High-volume intake, reconciliation, reporting, and the month-end grind, handled by AI that stays accurate, shows its sources, and leaves a professional in control. Not a demo, systems already running inside a firm.
Accountants can't work with software that's confidently wrong. In a numbers-critical, regulated profession, an assistant that invents a figure is worse than no assistant at all.
So we build for the opposite. Every output is linked back to its source — the document, the transaction, the schedule it came from. A professional signs off where it counts: the AI drafts, a human decides. And in a multi-client practice, entity scoping and access controls keep each client's data walled off from the next.
That's what makes AI safe to actually use in a firm, rather than a demo that impresses once and never touches a real ledger.
Each one built into how your practice already works, not a generic tool you bend to fit.
Categorisation that learns from your own history: models trained on a firm's past transactions suggest GL codes and keep coding consistent across clients. Reconciliation flags the anomalies worth a human's eye instead of burying them, so routine data entry becomes a review step.
Invoices, bills, receipts and statements read and turned into structured, attachable data, mapped to your chart of accounts and vendor rules. One consistent intake pipeline instead of manual typing, with every figure traceable back to its source document.
The last mile drafted for you: a first-pass narrative explaining what moved and why, kept source-linked to the underlying numbers. Fully editable and versioned, so the audit trail survives every edit.
Work allocated by capacity, close-checklist statuses updated automatically, and month-end bottlenecks surfaced before they bite. Built with firm-grade controls: role-based permissions, granular audit logs, and exception handling.
Assistants that answer "what changed in cost of sales since last quarter?" with an evidence-linked explanation, and draft client summaries that cite the transactions behind them. Strict access controls and entity scoping keep every client's data separate and every answer inspectable.
The extraction and reporting above already run for a large accounting firm, across dozens of clients. See how →
Proof beats promises. Here's one of these running for real.
Every month, the firm built a report for each client by hand, from source material scattered across email and WhatsApp as PDFs, receipt photos, spreadsheets and forwarded documents. We wired an AI pipeline into their email and WhatsApp that reads it as it arrives, extracts what matters, and assembles a structured monthly report per client in the firm's own format.
It runs in production today, across dozens of clients, at the high accuracy the work demands. The team now reviews the reporting instead of assembling it line by line, so the accountants spend their time on judgement, not collation.
Yes. Multi-client work is built on strict entity scoping and role-based access, so one client's data never leaks into another's, and every response is attributable back to its source.
Always. The AI drafts, extracts and suggests; a professional reviews and decides. In a regulated profession that human sign-off is not optional, so we design for it rather than around it.
No. Messy, scattered intake — email, WhatsApp, photos of receipts — is the normal starting point, and structuring it is part of the work. We tell you upfront how much of that a given workflow needs.
No. It removes the collation and data-entry load so your people spend their time on judgement and client relationships, the parts that actually need an accountant.
Reef TRH