Privacy-First Document Intelligence for Production RAG Pipelines
Most RAG pipelines fail at the data layer. Hallucinations, missed tables, garbled equations, and unverified citations stem from poor document parsing - not from the LLM itself. LongParser solves the input problem. Parse PDFs, DOCX, PPTX, XLSX, and CSV into validated, AI-ready chunks with HITL review and citation tracking.
pip install "longparser[gpu]"Most RAG failures happen before the LLM even sees the data. LongParser fixes the input.
PDF (native and scanned), DOCX, PPTX, XLSX, and CSV. Format-specific parsers that preserve structure, tables, and equations.
Fixed-size, sentence-based, semantic, recursive, structure-aware, and sliding window. Auto-select or configure per document type.
Human-in-the-loop review for low-confidence extractions. Critical for regulated industries where document accuracy is non-negotiable.
Every chunk is tracked back to its source location in the original document. RAG responses can cite exact page, section, and paragraph.
Extracts and preserves mathematical equations in LaTeX notation. Essential for scientific, technical, and academic document processing.
Built-in FastAPI server with Docker support. Deploy as a microservice in your RAG infrastructure. LangChain and LlamaIndex ready.
Every step is configurable. Skip HITL for automated pipelines, or add custom validation steps.
We build enterprise document processing pipelines using LongParser. Schedule a free consultation.