Engineering Intelligent Document Processing.
We turn flat pixels into structured, actionable data.
The Problem
Every day, teams lose hours—and margins—to manual data entry. Invoices, contracts, and operational documents pile up as PDFs and scans, then get retyped into spreadsheets and ERPs. Errors compound, audits slow down, and the cost of "good enough" OCR stacks up faster than leadership notices. The bottleneck is not storage; it is turning unstructured documents into data your business can actually use.
The Solution — OCR Context
OCR Context is not another file converter. It is a vision for how document-heavy work should run in the age of AI: capture text with production-grade OCR, refine structure with language models, and deliver outputs your systems can search, route, and automate against.
We believe documents should be understood, not merely read. That means preserving layout where it matters, extracting fields with context, and building pipelines that scale from a single upload to enterprise batch workloads—without sacrificing security or speed.
Open Source at the Core
The document pipeline behind OCR Context is also published as a standalone open-source Python package, ocrcontext. It follows the same approach you see in this product: routing each file to the right engine—text layers for digital PDFs, OCR for scans and photos, a dedicated path for handwriting—then refining the result with a language model that corrects errors without rewriting them, and returning either clean text or a typed Pydantic model.
It runs on its own, with no web server and no vendor lock-in, and works with whichever LLM provider you already use. We keep it public because a document pipeline is easier to trust when you can read it: install it, run it against your own files, and judge the output for yourself.
pip install ocrcontext