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Secure AI virtual data roomsAI for finance, built for the auditors.
Reconciliation, expense categorization, fraud detection, report generation, financial document review — engineered for finance teams that need accuracy, audit trails, and a clean answer when the regulator asks how it works.
The hours are real. The risk is real. So is the right answer.
Reconciliation eats two days a week. Expense categorization is a perpetual queue. Quarterly close is a 60-hour week for half the team. The instinct to automate is right; the historical tool stack just couldn't do it without dropping accuracy below what the controller would sign off on.
Modern AI changes that — but only with audit-grade controls, explainable outputs, and human approval where stakes are high. We build for the finance reality: every automated decision needs a paper trail your auditor would accept.
Where AI earns its budget in finance.
Bank ↔ ledger reconciliation
Multi-source matcher with LLM-assisted fuzzy matching; exception queue for human review. 8–15 hrs/week recovered.
Expense categorization
Auto-categorization, policy-violation flagging, auto-routing for approval.
AR / AP exception triage
Anomaly detection, dunning automation, escalation routing.
Report generation
LLM-assisted variance commentary with anomaly callouts on standard reports.
Forecast variance analysis
Auto-generated variance explanations grounded in source data.
Tax document extraction
Line-item extraction from invoices, receipts, K-1s, 1099s with classification.
What “audit-grade” actually requires.
- Per-decision audit logsEvery classification logged with inputs, model version, confidence, and timestamp.
- Human-in-the-loop for high-stakes actionsRefunds, large reconciliations, anomaly approvals all gated through a human approver.
- ReproducibilityDeterministic mode (temperature 0) so the same inputs produce the same output.
- Data residency & isolationCustomer financial data isolated per tenant; private LLM available for sovereignty.
- Explainability on demandPer-decision explanations when an auditor asks 'why did this match?'
Common questions.
Will auditors accept AI-driven reconciliation?
Can we use hosted models with financial data?
What about hallucinations on a financial system?
Will this replace finance headcount?
Related reading
Keep sensitive financial data in-house with a model you own.