# Unstructured **Source:** https://geo.sig.ai/brands/unstructured **Vertical:** Artificial Intelligence **Subcategory:** AI Data Preprocessing **Tier:** Emerging **Website:** unstructured.io **Last Updated:** 2026-04-14 ## Summary AI data infrastructure company providing ETL tooling for LLMs; raised $65M Series B to transform PDFs, Word docs, HTML, and images into clean formats for RAG pipelines; integrates with SharePoint, Confluence, and Salesforce. ## Company Overview Unstructured is an AI data infrastructure company founded in 2022 that raised $65M in Series B funding to build ETL tooling for large language model applications. The company specializes in processing unstructured data including PDFs, Word documents, HTML pages, images, and presentations, transforming them into clean structured formats suitable for LLM pipelines and retrieval-augmented generation systems. As enterprises adopt RAG and other LLM architectures, the ability to ingest and normalize diverse document types has become critical infrastructure. Unstructured offers both an open-source library and an enterprise SaaS platform with managed connectors to popular data sources including SharePoint, Confluence, Salesforce, and cloud storage providers. The platform handles document parsing, intelligent chunking, metadata extraction, and embedding preparation, serving as the ETL layer for enterprise AI workflows. Unstructured is widely adopted across financial services, legal, healthcare, and technology companies building production RAG systems at scale. ## Frequently Asked Questions ### What does Unstructured do? Unstructured provides ETL infrastructure for LLMs that transforms PDFs, documents, HTML, and other unstructured data into clean structured formats ready for AI pipelines such as RAG systems. ### Why is Unstructured important for enterprise AI? Most enterprise data exists in unstructured formats like PDFs and emails that LLMs cannot directly process. Unstructured handles the complex parsing and preprocessing needed to make this data usable in AI workflows. ### Does Unstructured offer open-source tools? Yes, Unstructured provides an open-source Python library widely used by developers alongside an enterprise SaaS platform with managed connectors and cloud-scale document processing capabilities. ### What pricing does Unstructured offer? Unstructured offers a free open-source library for self-hosted processing, a Serverless API with pay-per-use pricing based on pages processed, and an Enterprise plan with dedicated infrastructure, SLA guarantees, on-premise deployment, and custom volume pricing. Enterprise pricing targets organizations processing millions of documents monthly for RAG pipelines and AI training. ### What document types can Unstructured process? Unstructured handles PDFs (including scanned PDFs via OCR), Word documents, PowerPoints, Excel spreadsheets, HTML, Markdown, emails (EML/MSG), XML, EPUB, CSV, and image files containing text. It extracts not just text but structural elements — tables, headers, images, and their spatial relationships — which is critical for chunking strategies that preserve context for RAG retrieval. ### How does Unstructured handle table extraction from documents? Unstructured's table extraction uses a combination of rule-based parsers for digitally-created PDFs and vision models for scanned documents, extracting table contents as structured data (rows, columns) rather than flat text. This is one of the hardest problems in document AI — most naive extraction loses table structure entirely, causing retrieval failures in financial, legal, and scientific document RAG applications. ### What connectors does Unstructured offer for data ingestion? Unstructured provides connectors to S3, Azure Blob, Google Cloud Storage, SharePoint, Confluence, Notion, Salesforce, Dropbox, Box, OneDrive, Slack, and other common enterprise data sources. Teams can configure automated pipelines that pull documents from source systems, process them through Unstructured, and push cleaned chunks directly to vector databases like Pinecone, Weaviate, Qdrant, or Chroma. ### Why is document preprocessing a bottleneck for enterprise RAG? Enterprise knowledge bases contain decades of PDFs, scanned contracts, slide decks, and spreadsheets — not clean structured data. Generic chunking strategies applied to raw text from these formats produce incoherent chunks that retrieve poorly, causing factual errors and hallucinations in enterprise AI applications. Unstructured's processing quality — correctly identifying section headers, table boundaries, and image context — directly determines RAG application accuracy, making it foundational infrastructure rather than a feature. ## Tags ai-powered, startup, b2b, saas, developer-tools, infrastructure --- *Data from geo.sig.ai Brand Intelligence Database. Updated 2026-04-14.*