Automated invoice processing: OCR, RPA or AI? What to choose and why

When a company says “we want to automate invoices”, it gets quotes for three quite different things that all pretend to be the same: OCR, RPA and AI. The difference is not academic. It decides whether in six months you will have less work, or whether you will be fixing the robot’s mistakes instead of a person’s.

Let us take them apart so you can choose.

OCR: reads characters, does not understand the document

OCR (optical character recognition) turns an image into text. It is a mature, cheap technology, and in one scenario entirely sufficient: when documents come from a handful of suppliers in the same template every time. Then you can say “the total is bottom right, the invoice number top right” and it works for years.

Where it breaks: OCR does not know that “Total due” is the grand total and “VAT base” is not. It does not know that a line on page two belongs to the table on page one. It does not know that this supplier writes dates differently. Every new supplier means a new template — and with hundreds of suppliers nobody keeps up. On a phone photo with a shadow and a bent page it also gets the characters themselves wrong.

RPA: a robot that clicks for you

RPA (robotic process automation) is software that imitates a person at the keyboard: opens the email, downloads the attachment, types the data into the accounting program, clicks save. It is useful where a system has no API and the only way in is through its screen.

But RPA does not solve reading the document — it needs OCR or AI for that. And it is brittle: when the accounting program moves a button twenty pixels after an update, the robot stops. Or worse, does not stop and clicks the wrong place. RPA is therefore glue for old software rather than a solution in itself.

AI: reads the document by meaning

Current language and multimodal models read a document much like a person does: they look at the whole thing and understand what is the supplier, what is the company ID, what are the line items, where the VAT is, which amount is due. They need no template, so a document from a new supplier is processed like one from an old supplier. They handle Slovak, Czech and foreign documents, foreign currencies, different date formats, and cope with a handwritten note in the margin.

Where AI breaks: confidence. A model always answers, even when unsure — which is exactly why it must never be used on its own. It needs checks and a confidence threshold around it, which the next section is about. The second limit: a fully handwritten document is borderline, and the system has to admit that rather than guess.

Side by side

In practice the best answer is a combination: AI reads, OCR serves as a fallback for poor scans, and RPA is used only where the accounting program has no input other than its screen.

A process that keeps errors out of the books

Technology is half of it. The other half is a process that makes sure an uncertain document ends up with a person, not in the books:

1. Intake from anywhere. A mailbox, a shared folder, a scan, a photo. The document enters processing on its own.
2. Extraction. Supplier, company ID, payment reference, line items, VAT rates, totals, dates. Each value with the confidence the model read it with.
3. Sum checks. The lines must add up, VAT must match the base, the amount due must match the total. If not, the document is flagged — regardless of how “confident” the model was.
4. Matching. To the purchase order, the delivery note, the supplier record. Three-way matching of order – delivery note – invoice reveals when they bill more than they delivered.
5. Confidence threshold. Documents above the threshold go into the system. Below it, they enter a review queue where a person sees the document and the extracted data side by side and corrects with one click. You set the threshold — for example, everything above €10,000 is always reviewed by a person.
6. Posting and trail. Every posting links to the document, the page and the confidence. When someone asks a year later where this item came from, the answer exists.

Step five is what separates a solution from a demo. A solution that posts everything it reads saves you the retyping and adds the work of hunting errors at month-end close.

More detail, including what the system handles and what it costs, is on the page automated invoice and delivery note processing.

Slovak accounting software: can it connect?

The most common question on the first call. Pohoda, Money S3, Omega, Helios and SAP Business One have an API or at least an import format (XML, CSV, ISDOC). Where there is neither, file import or RPA through the screen remains — less elegant, but it works. An accounting firm that keeps the books for dozens of clients has an extra advantage: the same process is set up once and used for all of them.

The upcoming mandatory e-invoicing will solve part of the problem — a structured invoice does not need to be read. Delivery notes, foreign documents, receipts and phone photos will remain, and those make up most of the manual work.

What it costs and when it pays back

Indicatively, excluding VAT: document and process analysis €900–1,800, pilot on real documents €3,500–6,000, production rollout with accounting integration €6,000–12,000, operations from €290 a month plus model usage (single to low double-digit euros at hundreds of documents a month).

Payback depends on volume. At hundreds of documents a month, where several hours a day are spent retyping today, it is typically under three months. At dozens of documents, roughly within a year — and then it is worth asking whether a cheaper route would do. We write about AI project budgets more generally in how much an AI solution costs.

Summary

• OCR with templates is enough for a few suppliers with a fixed format.
• RPA is glue for software without an API, not a solution for reading documents.
• AI reads the document by meaning and handles a new supplier without configuration — but must never post to the books on its own.
• The process around it decides: sum checks, matching, a confidence threshold and a review queue.
• Count your documents per month. At hundreds, it is worth talking now; at dozens, do the maths first.

If you want to know what it would look like on your own documents, send us a sample — on the first call we will say which of the three routes makes sense in your case.