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Automation & Integration

Reconciliation and month-end close, automated.

Two systems that each hold a defensible version of the same number, and a close that has to land on a date regardless. This is the process that makes them agree — matching, exception queues, scheduled runs, routed approvals, and notification the moment one breaks.

why they disagreenot this pageData Engineering & Analytics

Reconciliation loop — match, except, return Two sources — invoices and accounts payable, inventory and the general ledger — are read together and passed to a match engine, step 1. Matched items go straight to a resolved ledger, step 2. Unmatched items go to an exception queue, step 3. Reviewed exceptions return to the match engine, step 4. Which exceptions to re-match stays a human judgement call. Source AInvoices / AP Source BInventory / GL 1 Match engine matched 2 Resolvedledger unmatched 3 Exceptionqueue re-matched 4 Not automated: which exceptions to re-match stays a human call.
Two sources feed a match engine. What matches resolves straight through; what doesn't goes to an exception queue, gets a human decision, and feeds back into the next scheduled run — the loop, not a one-time reconciliation, is the point.Full step-by-step description

This is a familiar week for a lot of operations-heavy companies: an invoice total in the ERP doesn't match the one on the bank statement. A count in the warehouse system doesn't match the one in the general ledger. Nobody disputes that the numbers should agree — they just don't yet, and the days before close get spent chasing the difference by hand, in a spreadsheet that exists for exactly one week a month.

This is delivery history, not a pitch.

company delivery historyfounder-asserted

Invoice reconciliation, inventory reconciliation, and — in the founder's own words — "any reconciliation process and systems," alongside month-end close process automation, are work this firm has done. No client is named and no cycle time or match-rate figure is claimed here; neither is permissioned yet.

Any-system reconciliation

The pattern does not depend on which two systems are involved. It depends on there being two sources that each claim to be right, and a rule for what "agree" means between them. We have built this against invoice registers, inventory systems, and other systems of record, matched on the keys that are actually reliable rather than the ones that look reliable on a data dictionary.

Month-end close automation

The close is the same problem on a deadline: cut-off, accruals, the reconciliations above, consolidation, then the report — and every one of those steps either runs on a schedule or waits on a named person's decision, never on whoever remembers to run it. What we do not automate is the judgement calls: whether an exception is a real break or an acceptable rounding difference is a decision for a person, every time.

What the diagram above shows, in full: two systems hold their own version of the same number — Source A, an invoice register or an accounts-payable ledger, and Source B, an inventory system or a general ledger. They are read on the same schedule. The four numbered steps are the loop itself:

  1. Both sources are passed to a match engine, which compares them line by line on the keys agreed at the start.
  2. What matches resolves straight through to the resolved ledger, with no person touching it.
  3. What doesn't match goes to an exception queue, with a reason attached. A named person decides it — write it off, correct a source record, or escalate it. That decision is the one step in this loop that stays human.
  4. Reviewed exceptions return to the match engine on the next scheduled pass, so an exception still open when the schedule fires becomes a notification, not a surprise at close.

Where this page stops

This page owns the process that makes two numbers agree on a date — the matching, the exception path, the schedule, the notification. It does not own why the numbers disagreed in the first place, which is a modelling and lineage question forData Engineering & Analytics — see the rail note above. Splitting the work this way keeps the two silos from writing the same paragraph twice.

How the loop runs

  1. Bring the two sides together. Both systems' versions of the same number — an invoice register against a bank feed, an inventory count against the general ledger — read on the same schedule.

  2. Match what agrees, automatically. Lines that reconcile on the agreed keys resolve straight through, with no person touching them.

  3. Route what doesn't. A mismatch goes to an exception queue with a reason attached, not into a spreadsheet someone has to remember to check.

  4. Name an owner for every exception. A person decides it — write it off, correct a source record, escalate it. That decision is the one step in the loop that stays human.

  5. Notify on break. If a run fails or an exception ages past its window, a notification fires — the same notification-system pattern used across this silo — instead of the gap surfacing for the first time at close.

This is one part of Automation & Integration.

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