Unstructured

Unstructured is a data processing platform designed to convert complex, unstructured content into clean, structured, and AI-ready inputs. It focuses on extracting, normalizing, and organizing information from a wide range of document types so that it can be reliably used in large language models, retrieval-augmented generation (RAG) systems, and other AI pipelines. The primary purpose of Unstructured is to remove the friction between raw enterprise data and production-grade AI applications.

The platform connects to common data sources and repositories, then processes more than 60 file types including PDFs, HTML, PowerPoint, Word, images, emails, and scanned documents. It uses layout-aware parsing and content segmentation to preserve document structure such as headings, tables, lists, and metadata, which are critical for accurate downstream retrieval and analysis. Unstructured outputs standardized formats like JSON and chunked text that are optimized for vector databases and search indexes. It can be deployed via API, SDKs, or containerized infrastructure, giving teams flexibility to run it in the cloud or within their own environment.