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Inside our agentic document extraction pipeline

A walkthrough of how an agent reads messy real-world documents, checks its own extractions, and flags what it is unsure about before handing off structured data.

Mekhos AI Labs·August 2026·8 min read
Inside our agentic document extraction pipeline

Why real-world documents are difficult

Real-world documents are rarely clean. Tables break across pages, important fields appear in unexpected locations, and the same piece of information can be represented in several different ways.

An agentic extraction pipeline

Our document extraction pipeline treats extraction as an agentic process rather than a single model call. The system reads the document, identifies relevant regions, extracts candidate values, and checks those values against the surrounding context.

Handling uncertainty

When confidence is low, the system flags the field instead of silently returning a potentially incorrect value. This makes the final structured output easier to inspect and trust.