AI-powered document parsing, table extraction, and ETL
Accurately extract and transform tables, images, and text for LLM-based apps, RAG frameworks, and vector databases.

Accurately extract and transform tables, images, and text for LLM-based apps, RAG frameworks, and vector databases.
Financial and Legal Documents
Extract tables, figures, and text from financial reports, legal cases, and more.
Manufacturing and Technical Documents
Structure complex technical manuals, documentation, and reports.
Customer Support and Marketing Transcripts
Quickly parse and chunk customer tickets, interview transcripts, and support chat.
For document segmentation, DocParse runs a state-of-the-art, deep learning AI model trained on 80k+ enterprise documents along with powerful post-processing steps. It's up to 6x more accurate, 5x faster, and 5x cheaper than alternative systems.
Supports over 30+ file formats including PDF and Office
Creates labeled bounding boxes for document segments
Scales to documents with thousands of pages
Cheaper than AWS Textract and Azure Doc Intelligence
After labeling each part of the document, DocParse uses a selection of purpose built models for OCR, table extraction, image extraction, and chunking. You can choose to output your parsed documents in JSON or Markdown.
Complex tables
Exract complicated
table formatting
Multi-language OCR
Support for over
60 languages
Easily add DocParse to your document automation workflows with a few lines of code. Or, use integration with the open source Sycamore document ETL library for metadata extraction, data cleaning, and reliably loading databases.
Easily add to your workflow using sync or async APIs
Support for Sycamore document ETL library
Use DocPrep wizard for creating basic ETL pipelines
Available as SaaS, private cloud, or on-prem deployment
Boost the quality of your RAG pipeline, search application, or document automation workflow by using DocParse to process and extract information from your documents. Visualize it on your data for free in the Aryn Playground.