Do your people actually catch AI mistakes?
PayReality is building software that tests whether human reviewers can spot and correct AI mistakes before those mistakes affect real customers.
Using realistic simulated cases, known AI errors and expert-validated answers, PayReality is designed to help organisations test the effectiveness of the human oversight they rely on.
A human in the loop isn't proof that the control works.
An organisation may record that a person reviewed an AI recommendation without knowing whether that person would recognise a mistake. An AI recommends rejecting a complaint. The employee clicks approve. The approval is recorded. But the recommendation was wrong. Who tested whether the reviewer would have recognised it?
Don't just document the review. Test its effectiveness.
A reviewer was assigned.
A recommendation was presented.
A decision was approved or escalated.
An audit trail exists.
Whether the reviewer recognised an error.
Whether the recommendation was challenged.
Whether escalation was appropriate.
Whether an incorrect outcome was prevented.
Ordinary audit logs and QA aren't useless. PayReality is designed as a complementary control-effectiveness assessment, not a replacement for them.
Test the review, not just the AI.
One recommendation. Two very different outcomes.
Choose what the reviewer does and see how the assessment records it. Illustrative, not a customer result.
A customer complains about a fee charged after a billing error. The AI assistant recommends rejecting the complaint, citing the standard fee policy. What the AI's summary leaves out: an approved exception applies whenever the error originated on the company's side, as it did here. The recommendation is wrong.
"Reject the complaint. The fee was applied correctly per policy."
The reviewer sees the AI's summary, which doesn't mention the exception, and approves the rejection. A review happened. The audit trail shows a human signed off. But the mistake reached the customer anyway. The assessment records this as a missed detection, not a process failure.
The point isn't that either outcome is more likely. It's that a completed review and an effective one aren't the same thing, and the difference only shows up when you test it.
See what your reviewers catch and what they miss.
Did they spot the mistake?
Did they question or reject the incorrect recommendation?
Did they refer the case when the rules required escalation?
Did they prevent the wrong outcome from reaching the customer?
Not every case requires escalation. Some correct recommendations should be accepted. Scoring reflects the approved expected action for each case, never a fixed universal pass rate.
Tests designed around how your organisation actually works.
PayReality is designed around your approved policies, evidence, workflows and reviewer responsibilities, so assessments test the judgement your organisation actually depends on.
Your approved rules and exceptions.
What's available to support or challenge a recommendation.
The operational process and escalation requirements.
What each reviewer may approve, reject or escalate.
The AI's actual task and plausible failure modes.
How serious different mistakes actually are.
When policies, models or workflows have moved since the last test.
Your AI changed. Can your reviewers still catch its mistakes?
Changes to AI models, policies or workflows can introduce different failure modes. PayReality is being designed to help organisations reassess whether their reviewers recognise relevant mistakes after those changes.
The AI sometimes produces obvious calculation mistakes. Reviewers learn to check the numbers carefully.
The model is more accurate with calculations but occasionally misses a subtle policy exception. Can the same reviewers detect the new errors?
A fictional, illustrative example, not a documented incident. We don't claim every model update requires retesting, or that reviewer performance always worsens after one.
For teams responsible for making human oversight work.
Directly assess whether reviewers catch known errors, not just whether a review step occurred.
Identify where review processes may need investigation, training or process change.
Predefined criteria and documented test evidence to understand oversight performance.
Built around consequential AI-assisted workflows.
Test whether reviewers catch incorrect AI recommendations in complaints, claims and other consequential customer-decision workflows.
Assess whether agents recognise errors in AI-generated guidance where human review is a contractual, operational or risk control.
These are our initial focus areas, not proof that every organisation in them needs this today. We don't claim named financial institutions or BPOs as customers.
PayReality is an early-stage company developing controlled, expert-validated human oversight assessments. We're exploring pilot partnerships with organisations responsible for reviewing AI-assisted decisions.
Human oversight shouldn't be assumed. It should be tested.
Talk to PayReality about testing whether your reviewers detect and appropriately respond to AI mistakes.