A national retail chain received a flood of supplier invoices across dozens of branches. Each one was checked against its purchase order and delivery note by hand, so approval took up to two days, overcharges and duplicate bills slipped through into payment, and the finance team spent the month keying numbers instead of catching errors.
Approving a supplier invoice meant a person pulling up its purchase order and delivery note, comparing the quantities and prices line by line, and only then releasing it for payment. Across thousands of invoices a month, that ran to as much as two days each.
Those manual checks were the only thing standing between the business and an overcharge. When a supplier billed for more than was delivered, priced an item above the agreed rate, or sent the same invoice twice, catching it depended on whoever happened to be reviewing that day, and plenty slipped through into payment.
The cost was double. Money leaked out on charges no one caught, and the finance team spent the month on data entry and chasing paperwork instead of the work that actually needed judgement.
Let AI read the invoice and check it against the order and the delivery note, so a person only touches the ones that do not add up.
A document-understanding model reads each supplier invoice whatever its layout, and extracts the supplier, line items, quantities, prices and totals into structured data, instead of a person retyping them from a PDF or a scan.
The system automatically compares every invoice against its purchase order and the recorded delivery note, confirming that what was billed matches what was ordered and what actually arrived, right down to the line.
When the invoice, the order and the delivery note agree within tolerance, the invoice is approved straight through to the company's ERP for payment with no human touch, so the routine majority never sits waiting in a queue.
When something does not line up, a short quantity, a price above the agreed rate, a duplicate of an invoice already paid, the system holds that invoice and routes it to the right person with the discrepancy highlighted, so review time goes only where it is actually needed.
Nothing contested is paid on its own. Every flagged discrepancy is decided by a member of the finance team, and any invoice the model reads with low confidence is held for a person to confirm before it moves.
Each new invoice is checked against everything already billed and against the agreed prices, catching duplicate submissions and inflated charges that manual spot-checks missed, and recovering money that used to leak straight out.
The routine invoices clear themselves, and the errors get caught before the money leaves.
A matching invoice now clears in under three minutes instead of up to two days, straight through to payment. The finance team stopped building its month around manual data entry, and suppliers started being paid on time, which strengthened the terms the business could negotiate.
Duplicate submissions and inflated charges that manual spot-checks used to miss are now caught before payment, recovering money that used to leak straight out and giving finance a clean, real-time view of exactly what the business owes.
Invoices get overpaid because no one can check every line by hand, and the errors only surface once the money is already gone. The value here is an AI that reads every invoice, matches it against the order and the delivery note automatically, and releases only what agrees, with a person owning every exception, so an overcharge is caught before it is paid rather than written off after. It is the same document-capture and reconciliation discipline behind our bookkeeping automation work, aimed at supplier invoices rather than the ledger.
An accounting firm's team burned days keying in invoices and reconciling ledgers by hand. AI document capture plus a learning categorisation engine cut month-end close 40% and manual entry 80%, and freed 25% more client capacity with no extra admin headcount.
Read the case → Procurement & spend · ManufacturingSpend sat in four ERPs and buying power was fragmented. AI unified and categorised all spend, read every contract against its invoices, and armed buyers with hard targets, cutting indirect spend 12%, recovering leaked money, and speeding contract approvals 40%.
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Reef TRH