Zero licence cost, total flexibility, works offline. No audit trail on edits, no drill-down from a number to its source voucher, and every reconciliation is rebuilt by hand each year — the most common source of peer-review and NFRA documentation findings.
Structured working-paper templates and some automation, but often slow to update for current standards, limited real-time collaboration, and rarely offer the same-input-same-output reproducibility a peer reviewer now expects of any automated computation.
Powerful for dashboards and workflow tracking, but not purpose-built for Indian statutory audit mechanics (SA-compliant documentation, CARO 2020 annexure, Form 3CD, Schedule III) — usually needs heavy configuration to fit an Indian CA firm's actual engagement structure.
The newest category. The AI-in-audit pillar's six-question table (below) is exactly how to separate tools that compute figures deterministically from books, with drill-down and reproducibility, from tools that generate plausible-looking numbers a model made up.
Take these to any vendor demo — including ours. A “no” on any row is a finding waiting to happen.
| Ask | Why it decides |
|---|---|
| Same input, same output? | Re-runs must reproduce byte-identically — the peer-review and NFRA test. |
| Who writes the figures? | Amounts should be computed from the books, never generated by a model. |
| Can every number drill to a voucher? | Auditors trust vouchers, not indicators. A metric with no drill-down is an assertion, not evidence. |
| Where does the data live? | India hosting, no training on client data, DPDP-compatible processing. |
| Does the file export and lock? | Working papers must survive outside the tool — locked, dated, reviewable. |
| Does it suggest or decide? | Good tools surface and explain; the conclusion box belongs to the auditor. |
CORAA is built as our answer to this table — deterministic computation from the books, AI narration on top, every number drilling to its voucher, hosted in India. See the AI Modules or start free: your first audit is on us.
Broadly four: spreadsheet-based manual workflows, legacy on-premise/licensed audit software, generic GRC or BI platforms adapted for audit use, and the newer category of AI-native audit tools built specifically for Indian statutory/tax/internal audit mechanics. Each has genuine trade-offs — there is no universally "best" answer independent of firm size, client mix, and what a firm is optimising for.
The Standards on Auditing are technology-neutral — nothing prohibits machine assistance. What matters is reproducibility and evidence: a peer reviewer or NFRA inspector will ask whether the same input reliably produces the same output, whether figures are computed from the books (not generated by a language model), and whether every number drills down to a source voucher. Tools that fail these tests create documentation risk regardless of how good the AI narration looks in a demo.
Bring the six-question table on this page to any vendor demo. A confident "no" — or a dodge — on reproducibility, figure provenance, drill-down, data residency, file portability, or the suggest-vs-decide boundary is a finding waiting to happen once the tool is actually in use on a real engagement.
