We turn documents into data that LLMs can actually reason over.

Existing OCR APIs treat documents like images of text. They rasterize a chart and ask a vision model to read the bars, then return approximate numbers. They flatten diagrams into alt text. They lose math entirely or stringify it. The output is fine for full-text search and brutal for anything an LLM needs to reason about.

We took the opposite approach. Where the original document already encodes a piece of information losslessly — text in a PDF text layer, chart data in a PPTX Chart shape, a table's cells — we read it byte-perfect. We only call an LLM for things that genuinely require semantics: classifying a scanned page, describing a photo, transcribing handwriting, or extracting structure from a vector diagram. Even then we verify every LLM output against the source before returning it.

The result is an extraction you can audit. Every block tells you where it came from. Every confidence score is meaningful. Every chart carries the exact numbers a human drew, not an OCR'd approximation of them.

Teams building RAG pipelines, document understanding workflows, and AI agents that read structured business content. Customers tell us they reach for OCRQueen when accuracy of the structure — not just the text — matters: legal briefs, patents, financial reports, technical specs, research papers, sales decks.

We're developer-first. There's a free tier, a one-line SDK in Python and Node / TypeScript, predictable per-page pricing, and documentation a human can actually read.

Early. Today OCRQueen has a stable extraction API, two SDKs, a dashboard, webhooks, and a free tier you can use without talking to anyone. We're building toward enterprise features (SSO, BYOS, audit log exports, on-prem deployment) on customer demand — not roadmap inertia.

Found a bug, want a feature, comparing us to a competitor — email support@ocrqueen.com. We read everything.