Built around consequential AI-assisted workflows.
These are our initial focus areas: financial services and regulated outsourced operations, plus the recurring need to reassess oversight after an AI system changes. They're examples of intended applications, not a claim that any named organisation is a customer.
AI-assisted complaints
An AI system recommends rejecting a customer complaint. A policy exception means the recommendation is incorrect.
Will the reviewer catch the exception?
Whether the reviewer checks the applicable policy and evidence closely enough to recognise a recommendation that looks routine but isn't, and challenges it before it's approved.
Outsourced customer operations
An AI system suggests the next action for a customer-service agent. The recommendation conflicts with a required escalation procedure.
Will the agent recognise the conflict and escalate appropriately?
Whether reviewers in an outsourced or regulated operational setting apply the client's escalation rules even when the AI's suggestion looks reasonable on its face.
Human oversight after AI changes
A provider updates an AI model. The kinds of mistakes it makes may change as a result.
Can reviewers still detect the new failure modes?
Whether reviewers who learned to catch the old model's typical errors can recognise a different kind of mistake the updated model introduces.
Not every organisation using AI alongside human staff needs this. A strong fit has a real oversight responsibility: a person expected to identify, challenge, correct or escalate inappropriate AI outputs, and a QA, Operations or Risk leader who needs evidence that review actually works.