The AI impersonates a Big 4 external auditor and stress-tests your control before real auditors do. Surfaces deficiency risks, missing evidence, and a hardening roadmap — no other AI assistant offers this.

Describe one control — the AI performs a senior manager's planning-stage work: a PBC evidence-request list, separate ToD and ToE procedures with audit reasoning & pass/fail decision rules, an evidence-traceability matrix, contradiction detection, honest planning-confidence scoring, and a PCAOB inspection-readiness check. Conclusions stay ⏳ pending until evidence is evaluated — the workpaper never claims testing it hasn't done.

You found an exception in testing — now what? The AI classifies its severity (deficiency / significant deficiency / material weakness), reasons through root cause, drafts the finding in Condition–Criteria–Cause–Effect–Recommendation form, and proposes remediation, compensating controls, a management-response template and the ICFR aggregation impact. The finding write-up a senior spends an afternoon on — in seconds.

Upload your ERP security extracts. Your private backend runs a secure server-side rule engine (198 licensed SOD rules across SAP, Oracle EBS R12 and Oracle Cloud) to find access-level conflicts — the rule book and matching logic stay on the server and are never exposed to the browser. Your extracts are parsed in the page into a user→entitlement map, evaluated in memory on the server (not stored), and only the violations are returned. The on-device LLM then writes the risk narrative & remediation. Finally, upload your transaction/change logs to confirm which access conflicts became real transactional violations — same vendor created and paid, same document posted and approved, etc.