Find a better way
to review AI work.
We help teams test changes to how employees check AI-generated work. Using representative cases and real reviewers, we compare the current process with alternatives, measure time and mistakes, and recommend what the evidence supports changing.
This initial offering is still being shaped and validated with design partners.
Read the draft and the evidence. Decide for yourself.
A short, fictional customer-service case. Everything you need to judge the answer is here, nothing is held back.
"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've gone ahead and processed a refund of $103.00 for your order. Let us know if there's anything else we can help with.
This demonstrates a review arrangement, not measured product effectiveness. PayReality tests arrangements like these using representative cases and independent reviewer groups or another suitable study design, measuring effort and final quality together.
Review takes time. Speed alone doesn't tell you whether it's working.
You're deciding whether a change to how your team reviews AI-generated work is worth making, based on evidence about effort and quality rather than guesswork. Checking an answer that happens to be correct is not automatically wasted effort, and we don't assume we already know which changes would help your workflow.
Where does human review catch important mistakes?
What makes review take longer than necessary?
Which changes improve the final result for the effort involved?
How the assessment works.
Controlled tests outside production, on one real review workflow. The customer decides whether to adopt any recommendation.
Agree
Agree on the workflow and what a good result means.
Prepare
Prepare representative cases from that workflow.
The review process as it runs today.
A selected change to that process.
Compare
Run both arrangements against the same cases, outside production.
Measure
Measure effort and final quality together.
Recommend
Recommend what the evidence supports changing.
Agree
Agree on the workflow and what a good result means.
Prepare
Prepare representative cases from that workflow.
The review process as it runs today.
A selected change to that process.
Compare
Run both arrangements against the same cases, outside production.
Measure
Measure effort and final quality together.
Recommend
Recommend what the evidence supports changing.
Comparisons we might test for your workflow.
Not customer stories. These illustrate the kind of comparisons an assessment might test, selected for the workflow, with no result implied.
Does seeing the evidence first help people catch mistakes?
An AI drafts a customer response. An employee checks the response before it is sent.
The employee sees the AI draft first, then checks it against the request.
The employee sees the underlying customer evidence first, then the draft.
We would compare review time and the final answer's quality (mistakes caught, missed or introduced) across both arrangements. We do not assume which arrangement wins before testing it.
Effort and quality, together.
Measures are selected for the workflow and available evidence. An approval click is not treated as proof that someone understood the material, and recording what was displayed does not by itself prove what a person actually read.
A tested recommendation for how the review should work.
Decision tested
Which review decision was compared, for example whether evidence order changes mistake detection for a defined case type.
Arrangements compared
The current arrangement and the one tested alternative, stated precisely enough to repeat.
Effort and quality measures
Measured during the assessment: reviewer time, consequential errors caught, missed and introduced, and rework.
Evidence limitations
What the cases did and didn't cover, and where the result would need more evidence before relying on it further.
Recommendation
What the evidence supports changing, or keeping the current arrangement if that's what the evidence supports.
What to check before adoption
What should stay unchanged, and what to verify before and after adopting any recommended change.
Sample structure, not a completed customer assessment. The evidence may support keeping the existing arrangement, changing it, or collecting more evidence first. Not every engagement produces a redesign.
What participation actually looks like.
A first conversation
A high-level description of what your AI produces, who checks it today, and what you'd like to improve.
Before an assessment starts
Agreement on scope, which cases are permitted, what source information is available, a credible way to judge results, and reviewer participation.
What we'd need from your side
Access to the right workflow owner, and the relevant domain expertise to judge whether a result is actually correct.
Agreed before work begins
Scope, participation effort, data handling and commercial terms, all agreed up front, not discovered partway through.
Already using AI, with people regularly checking its work?
We're looking for a workflow owner who can discuss the process and explore access to suitable cases and reviewer time. Not every workflow is a fit, and that's fine.
- A workflow already using AI-generated outputs.
- Regular human checking, correction or approval.
- An identifiable workflow owner.
- Cases and reviewers potentially available for a controlled assessment.
- A meaningful quality standard.
As part of the measurement approach, we'd also want to track what was presented for review, what changed during review, and the version approved, where that can be observed. This supports how an assessment is measured, not a claim that production tracking or enforcement is already available.
Questions about the assessment.
Is this an approval tool?
Our initial focus is testing and improving a review process. An assessment does not require replacing the customer's approval platform.
Will you remove our human approvals?
No changes to production controls or required approvals happen automatically. Any recommendation remains subject to the customer's requirements and decisions.
What if our current process works well?
The assessment may support keeping it. We do not presume that every workflow needs fewer reviews, and checking an answer that happens to be correct is not automatically wasted effort.
Is this available today?
We are seeking design partners to shape and validate the initial offering. This is not a generally available platform, and we don't yet have validated savings or fixed pricing.
What is a 'reference assessment'?
An independently assessed answer or quality standard we compare results against. Some cases have more than one acceptable answer, and we account for that rather than forcing a single right answer.
Do we need to share sensitive information immediately?
No. The initial discussion can use a high-level description. Data requirements and handling would be agreed before any assessment, and no confidential case material is needed to start the conversation.
Does this certify compliance?
No. Findings concern the tested process and conditions, not a compliance certification.
Have an AI workflow
that people spend time reviewing?
Tell us what is being reviewed, who reviews it and what you would like to improve.