Blog
OCR, QC, and pipelines
Practical notes on layout-preserving extraction, honest AI QC, and shipping text into your vector database.
The real cost of document AI isn't the per-page price
Per-page rates are the easiest number to compare and the least complete. A framework for evaluating document AI on total cost — with OCRs.ai's $0.012/page as a worked example.
The document-to-vector pipeline is one system, not four vendors
Retrieval quality is decided upstream — at extraction and chunking. Why OCR, chunking, embedding and vector delivery belong in one pipeline with one set of metadata.
AI quality control for OCR should never invent a word
Language models can fix OCR errors — or quietly fabricate account numbers and names. The safe design constrains AI to choose among what the page actually shows.
Layout is the product: why plain-text OCR fails modern document workflows
Plain-text OCR was built for keyword search. Review, export and retrieval need geometry — reading order, regions and structure. Here is why layout is now the baseline.