects
Failures that a closed ticket or a high automation rate can hide.
What your team gets
Evidence to choose the next change and measure its effect.
“Rulebase is AI for customer ops at fintechs. It watches every interaction, flags risk in real time, and works the follow-up.”