1
Assemble the evidence map
Connect each material statement to its supporting document, transcript section, screening result, wallet report or case note.
Senior AI reviewer
Read the complete case. Challenge what does not fit.
The reviewer operates across documents, transcripts, checks, wallet intelligence and case notes. It prepares the case within the firm’s own framework so the senior human reviewer can spend time on judgement.
Evidence
→ reasoning
→ reasoning
Documents
Transcripts
External checks
Case notes
The review job
A senior manager’s view of the whole file.
The reviewer works through the file in five stages, each producing analysis the human reviewer can trace, test and challenge.
2
Test consistency and completeness
Identify conflicting dates, ownership gaps, unsupported narratives, missing corroboration and differences between stated and evidenced activity.
3
Apply the firm’s framework
Prepare the analysis against the firm’s policy, customer type, risk appetite, required EDD and applicable regulatory requirements.
4
Run a separate challenge
A distinct challenge review examines the evidence without inheriting the first review’s recommendation, reducing the risk of simply repeating the original conclusion.
5
Present the decision ready case
The human receives material findings, unresolved questions, supporting evidence, policy context and suggested next actions. The human remains responsible for the decision.
What the reviewer produces
Structured work, not a block of AI text.
Every conclusion should be traceable to the file and clearly separated from the human decision.
Evidence map
What supports each material statement and where the source can be reviewed.
Material findings
What could affect the risk classification, approval, conditions or need for additional due diligence.
Contradiction record
Where answers and evidence conflict, together with the exact documents or statements involved.
Regulatory context
How the issue connects to the firm’s approved framework and relevant external material.
Challenge response
What the second review questioned, how the issue was resolved and what remains open.
Human decision record
The final outcome, rationale, conditions, reviewer identity and any second sign off.
AI governance
Useful because it is controlled.
The platform is designed to support accountable human decisions, not create autonomous regulatory outcomes.
Versioned output
Model, workflow and review versions can be associated with the resulting case record.
Source traceability
Material conclusions can point back to the evidence used to support them.
Human override
The reviewer can reject, amend or add to the prepared analysis and record the reason.
No autonomous approval
Approval, rejection, filing and client communication require authorised human control.
Detailed model evaluation, prompt injection controls, change management and test evidence are available during qualified procurement review.
Working session
Review a complex case with us.
We can show how the reviewer moves from evidence to material findings while keeping the decision and accountability with your team.
Fictional demonstration dataSecurity pack availableControlled pilot option