AI Month-End Close

We will not give you a headline “close 60% faster” number, because it would be dishonest — the answer depends entirely on how far behind your ledger currently is and where your time actually goes. What we can tell you is where the compression comes from, and it is almost never the parts people expect. The judgement work — accruals decisions, the review itself, the sign-off — does not compress much, because it is analytical. What compresses is the upstream backlog: the AP that was sitting uncoded, the reconciliations nobody had prepared, the expense claims still in a glovebox, the approvals waiting on a manager who did not read the email. In most functions the close is long because the ledger was not current on day one, not because the close activities are slow. Getting the ledger current continuously is what moves the calendar, and how much it moves is a measurable thing we work out on your actual numbers.

It means doing the close work through the month instead of in a compressed burst after month-end, so day one starts from a current position rather than three weeks of backlog. In practice the AI codes transactions as documents arrive, prepares bank and supplier statement reconciliations continuously, keeps the exception queue current instead of letting it pile up, and surfaces accrual candidates from unmatched commitments as they appear. Nothing about this removes the formal close — you still have a cut-off, a review and a sign-off. What changes is that those activities operate on a ledger that is already nearly closed, so the event shrinks from a fortnight of catch-up to a short, calm confirmation.

It prepares them for your team to finish and approve. The AI matches what can be confidently matched — statement lines to transactions, supplier statements to entered bills — and assembles the reconciliation with the differences itemised and explained where it can. What it does not do is force a match it is unsure about or declare a reconciliation complete. Genuinely ambiguous items are set aside into an exception queue with the reason attached, and a person resolves them and signs the reconciliation off. That boundary is deliberate: a reconciliation is a control, and a control that quietly matches things to make itself look finished is worse than no control at all.

Your team, always. Accruals, provisions, the treatment of an unusual item, and the decision that the numbers are right and ready to report are professional judgements, and they are exactly the part of the close that should not be automated. What the AI does is remove the noise around them: it surfaces accrual candidates from commitments that have no matching invoice yet, flags prepayments against last period’s pattern, and lays out the information the decision needs — so your accountant is making the call from a prepared position rather than hunting for the inputs first. The judgement is human; the legwork before it is not.

Yes — this is built for a practice running many closes, not only for a single in-house finance function. Each client file is an isolated context with its own chart of accounts, history and rules, and the practice view shows which closes are on track and which have exceptions outstanding. For a firm, the bigger win is often consistency: the same close checklist applied to every file so quality does not depend on which staff member happened to do it, and a rolling view that stops the end-of-quarter pile-up where twenty files all need closing in the same week. Client data stays strictly separated, access is per staff member, and every action is logged.

It should strengthen your controls, not bypass them, and we would roll it out to prove that rather than assert it. The sensible approach is to run it alongside your existing process for a cycle: your team closes as they always do while the AI prepares in parallel, and at month-end you compare where it agreed, where it differed, and where it correctly refused to decide. That tells you whether to trust it before you depend on it. The controls that matter — the review, the reconciliation sign-off, the approval of the numbers — stay exactly where they are. The AI changes how prepared you are when you reach them, not who performs them.