UnDatasIO
Layout-aware document parsing that turns PDFs and images into RAG-ready structured data, pay-per-accurate-result credits from about $1 per 1,000 pages
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undatas.ioAbout UnDatasIO
UnDatasIO is a document-parsing service built specifically for the ingestion stage of RAG pipelines and AI agents, where the failure mode is rarely the model and usually the extraction: a table flattened into prose, a formula lost to OCR, a layout that scrambled reading order and poisoned every downstream chunk. The engine targets exactly those cases, recognising document layout before extracting, and returning text, tables, images and formulas as structured output in JSON, CSV, Parquet or SQL-like database form rather than as a wall of markdown. The commercial hook is unusual and worth stating plainly: billing is on accurate results, so a customer pays for what was successfully extracted rather than for every page fed in. Integration is a small Python SDK — initialise a client with a token and task name, upload a directory of files, list what was uploaded, parse a named file list, and pull historical parse versions — alongside a REST API, and UnDatasIO is now an official core provider inside LangChain, which removes the usual glue work for anyone already on that stack. The published comparison table positions it at roughly $1 per 1,000 pages against Mistral-OCR, Docling, Claude, unstructured.io and LlamaParse, with layout and multilingual handling called out as the differentiators. The named verticals are mechanical drawings for manufacturing and construction, financial statements for accounting and investment firms, and legal and litigation documents for e-discovery and document review — all cases where a parsing error is expensive rather than cosmetic.
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