The Business Perspective on Document AI

Tue, 22 Jul 2025 15:53:00 +0000
Machine Learning, Artificial Intelligence, LLMs, document AI

You need to process tens of thousands of documents each month and wonder whether it’s worth investing in AI. Or maybe someone on the team said, "OpenAI will probably handle it - let’s give it a try"?

Extracting key information from documents such as invoices, receipts, contracts, or reports is one of the most common problems companies face. And one that can be automated using document AI. It's also one of the easiest to be poorly designed. The choice of technology directly impacts costs, scalability, output quality, and user frustration.

Below, you'll find a list of key questions every company should ask before committing to a machine learning (ML) solution. Choosing the right approach isn’t just a technical decision; it’s a strategic one with long-term impacts on costs, capabilities, and flexibility.

1. Should we build our solution or buy an off-the-shelf one (build vs. buy)?

2. Do we care more about low operational cost (OPEX), or are we willing to accept a higher upfront investment (CAPEX)?

3. Is complete control over the model critical, or is a working "black box" enough?

4. Do we want to build internal capabilities and invest in future AI initiatives?

In this article, I compare three practical approaches to solving this problem:

Ready-made SaaS services (Azure AI Document Intelligence)

Commercial LLMs (OpenAI)

And fine-tuning open-source models

I’m not looking for a universal winner. Instead, I’ll show which approach works best at which scale, with what trade-offs, how operational costs differ, and what you should know before investing your ML team’s time or money in external APIs.

In the first part, I’ll explain what VRDU (Visually-Rich Document Understanding) really is, why classical NLP often fails on real-world documents like invoices or contracts. Next, I walk you through the three most practical approaches to key information extraction from visually-rich documents - SaaS tools, Large Language Models (LLMs), and fine-tuned open-source models.

Then, I’ll show how different solutions compare in terms of cost, control, scalability, and implementation effort. I also share a cost breakdown based on real calculations, so you can estimate ROI and choose the right path for your specific needs and scale. Whether you’re building a PoC, scaling a mature platform, or trying to avoid expensive mistakes - this guide is here to help.

What is Visually-Rich Document Understanding (VRDU)?

Most Natural Language Processing (NLP) systems operate on plain text: lines, paragraphs, sequences of words. But business documents follow different rules.

Take an invoice image, for example. It’s not just text, its structure: columns, fields, headers, table rows, signatures. Key information might be located in a corner, next to a logo, in the footer, or below a table. Two documents might contain the same data, but arrange it differently.

That’s why classical NLP or Information Extraction (IE) approaches, which rely only on text sequences, often fail. They lack visual awareness, an understanding of how information is positioned on the page.

Visually-Rich Document Understanding (VRDU) is a field that combines: text recognition (OCR), layout analysis, and modeling of visual-textual...