Aryn
Aryn provides agentic document intelligence to structure unstructured data using vision AI models and an agentic data processing engine. The platform offers highly accurate parsing and intelligent property extraction for various document types, delivering structured JSON, HTML, or Markdown outputs. This capability accelerates enterprise document workflows in sectors like insurance, BPOs, and logistics by automating tedious data handling.
- Artificial Intelligence
- Data & Analytics
- Developer Tools
- Enterprise Software
- Software Only
Funding
Raised to date
$7.5MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Extracting structured data from complex documents like PDFs, presentations, and HTML files is challenging due to variations in layout, tables, images, and text formatting. Existing solutions often lack the accuracy and speed required for efficient processing, hindering the use of unstructured data in downstream applications.
Solution
Aryn provides an AI-powered document parsing and ETL platform that accurately extracts and transforms data from unstructured documents into structured JSON format. The platform leverages purpose-built AI models to handle complex document layouts, including tables, images, and text, with high precision. Aryn's DocParse service facilitates chunking, OCR, and data extraction, while DocPrep generates ETL pipeline code for loading data into vector databases and hybrid search engines. The platform supports declarative dataflows and customizable data transforms, enabling users to process and load unstructured data reliably.
Target Audience
Aryn targets developers, data scientists, and knowledge workers in financial services, healthcare, manufacturing, eCommerce, and customer support who need to extract and analyze data from unstructured documents.
Features
- AI-powered document parsing for PDFs, HTML, presentations, and other file formats
- High-quality table extraction using purpose-built AI models, outputting JSON or Markdown
- Declarative dataflows for generating ETL pipelines using the Sycamore document ETL library
- Connectors for loading data into vector databases like Pinecone, OpenSearch, Weaviate, Elasticsearch, Qdrant, and DuckDB
- Support for various chunking strategies and vector embedding models
- Open-source base AI model for document parsing, available on Hugging Face
- Cloud-native architecture for on-demand processing via API or Cloud UI, with options for self-managed deployment in VPC or on-premises
- Agentic query engine for deep analytics and search at scale