Find a better way
to review AI work.
We help teams test changes to how employees check AI-generated work: comparing the current process against alternatives, with representative cases and real reviewers.
The result is a recommendation backed by evidence about effort and quality, plus what it doesn't yet tell you.
"Hi, the headphones from my order arrived damaged. Could I get a refund for those, please? Thanks, Priya."
Hi Priya, we're sorry to hear that. We'll refund $103.00.
Case rule: a refund covers the reported damaged item only.
This demonstrates a review arrangement, not measured product effectiveness. PayReality tests arrangements like these using representative cases and real reviewers, measuring effort and final quality together.
Review takes time. Speed alone doesn't tell you whether it's working. You're deciding whether changing how your team checks AI work is worth it, based on evidence, not guesswork, and checking a correct answer isn't automatically wasted effort.
Controlled tests outside production. The customer decides whether to adopt anything.
Agree on a good result; prepare representative cases.
Both arrangements, same cases, outside production.
Effort and quality together; what the evidence supports.
Agree on a good result; prepare representative cases.
Both arrangements, same cases, outside production.
Effort and quality together; what the evidence supports.
We balance cases across review arrangements and control for familiarity and reviewer differences. The same reviewer doesn't simply repeat the same case in both conditions.
Comparisons we might test for your workflow.
Not customer stories; the alternative isn't presented as the winner. That's what the assessment is for.
Does seeing the evidence first help people catch mistakes?
An AI drafts a customer response. An employee checks it before it's sent.
The AI-drafted response.
The customer's order and request.
We'd compare review time and final answer quality, mistakes caught, missed or introduced, across both orders. We don't assume which one wins before testing it.
A tested recommendation for how the review should work.
Customer Refund Request Review
Assessment summary · sample structure, not a completed case
Review question
Framed as a single, testable question before any case is run, so the result answers something specific rather than a general impression of “did review go well.”
Arrangements compared
Both arrangements use the same case pool and the same reference answers, so the only deliberate difference between them is presentation order, not case difficulty or reviewer skill.
Effort and quality
- Reviewer time per caseMeasured during assessment
- Consequential errors caughtMeasured during assessment
- Consequential errors missedMeasured during assessment
- Errors introduced during reviewMeasured during assessment
- Corrections and reworkMeasured during assessment
Evidence limitations
What the case set did and didn't cover, and where the result would need more evidence before relying on it further for cases outside that coverage.
Recommendation
What the evidence supports changing, or a finding that the current arrangement should stay, if that's what the cases show. Not decided in advance.
What to check before adoption
What should stay unchanged, and what to verify before and after adopting any recommended change — a rollback point, not a one-way door.
The evidence may support keeping the existing arrangement, changing it, or collecting more evidence first. Not every engagement produces a redesign.
Already checking AI work, regularly?
- Already using AI-generated outputs, with regular human checking or approval.
- An identifiable workflow owner, and a meaningful quality standard.
- Cases and reviewer time potentially available for a controlled assessment.
Not every workflow is a fit, and that's fine.
What your AI produces, who checks it today, and what you'd like to improve.
Agreed scope, permitted cases, source information, a credible way to judge results, and reviewer time.
Access to the right workflow owner, and domain expertise to judge whether a result is correct.
Questions about the assessment.
Is this an approval tool, or will it remove our human approvals?
No. Our focus is testing and improving a review process; no changes to production controls happen automatically, and any recommendation stays subject to your decisions.
What if our current process already works well?
The assessment may support keeping it. We don't presume every workflow needs fewer reviews, and checking a correct answer isn't automatically wasted effort.
Is this available today?
We're seeking design partners to shape and validate the initial offering, not a generally available platform, and we don't yet have validated savings or fixed pricing.
What is a ‘reference assessment,’ and do we share sensitive data immediately?
A reference assessment is an independently judged answer or quality standard we compare results against; some cases have more than one acceptable answer. The first conversation only needs a high-level description, no confidential case material.
Does this certify compliance?
No. Findings concern the tested process and conditions, not a compliance certification.
Have an AI workflow people spend time reviewing?
Tell us what's being reviewed, who reviews it, and what you'd like to improve.