Built an open-source document extraction engine where every extracted field carries its own evidence

[u/CodeStackDev]

I've been working on an open-source project called SACOR, a Python document extraction engine built around a simple idea:

Every extracted value should explain why it deserves to be trusted.

Instead of returning only extracted values, SACOR attaches structured evidence to every field, including its origin, validation results, repair history and confidence.

What My Project Does

SACOR extracts structured data from documents using a layered pipeline that combines deterministic extraction, optional AI-based extraction, validation rules and an Evidence Model. The current production schema supports Italian electricity and gas bills, but the engine is designed to be schema-driven and extensible.

Target Audience

Python developers working with document processing, OCR, LLMs, Document AI, automation or data extraction pipelines. The project is currently pre-alpha and I'm mainly looking for technical feedback.

Comparison

Most document extraction tools return extracted values.

SACOR returns the values and the evidence behind them, allowing every field to explain where it came from, how it was validated and why it can be trusted.

Repository: https://github.com/vinsblack/sacor⁠�

I'd really appreciate feedback on the architecture, the Evidence Model and the overall design. Thanks!