Auto Reconciliation
A reconciliation engine that ingests invoices, ledger entries, and bank movements, matches them with auditable rules, and surfaces only the exceptions that genuinely need human judgement.
Reconciliation is one of the most expensive recurring tasks in any finance function. The work is mechanical for the majority of records and unforgiving in the long tail of mismatches. Most teams handle it with spreadsheets, manual matching, and a steady tax of errors discovered late by an auditor.
The organisation in this engagement was running monthly closes that consumed senior accounting time on tasks that could be specified as rules.
Focus AI built a reconciliation service with two layers. The first is a deterministic rules engine: configurable matchers (exact, fuzzy, currency-aware, tolerance bands) over the imported records. Whatever matches at this layer is auto-cleared with a full audit trail.
The second layer reads invoice PDFs that arrive without structured data. An extraction model pulls amounts, vendors, and references; the rules engine then attempts to match. When the match is ambiguous, the case is queued for human review with all the context and a one-click resolution.
The surface area for human work shrinks to the irreducible exceptions: cases where judgement is genuinely required.
The monthly close stopped being a recurring multi-day event. The reconciliation engine clears the bulk of records continuously as data arrives, and the exception queue is short enough to be drained in a normal working session.
Because the rules are explicit and versioned, the audit trail improved as a side effect. The team can answer "why was this matched" with a single click. Freed-up time has been redirected to forecasting and analysis, which were previously squeezed by the close cycle.
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What's next?
Every project starts with a focused session discussing your bottlenecks. No slides, no fuss. We listen, understand, and execute on the issue.