Problems we solve
Unlock and enhance data for agentic AI: Your messy files in, and clean, structured JSON out. Adding structured metadata fuels better decisions and actions. Clean chunks and the right context help your agents work faster and understand more. Accelerate prototyping: Production-ready from the start. Code-first or no-code pipelines, your choice. Build and preview, source to vector DB in minutes. Replace DIY pipelines: Move solutions into production faster. Reduce engineering costs. Eliminate maintenance. Resilient partitioning logic and robust visibility built in from day one. Unify data silos: Discover hidden insights with 35+ connectors and 65+ file types. Consistent JSON format. All-in-one data layer. A unified knowledge base across your business units and tools.Quickstarts
Claude quickstart
Use Unstructured with Claude and plain language prompts to go from an unstructured source file to structured data output in about 5 minutes.
Python quickstart
Use Unstructured with Python to go from an unstructured source file to structured data output in about 5 minutes.
Core functions
Partition
Turn unstructured source files and semi-structured data records into structured document elements and metadata that use a predefined, expressive, and contextualized JSON format.
Extract
Define your own target JSON schema and have Unstructured extract values from your source files and data records directly into that shape in JSON format.
Enrich
Add AI-generated enhancements to partitioned output. Enrichments include image descriptions, table descriptions, table-to-HTML conversion, named entity recognition (NER), generative OCR, and more.
Chunk
Reorganize partitioned output into manageable pieces sized for embedding models and optimized for retrieval precision.
Embed
Convert text output into numeric vectors using an embedding model. These vectors capture semantic meaning. Unstructured stores them alongside the text so you can load them into a vector store and power similarity search in RAG applications.
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