Data extraction for automated document processing
The reason why automated document processing is becoming a priority for many – 80% are still unstructured. The leaders, with automatic document processing to reach 12-18 billion (at least), are embracing the change and moving towards smarter data extraction.
The companies that automate data extraction don’t pay skilled employees for work a computer can handle.
The paperwork’s still everywhere: invoices, claims, receipts, contracts, bank statements, scans, screenshots. The businesses are drowning in documents they don’t really need.
They need what’s trapped inside them.
Now, intelligent data extraction is gaining more and more momentum – and that’s a shift to be happy about. With advanced data extraction, you get the insight you can actually use, and that with consistency & accuracy (for document data classification or other domain-specific objectives).
Not “digitize”, but remove the drag data creates.
What’s automated data extraction?
By automated data extraction we mean data pulled from anywhere without anyone actually reading and typing. You identify key information (vendor name, invoice total, policy number, claim amount, and many other details) without scrolling and clicking.
Quite simple in theory, rather complex in practice.
It’s automated data extraction and not nuclear physics – why shouldn’t that process be easy for computers? One document might include different layouts, blurry photos, stamps, signatures – not only plain text.
That’s why several layers of technology come together.
On automatic data extraction: the market keeps growing
One research has estimated IDP was $2.3 billion in 2024 and will be hitting $12.35 billion by 2030 (33.1% CAGR). Another report is stating IDP was $6 billion in 2023 and will be around $18 billion by 2028.
The market is growing.
The companies are sitting on mountains of records – tables, emails, images, scans – and its all unstructured. The driver isn’t “hype” but volumes of paperwork.
The pressure is visible:
- As to some reports, 63% of Fortune Global 250 companies already implemented IDP technology
- As to other sources, 70% of surveyed companies are moving towards automation (IDP included)
AI reshaping OCR technology: from classifying to understanding
The problem
OCR on its own – simply converting from pixels to characters – is no longer sufficient for today’s business needs. It has long been the backbone and basis of digitization, but its traditional form can’t keep up with the varied data landscape.
OCR flounders when layouts are changing or content is free-form – template-based logic doesn’t do the job. Yet documents to process can come in many different formats: scanned contracts, photo-clipped receipts, emails with embedded spreadsheets, notes written by hand.
The solution
AI does not “read” – it understands the context of content, its structure, and relationships inside documents. It’s not about capture, but interpretation.
AI isn’t about text – it’s about the meaning within text.
Where are we headed?
Multimodal AI, vision-capable AI, RAG pipelines, schema-aware extraction – all these are not just buzzwords. They mark a much bigger shift.
The revolution is happening:
- Multimodal AI is breaking text-only limits
- Vision-capable AI can analyze images too
- The goal is interpretation – not pulling the details, but understanding the information in context
- The workflow is conversational – no defining every step, only specifying the need
- Hybrid pipelines are the new standard (OCR, NLP, machine learning, computer vision)
- Human-in-the-loop validation is shifting from “every single record” to exceptions
Looking ahead, document processing won’t longer be at the edge of operations for cleaning up paperwork. Forget that – document processing will sit right inside the workflow and systems.
A contract won’t just be extracted – it will be checked, routed, approved, and used to trigger entire workflows. A claim won’t just be read and shared – it will be verified, compared, flagged, and moved forward immediately.
Our technology for document data extraction
Our prototype for automated data extraction is built for the complex kind of documents you actually deal with. Our objective: to organize messy records, not sell you another flashy logo for millions.
The flow is simple, but the logic behind – not really.
The system will preprocess the document, identify text, find keys you need, extract values, and clean up results. The vision is minimizing human labor (and error) and freeing up people for priorities.
Key features of document data extraction – a demo worth trying
What makes it useful in practice:
You set the keys
Every document has its own language, so rigid, template-based logic is not the way to handle the paperwork. You set the keys, the rest is handled.
Do batches without drama
Invoices, statements, forms, receipts – one batch can house many layouts, and that shouldn’t break the flow. Do batches, don’t work around them.
Get what you need
You don’t just digitize the paperwork for the sole sake of digitizing (if so, you’re throwing away time and cost). Get what you need and go on with what drives business value.
Be confident
When scans are low-quality, layouts shift, or documents simply arrive in a less-than-perfect state – we got it still. We have the expertise to get it done.
How we can help
Not another back-office upgrade – a shift in how you move the details from system to system on a daily basis. That isn’t a new side-quest project – it’s infrastructure.
So, are you ready for change?
Our expertise:
Our services:
FAQ
In brief, it’s pulling what’s needed from documents by leveraging a range of technologies (OCR, NLP, ML, CV).
No reading the invoices, contracts, receipts, and forms – no wasting the resources (human too) on routines. The machine can identify, process, understand, and convert the records into insights.
It’s almost every industry that handles large amounts of paperwork.
Healthcare providers can process patient forms, insurance claims, and other information-heavy documents. Financial institutions can manage invoice handling, statement checks, and similar.
OCR recognizes the characters, but does not understand the meaning behind content.
IDP dives much deeper – it combines OCR and AI technologies to interpret the document and get the context. IDP can classify documents, identify relationships between fields, validate information, and trigger other actions.
By generative AI summarization we mean LLMs analyzing the content and producing human-friendly summaries.
No reviewing long reports, contracts, claims, or emails – the system can do all that without draining your team. That means better decisions on request – no delays.
NLP helps AI understand the meaning and context.
That matters as documents are rarely perfectly structured – they differ in format, style, wording, and extras. But using these capabilities, we handle different layouts.
The process usually depends on existing business systems.
Most solutions do integrate with common business systems: ERPs, CRMs, analytic pipelines, internal databases. But, typically, that requires a specific domain expertise.