Why AI Document Extraction Beats Rule-Based OCR

Rule-based OCR has been the default for invoice and PO extraction for two decades. It works — until a vendor changes their template, adds a new line, or switches from PDF to scanned image. Then the rules break and someone has to rewrite them.

AI document extraction takes a different approach. Instead of writing extraction rules by hand, you define the schema (the fields you want) and let a model figure out where each field lives on the page. When the model gets something wrong, you correct it once and a learning loop rewrites the per-field prompt so it gets the same case right next time.

The result is a system that handles new templates without engineering work, gets more accurate the more you use it, and gives you a clear feedback loop instead of a brittle pile of regexes. That is why we built dataextractor.io around the GEPA learning loop instead of a rule engine.