When every customer sends their remittance advice - the note explaining what a payment covers - in a different layout, adding people just buys you more typing, not faster books.
How we got a global testing and certification provider out of manually processing customer payment notices.
Payment Notices
Mostly Auto
Read without manual typing
Finance Capacity
Freed
Off repetitive data entry
Peak Volumes
Absorbed
Month and year-end, no new hires
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A quick translation first. A remittance advice is the note a customer sends explaining which invoices their payment is covering. Reconciliation is matching that payment to the right open invoices. Cash allocation is how fast that matched money shows up as 'paid' in the books. The team doing this work is Accounts Receivable, or AR. These are the people responsible for collecting and applying customer payments. With that out of the way: this global testing and certification provider was processing a huge volume of customer payments by hand, because every customer formatted their remittance advice differently. Simple 'look for this keyword' automation didn't work. AR staff had to read each note, type the payment details out, and then go hunt for the matching invoices. The backlog spiked at month-end and year-end, which slowed how quickly money was recognised in the books and forced the team to chase invoices reactively instead of staying ahead of them.
We built a document-reading pipeline on UiPath (a software robot platform) using its Document Understanding tool and a pre-trained machine learning model designed specifically for remittance advices. The bot opens each incoming document, finds the payment details no matter where they sit on the page, and extracts them. When the model is confident, the data flows straight into invoice matching. When it isn't, the same bot hands that one document to an AR specialist for a quick check. This is called human-in-the-loop, meaning a person only touches the edge cases instead of every document. The cleaned-up data then drives invoice matching, so the team reviews exceptions rather than the whole queue.
From Robbie's desk
A short, no-fluff read from our VP of Growth on the bottlenecks worth watching, the AI execution patterns that are actually shipping, and the numbers behind them.
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Recruitment
Processing Time
< 5 Mins
Emergency Services
Auto-Cleared
60%
Retail
Payback Period
< 1 Month
Most consultancies want to sell you a 12-month transformation roadmap. We don't. Every engagement starts with a blunt 15-minute operational review directly with our VP of Growth. We look at your bottleneck, work out where it's actually breaking, and tell you if an AI execution path makes sense. No pitches. Just operator to operator.