Chunkr

Agentic document parsing and extraction for AI teams, via one API.

A document-intelligence API that parses, splits, extracts, and edits PDFs, images, spreadsheets, and slides into clean, structured output for RAG and AI pipelines. It blends custom in-house models with frontier ones and bills via usage credits, automatically discounting pages it can parse without the heavier pipeline.

ETL for LLMs — turn PDFs, decks, and emails into clean, structured data.

Ingests 64+ file types and partitions, chunks, enriches, and embeds them into LLM-ready output, handling OCR, tables, and document hierarchy. An open-source library plus a low-code platform and API; a staple preprocessing layer for production RAG.

Toolkit that turns documents into AI-ready Markdown and JSON.

A document-processing toolkit that converts PDF, DOCX, PPTX, XLSX, HTML, images, and audio into clean Markdown or JSON for LLM and RAG pipelines. It does advanced PDF understanding — page layout, reading order, table structure, and OCR for scans — and ships a hybrid chunker plus native LangChain and LlamaIndex integrations. Small enough to run on a laptop via a Python API or CLI; MIT-licensed and community-governed.

AI agents for document processing and enterprise data extraction.

Nanonets automates document-heavy workflows — invoices, orders, contracts, and claims — with AI agents that read, extract, and route structured data across ERPs, email, and approval chains. It runs on its own OCR-3 extraction model and can fold in LLMs for agentic pipelines. Offered as managed cloud with VPC, single-tenant, and on-premises deployment options and regional data residency.