3 min read • November 19, 2025

ML or LLM in document processing?

What technology performs best in today’s business document processing – traditional machine learning (ML) or large language models (LLM’s)?

Our position: it depends.

Let's break it down.

What wins today, and why

1. Consistent and low-complexity documents: ML still rules

Traditional Machine Learning, built on rules and templates, usually perform well on documents with low complexity, fixed and consistent layouts and where the amount of data extraction needed is limited.

Why?
Because the technology is :

Fast and inexpensive: it doesn't need to “think”, just match patterns.
Predictable: same input, same output, every time.
Auditable: if something goes wrong, you can easily trace and fix it.

Example Use Case:

Extracting header-level information on a monthly utility bill with consistent layout.

2. Messy, large or complex documents: LLM’s take the lead

But in the real world, there is often more complexity. New vendors, changing layouts or complex data patterns brings a level of “messy” that legacy technology simply cannot cater for. At the same time, as organizations look to level up automation and become more data-driven, the need to extract and understand more document data becomes critical.

This is where the power of LLM’s open new possibilities – especially solutions that can combine the strengths of multiple AI models to understand and process document data.

They can:

Understand new document formats without needing retraining
Follow more complex instructions and business rules
Improve accuracy and reliability using contextual understanding
Utilize 3rd party data sources to improve ability to validate and enrich document data.

Example:

Interpret handwritten documents without consistent layouts.

Example:

Understand geographical context and update document data (such as currency code, date formatting, address details) accordingly.

Example:

Detect and categorize different documents based on content and context.

Key differences

The best of both worlds

Docupath has built a hybrid system that combine the best of both ML and LLM’s.

Here’s how it works:

1. We start with layout-aware ML

Instead of just reading the text (like OCR), we run extremely well trained ML models that understand the structure and layout of documents, where sections, tables, headers, and fields are.

Think of it as reading the document like a human would, understanding what belongs together visually.
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Beyond OCR, we capture:

Document structure
Visual hierarchies
Table relationships and spatial semantics

This gives us a grounded representation of the page, not just text in boxes.

2. Then we add LLM’s that “see” and “reason”

These are vision-language models, which means they don’t just read text, they also understand the visual context (Ex: where the text is, what font it’s in, what it’s next to).

This helps the system:

Handle layout changes
Interpret ambiguous or mislabeled fields
Understand what a field means, not just what it says
Use visual artifacts as a guide

Example :

Even if “Total Due” is written as “Amount Payable” and moved to a different section, the system still gets it right.

3. We use the best model for the job (task-based model routing)

Our system has what we call an AI Model Garden, basically a smart router that picks the right tool for each task.

For fast, simple tasks → use a small, fast model.
For complex understanding or enrichment → use a more powerful LLM.

4. We go beyond extraction (semantic enrichment & intelligence)

After pulling out data, we clean, link, and enrich it.

For example:

Linking a policy ID to the right client record
Standardizing vendor names
Filling in missing metadata using context

Why this approach wins

The result is a system that blends the precision of traditional ML with the flexibility and contextual understanding of LLMs.

You get:

High accuracy even on unseen layouts
Lower operational overhead (fewer brittle templates)
Explainable, auditable outputs through structured schemas
Rapid adaptability to new document types and evolving workflows

Bottom line: It’s not ML versus LLMs. The future belongs to systems that know when to use both, and how to make them work together.