AI Audit Precedent Search Agent

How Cassidy automates using AI

Step 1: Capture engagement context

The Workflow triggers when an audit senior submits a query, automatically capturing engagement details—entity, period, reporting framework, audit regime, materiality thresholds, and the specific issue (e.g., "MUS sample size for AP" or "Revenue cut-off for bill-and-hold").

Step 2: Apply applicability filters

Cassidy's Knowledge Base applies guardrails to restrict results by jurisdiction, framework, industry, and effective date—excluding superseded standards and flagging any content that doesn't match the engagement's regime.

Step 3: Retrieve from authoritative sources

The Agent searches across connected sources: firm methodology manuals, prior-year workpapers (sanitized), PCAOB/ISA standards, regulator staff guidance, and internal consultation memos—using semantic search tuned to audit terminology and assertion mappings.

Step 4: Extract and normalize evidence

Cassidy pulls quotable clauses with paragraph numbers, maps content to assertions (Existence, Completeness, Accuracy, etc.) and procedure types (TOC, TOD, substantive analytics), and tags each result with metadata for regime, cycle, and sample method.

Step 5: Synthesize with decision lineage

The Agent builds an audit-ready rationale trail: issue → applicable criteria → alternatives considered → judgment factors → selected approach → procedures → results → conclusion. Cross-references to workpapers, PBC items, and lead schedules are automatically populated.

Step 6: Deliver audit-ready outputs

Cassidy generates precedent-based memo templates, workpaper exemplars, annotated citation packs with permalinks, and traceability artifacts—formatted with sections aligning to documentation standards and ready for reviewer sign-off.