Automating invoice processing with AI: how it works

For many companies, processing purchase invoices is the textbook example of work that should be automatic. Every invoice looks different, but the steps are always the same: capture the data, check it, match it to a purchase order and book it. With AI, most of that can now happen on its own.

Why it used to fall short

Automatic invoice recognition has existed for years. Classic systems relied on templates per supplier: "the invoice number is top right, the total at the bottom". When a supplier changed its layout, things broke. New suppliers had to be configured by hand first.

Modern language models work differently. They read an invoice the way a person does: they understand what an invoice number, a VAT amount or an IBAN is, wherever it appears. That means it also works for new suppliers and for scans or photos of reasonable quality.

The process in six steps

1. Receive. Invoices arrive through a dedicated email address, a portal or a folder. The automation picks them up there, even when one email contains several PDFs.

2. Extract. The AI pulls out the data: supplier, invoice number, dates, line items, VAT, total and payment details. Every field gets a confidence score.

3. Check. Does it add up? Do we know this supplier and this IBAN? Has this invoice number been booked before? These checks catch duplicate invoices and fraud attempts using changed bank details.

4. Match. Where possible, the automation matches the invoice to a purchase order and a goods receipt. If quantities and prices match, the invoice is ready to book.

5. Pre-book. The invoice is prepared in your accounting system with general ledger account, cost centre and VAT code. The AI learns from previous bookings for the same supplier.

6. A person decides when in doubt. Anything that doesn't add up or is uncertain goes to a work queue for an employee, with an explanation of why. Everything else flows through.

Where a human stays involved

The goal is not that nobody looks at invoices any more, but that people only look at the difficult ones. Common rules are:

• always require approval for invoices above a certain amount;
• always check new suppliers and changed bank accounts;
• review deviations of more than a few percent from the purchase order.

You set these thresholds yourself and adjust them as confidence grows.

What to measure

Measure the same things before and after, so you can see the result:

• Lead time per invoice, from receipt to booking.
• Straight-through rate: the share of invoices processed correctly without human intervention.
• Errors and corrections afterwards.
• Early-payment discounts and late fees: fewer late payments often pays off directly.

Connecting to your accounting

The automation only really pays off when it talks directly to your accounting system. Most mainstream packages, such as Exact, AFAS, Xero, SAP and Microsoft Dynamics, offer integration options. If you use something else, we look at what is possible together.

Privacy and security

Invoices contain business and sometimes personal data. So pay attention to where processing takes place and whether your data is used to train third-party AI models. At Nuraghi Cloud, processing runs in European data centres or in your own cloud environment, and your data is never used for training.

Getting started

Invoice processing is an ideal first automation: the process is well defined, doesn't touch customers directly and delivers measurable results quickly. In an automation sprint we set this up for you in four to six weeks, connected to your own accounting and tested on your own invoices.