Unstract is a document extraction platform that uses large language models to convert unstructured documents into structured JSON data.
The tool addresses the challenge of automating data extraction from diverse document types including PDFs, images, and scans. Rather than building custom parsing logic for each document format, Unstract lets users define extraction requirements through natural language prompts. These prompts guide the LLM to identify and structure the relevant information, which is then returned as JSON. The extracted data can be deployed either as an API endpoint or integrated into ETL pipeline workflows.
Teams in finance, insurance, healthcare, and compliance-focused domains benefit most from this approach, particularly those handling high volumes of document processing where manual extraction would be prohibitively expensive. The tool is designed for API deployments and pipeline integration, making it suitable for organizations that need to embed extraction capabilities into existing systems rather than operate a standalone interface.
The project maintains active code quality monitoring through pre-commit checks and static analysis. Development relies on Python with modern tooling including Vite for frontend builds and Bun for JavaScript runtime support. The codebase is linted and formatted using Biome, and code quality is tracked through continuous integration checks.
