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.

Give it the blank document, a completed example of the same document for a different subject, and the evidence. It reads the evidence, fills the blanks in the template’s own file — every sheet, every section — and cites the evidence behind each value. The completed example is used for shape and phrasing only: its names, dates and findings belong to another engagement, and anything that leaks from it is caught and held back. Gaps stay gaps. Nothing is uploaded.

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.

Assess an organisation against India's Digital Personal Data Protection regime from its own evidence. Conclusions are produced by deterministic predicates, not by a language model: the same evidence gives the same result every time, and every result cites the evidence fact behind it. Read the scope limitation before using the output. The DPDP Rules text could not be retrieved from an official source, so no statutory obligation can currently be tested — controls whose standard is a statute report NOT TESTED, never a pass. What this does test today is internal consistency: whether the notice matches what is collected, whether withdrawal stopped processing, whether retention matches policy.

Statutory testing activates by importing the official instruments. Use Import legal instrument below to supply the Gazette PDF text for the DPDP Act, the DPDP Rules and their commencement notifications. Each import is hashed, and a named person must attest that they compared it against the official source before it becomes law inside this tool — the system cannot verify its own legal sources, because everything it could compare them against is the same document. Once verified, the AI can propose obligations from the text; each one still needs a person to accept it.

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.