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Enterprise Layer Intelligence
Categories/Data Platform & Integration/Unstructured
Unstructured

Unstructured

Founded 2022

Transform complex documents, tables, and scans into structured data for machine learning models

Pricing model

Free Tier, Paid

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Unstructured to ingest, clean, and convert messy files like PDFs, Word documents, emails, and images into structured formats for language models and search pipelines. It extracts tables while maintaining row and column relationships, performs OCR on scanned pages, and preserves original document reading orders and layouts. The service automatically chunks, embeds, and transfers information from enterprise cloud storage directly into vector databases, eliminating manual document preparation so your engineering team can build reliable search and retrieval systems quickly.

What Unstructured does

Extract text and tables from complex PDF documentsRun optical character recognition on scanned images and formsChunk and embed large text files for vector storageConnect data sources directly to target vector databasesMaintain document layouts and reading order during ingestionTransform raw documents into structured JSON outputsParse over 65 file types including PDFs, office documents, and imagesExtract multi-page tables while maintaining row and column hierarchiesPerform OCR on scanned files and handwritten documentsPreserve native document reading orders, layouts, and headersConnect directly to cloud storage like Amazon S3 and Google DriveAutomate chunking, embedding, and loading into vector databasesDeploy via API, managed cloud service, or private VPC

Pricing breakdown

Pricing model: Free tier, Paid

PlanList price
FreeFree
Pay-As-You-Go (per page)$0.02 / mo

Tutorials & Demos

Frequently asked

Google DriveGoogle DriveSnowflakeSnowflakeDatabricksDatabricksTeradataTeradata

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Unstructured, document parsing, PDF extraction, OCR, RAG pipelines, data ingestion, vector databases, table extraction, chunking, embeddings, text parsing, ETL, document processing, data pipelines, LLM data prep