The problem with "AI did it"
Language models are very good at reading, summarizing and writing. They are not reliable calculators, and the same prompt can give slightly different answers on different runs. That's fine for a draft email. It isn't fine for a reconciliation difference, an accrual or a covenant ratio.
When AI produces a number and a reviewer can't see how, two things happen. The reviewer re-does the work to be safe, and the time savings disappear. Or the reviewer doesn't, and the risk moves to the audit.
Yoraito's rule: queries compute, AI explains
| Work | Who does it in Yoraito | Why |
|---|---|---|
| Matching, tie-outs, totals, variances, ratios | SQL queries, saved with each result | Deterministic: same data, same answer |
| Reading invoices, statements, contracts | AI, with rule-based checks on what it read | Language is AI's strength; checks catch mistakes |
| Explaining a variance or reconciling item | AI, citing the transactions behind it and labelled by support | Reviewers know which explanations to check first |
| Proposing entries | AI, for a person to approve | Nothing posts without approval |
| Approving, signing, locking | Your team | Accountability doesn't move |
This split is what we mean by auditable AI: you can see which parts were computed and which were written, and reproduce every number.
What Yoraito automates
- Bank and account reconciliations → /solutions/bank-reconciliation-software, /solutions/account-reconciliation-software
- The month-end close → /solutions/financial-close-software
- Invoice processing → /solutions/invoice-processing-automation
- Excel automation: schedules and workbooks rebuilt from source data with the logic visible
- Compliance checks: cut-off, duplicate, approval and segregation-of-duties checks run every close
All on your existing GL: 22 named systems, 11 direct.
Six questions to ask any AI accounting software
- How is each number computed? By the model, or by code you can see?
- Can my auditor reproduce a number without your product?
- What can the AI change in my ledger, and under what role?
- What does the reviewer see before approving?
- What happens when the AI isn't sure? Does it guess, or flag it for a person?
- Can we turn AI drafting off for a close and still run the process?
For a full RFP list → /blog/ai-accounting-software-rfp-audit-trail-questions
What an AI-drafted explanation looks like
| Account | Change | Draft explanation | Support | Label |
|---|---|---|---|---|
| 6100 Marketing | +$48,200 (31%) | Conference sponsorship, invoice INV-2291, $45,000, posted 14 Oct | Cited lines cover 93% of the change (query Q05) | Evidenced |
| 6400 Professional fees | -$31,500 (-38%) | September included a one-off audit fee accrual; no October equivalent | 81% coverage, no document (query Q06) | Likely |
| 5200 Hosting | +$27,940 (12%) | Drivers found cover only 44% of the change | Query Q07 | Needs owner |
Illustrative. The variance and coverage figures come from queries; the words come from AI; the label tells the reviewer where to look first.
Who it's for
| If you are… | Start here |
|---|---|
| A controller or CFO at a mid-market company | /solutions/financial-close-software |
| A PE-backed portfolio company | /solutions/pe-backed-companies |
| A VC-backed startup preparing for its first audit | /solutions/vc-backed-startups |
| A CPA or CAS firm | /solutions/accounting-firms |
AI accounting software vs AI bookkeeping
AI bookkeeping tools categorize transactions for small businesses. AI accounting software for finance teams runs the close: reconciliations, accruals, flux, review and reporting. If you have a controller, an auditor or a sponsor, you need the second. More → /blog/ai-bookkeeping-vs-ai-close
Governance built in
- AI drafting can be turned off per close
- Preparer and reviewer recorded separately
- Content in documents is treated as data, never as instructions
- Every AI-drafted item labelled by how well it's supported
For how this maps to SOX and COSO's 2026 generative AI guidance → /resources/sox-controls-for-ai