AI for Audit & Accounting Firms
Review working papers faster. Going concern indicators and related party references are located and linked to source for the engagement team to assess.
Built for
Why Audit teams struggle today
The problems we see in every audit workflow.
- Audit working paper review across large engagements is partner-time intensive
- Going concern assessment requires reading all client documents
- Related party transaction identification is a manual exercise
- Mid-tier firms can't afford to build internally like Big 4 do
- Talent shortage means doing more with fewer staff
How DataStruct AI solves it
Purpose-built capabilities for audit workflows.
- Audit finding extraction with severity classification
- Going concern indicator detection with evidence collection
- Related party transaction flagging with source-linked evidence
- Internal control weakness identification
- Source-linked documentation for your ISA/PCAOB files
Audit use cases
The most impactful workflows DataStruct AI powers for audit teams.
Working Paper Review
Problem: Manager and partner review of working papers is the audit bottleneck.
Solution: Rule packs flag issues across working papers with source links, so partner attention goes to the flagged items first.
Impact: Faster reviews. Better quality. More engagements per partner.
Going Concern Assessment
Problem: Going concern indicators hide across financials, board minutes, and contracts.
Solution: Automated detection of GC red flags with evidence aggregation.
Impact: Going concern language located and linked to source for the engagement team's conclusion.
Related Party Identification
Problem: Identifying RPTs requires cross-referencing across multiple sources.
Solution: Extraction of related party references across sources, each linked to its passage.
Impact: Catch undisclosed RPTs. Stronger audit opinions.
Internal Controls Testing
Problem: Testing internal controls per ICFR/SOX requires sampling and documentation at scale.
Solution: Evidence located against control descriptions, with exceptions flagged by rules you define.
Impact: More efficient ICFR audits. Source-linked evidence trail.
Document types we handle
Questions you can ask
- ›“What audit findings were identified?”
- ›“Compare materiality thresholds across engagements”
- ›“What internal control weaknesses are documented?”
- ›“Identify going concern indicators”
- ›“What related party transactions are disclosed?”
Why Audit teams choose DataStruct AI
Evidence-cited AI
Answers cite the sources they rely on, and records the system could not confirm are shown separately for a reviewer to check.
Access control and audit
Workspace roles with granular permissions and an audit log on sensitive actions. Tenant isolation is enforced in the application layer.
11 industries
Pre-configured terminology, document types, and rule packs for your vertical.
See DataStruct AI in action
Tell us about your team, document volumes, and integrations. We'll put together a tailored package and walk you through the platform.