Docunerve

Most PDF parsers treat scanned pages as a problem to ignore and tables as text to flatten. Docunerve is an extraction API that converts PDFs — including OCR-dependent scanned documents — into Markdown or JSON formats sized for LLM ingestion and vector database storage. The core workflow is a single API call: send a document, receive structured output. No UI to click through, no pipeline to configure inside the vendor’s dashboard.

The differentiating claim, per the vendor page, is fidelity on document types that break generic parsers: scientific papers with embedded formulas, financial reports with nested tables, and multilingual scanned contracts where character recognition and structure preservation have to work together. The output targets formats that feed directly into RAG retrieval layers without an intermediate cleaning step — which is where most extraction tools lose time and accuracy.

Docunerve fits cleanly into a pipeline where extraction is a discrete, upstream step: ingest document, get structured text, pass downstream. It does not perform autonomous planning, conditional branching based on document content, or multi-step validation loops. Teams whose ingestion workflow requires those behaviors — flagging a document for human review if confidence is low, routing different document types to different prompts — add that logic outside Docunerve. The tool is not self-hosted, so teams with data residency requirements hit a hard wall regardless of extraction quality.

The API returns results in Markdown or JSON. The freemium tier provides a fixed credit allocation; beyond that, processing runs on paid credit packs. No SDK or webhook behavior is described on the vendor page beyond the API itself, so integration complexity depends on what your pipeline already handles for HTTP calls and response parsing.