AI bookkeeping is software that reads source documents, proposes the account code and tax treatment for each transaction, and matches bank lines to invoices, leaving a person to accept, correct or reject every entry before it reaches the books.
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The easy part of bookkeeping is already automated. Bank feeds and rules in Xero, QuickBooks, Sage or A3 code the recurring supplier and match the invoice paid to the cent. What fills a bookkeeper’s week is what is left: the receipt photographed at an angle, the invoice paid in three transfers, the refund netted against a new order, the supplier who appears under four names, the expense that is half personal.
That residue is where an agent is worth building, and it is also where a confident wrong answer does the most damage. A miscoded line is cheap to fix in the week it happens and expensive to find at year end. So the agent is designed to be checked quickly: every proposal carries the document it read, the rule or precedent it followed and how sure it is, and the review screen is built around accepting good proposals in one action and correcting bad ones in two.
receipt, invoice, statement or bank line
fields read, with the page region kept
code, VAT and match, from the client history
accept in one action, correct in two
through the ledger’s own API, logged
low confidence, new supplier or unusual amount
Before go-live the agent runs in shadow on a closed period: it proposes, the firm’s existing coding is the answer key, and we look at where it disagrees and why. Acceptance rate alone is a poor measure, because a reviewer under deadline pressure accepts too much. We also sample accepted entries afterwards and track how many were later reversed.
A category or a client goes live only once its proposals hold up on that sample, and new suppliers and unusual amounts always start in the held queue. Corrections are recorded as the firm’s own rules, so the system learns your chart of accounts rather than a generic one.
It replaces much of the data entry and the first pass at reconciliation, not the bookkeeper’s judgement. Someone still has to decide how an unusual transaction should be treated, notice when a client’s pattern changes and stand behind the books at year end. The agent clears the routine volume so that judgement is spent on the exceptions rather than on typing.
For a single small business, the AI features already built into Xero, QuickBooks or similar software are usually the right answer and cost far less than a custom build. A bookkeeping agent like this one is built for firms handling many clients, where documents arrive outside the bank feed and the firm’s own rules and review process need to be applied consistently.
Anything with a usable API: Xero, QuickBooks Online, Sage, A3 and Holded are the common ones we see. Integration through the official API matters, because it keeps the ledger as the record and means every posted entry carries the normal audit trail of the accounting software itself.
It searches for combinations of open invoices that explain a payment, using amounts, dates, references and the payer’s history, and presents the best candidates with the reasoning. A person confirms the match. Where no combination fits within tolerance, the line is held with a note rather than forced onto the nearest invoice.
What we built, what stage it is at, and a link to the live thing wherever there is one.
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We start with a closed period and your own coding as the answer key.
30-minute technical call · no deck