Invoices, orders, and forms you no longer have to retype

A pile of paperwork arrives every day. Purchase invoices as PDFs in the mailbox, delivery notes the driver photographed with a phone, request forms a client filled in by hand. Someone opens each file, finds the invoice number, the amount, and the supplier, and types them into the accounting package. At the end of the month, something still does not add up, and the search begins.

What OCR does and where AI comes in

OCR turns a picture into text. It has done that for years, and it does it well for printed text. It stops at meaning. It sees three amounts on an invoice, but it does not know which one is the total, which is the VAT, and which is a discount that has already been applied. Every supplier puts those fields in a different place, and a fixed template breaks as soon as a new supplier appears.

We add a language model behind it. It reads the text that OCR produced the way a colleague would: this is the supplier, this is the due date, this is the total including VAT. It does not need to know the layout in advance. OCR handles the letters, and AI handles the meaning. No magic, just two tools that complement each other.

Which documents suit this

Purchase invoices are the best-known example. The same approach fits any flow where the same kind of information keeps arriving in a different format. Orders that clients email as PDFs. Delivery notes and waybills at a wholesaler or carrier. Annual accounts and bank statements at an accounting firm. Intake forms in healthcare. Contracts where you only need the term and the notice period.

It pays off when there is volume and the work repeats. If only a handful of unique documents arrive each month and someone has to read them anyway, we will not build anything for that, and we will tell you so right away. If you do not yet know which flow costs the most hours, the AI process scan measures that first, before anything is built.

People check, the model does the first pass

We do not want a model that books everything on its own while nobody looks, and neither do you. So the model reports how sure it is for each field. An invoice number that is sharp on the scan goes through. An amount that sits half under a stamp is flagged on a review screen. Your colleague looks at those 3 doubtful fields instead of 30 fields per document, corrects what is wrong, and approves.

Every correction is saved. After a few weeks, you can see exactly which document types the model handles flawlessly and where it still struggles, and the share that needs no review grows. The work does not disappear. It shifts from retyping to checking. That is faster, and it is work people do better than software.

Tested on your own documents within 10 business days

We do not start with a demo on tidy sample documents. You give us last month's folder, crooked scans and unusual suppliers included. Within 10 business days, a prototype reads that pile, and we measure how often each field is right. That number decides whether we continue. If it is too low for your documents, we stop there, and you have not lost months.

If it works, we connect the extracted data to the system where it belongs, such as Exact, AFAS, Odoo, or your own case system. Processing runs on an isolated server in the EU, your documents never train other companies' models, and you own the code and every line it produces. If the documents contain personal data, we arrange the agreements for that through GDPR compliance. Because every processed document ends up neatly in an archive, AI document search is a small step later on.