Software to design, run and evidence a human oversight assessment.
PayReality is developing software to test whether human reviewers can spot and correct AI mistakes before those mistakes affect real customers, using realistic simulated cases and expert-validated answers.
PayReality is early-stage. The stages below describe what the software is designed to do. We verify and state plainly what's already functional versus still in development before any pilot conversation goes further than this page.
Assessment setup
You identify the AI-assisted workflow to test and agree the scope: reviewer roles, error categories, expected actions, severity levels and success criteria, all defined before any results exist.
Organisational context
You provide approved policies, procedures, evidence requirements, escalation pathways and examples of known AI failure modes. PayReality uses this to support realistic scenarios; it doesn't invent policy on your behalf.
Scenario generation
PayReality uses AI to propose realistic synthetic cases, mixing correct and deliberately incorrect AI recommendations, so reviewers can't simply assume every case contains an error.
Expert validation
Non-negotiable: your authorised experts approve, reject or revise every scenario before it's used, including the expected answer, the planted error and the severity classification. Disagreements aren't silently resolved by AI.
Reviewer assessment interface
Reviewers see a simulated case, the supporting evidence and the AI recommendation, then accept, challenge, correct, request more information or escalate. Standalone and controlled: nothing here reaches a live system or a real customer.
Detection, challenge, escalation and correction
Each response is scored against the approved expected action for that specific case, across four dimensions, never collapsed into one score and never assuming every case needed escalation.
QA reporting
Errors caught and missed, by category and severity, reviewer response patterns, and possible areas for training or process investigation.
Risk and Assurance evidence
Scope, pre-agreed criteria, scenario validation records, source policy references, results, limitations and version history, structured for an assurance audience.
Repeat testing
Intended to support reassessment after a model, policy or workflow change, without rebuilding everything from scratch each time.
See the product in context
Walk through a demo and discuss whether a controlled pilot assessment fits your workflow.