Traditionele OCR-systemen “look” at letters and numbers, but do not understand what they see. They recognize text, but do not know that the number in the bottom right of an invoice is the total amount. An invoice number in column A is for OCR exactly the same as an order number in column B: characters without meaning.

AI data extraction goes fundamentally further. The system reads and understands the context of your documents, comparable to how an experienced employee does. Whether an amount is in the top left or bottom right, the AI recognizes it as “Total amount” by analyzing the surrounding text, layout and document structure. The system combines visual recognition (where is something on the page?) with language understanding (what does the surrounding text mean?) to arrive at the correct interpretation.

This difference is crucial. Where OCR gives you flat text that you still need to manually interpret, AI data extraction delivers directly structured, usable data: supplier name, invoice number, amounts, VAT rates, dates, all neatly in the correct fields. And this works not only for standard documents. Unstructured sources such as emails, attachments and handwritten notes are also processed by the same AI-technologie. The system also learns from every document it processes, causing accuracy to continuously increase as you submit more documents.

A single AI model makes mistakes. That is inevitable. But what if you let multiple models work independently and then let them “vote” on the correct answer?

That is exactly what our Large Model Voting methodology does. Multiple AI engines analyze the same document. Each model extracts the data in its own way. Then we compare the results and select the values the models agree on.

The principle is comparable to a team of experts independently looking at the same problem. Where they agree, the chance of errors is minimal. Where they disagree, we know exactly which fields need extra attention. Those fields are automatically routed to the human-in-the-loop verification, so an employee only needs to check the uncertain cases.

The result is high extraction accuracy, without being dependent on a single model or a single vendor. Is a better model available tomorrow? Then we add it to the voting team. Want to replace a model? The system just keeps working. That is the power of a open-source AI approach: no vendor lock-in at model level.

In practice this means your extraction accuracy is higher than with any single model, while you retain the flexibility to move with the rapid developments in the AI world. Where competitors are stuck with a single model they cannot replace without rebuilding their entire system, at EasyData you can add, replace or combine models without downtime. That architectural freedom is what makes the difference between an AI implementation that is outdated in two years and one that grows with the technology. Read more about how our ESV Platform makes this multi-engine approach possible.