Unstructured
@UnstructuredIO
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ETL+ for GenAI data. 👉🏼 Get Started: https://t.co/7Phj5PbxNU
San Francisco, CA
Joined August 2022
🔧 What are we building at Unstructured?. So much of the world’s data is messy and unstructured—PDFs, slides, emails, HTML, and more. Our engineers are tackling one of the biggest challenges in AI today: making the hardest, messiest data usable for real-world systems. Fast,
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We parsed this product manual using our VLM partitioning strategy with GPT-4o by OpenAI. Each illustration was auto-identified with detailed captions like “a person assembling furniture incorrectly with a cross mark" without a single line of manual annotation. Check out more
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📣 We’re hiring!. We’re growing faster than ever, and we need more incredible engineers to join our team. Could that be you? 👀👇. 🔹 Field Infrastructure Engineer.🔹 Solutions Engineer (Post-Sales).🔹 Solutions Architect (Pre-Sales).🔹 Principal Software Engineer.🔹 Staff
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🚨 Your RAG pipeline might be dropping the ball. Even if you fetch the right documents, they might be buried too deep in the list for the LLM to see. That’s where reranking comes in. Our latest blog post breaks down:.🔹 Why vanilla RAG isn’t always enough.🔹 How reranking fixes.
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Last week we showed you how to go from @awscloud S3 to @qdrant_engine with no code using Unstructured. Now we’ve got a Colab notebook if you’d rather do it programmatically!. 🔧 What it covers:.- Connecting S3 as a source.- Preprocessing with partitioning, chunking, and.
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We used our VLM partitioning strategy with GPT-4o by OpenAI to parse this handwritten field trip form. Names, contact info, dates, and even the signature—cleanly extracted into structured JSON. See more real-life examples in our transformation gallery 👉
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Make sure to check out our new Slack Source Connector!. You can now pull content directly from your organization’s Slack messages using Unstructured. Instead of digging through channels to track down that one decision about a launch date or project scope, just extract it directly
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Using the VLM partitioning strategy, we extracted everything from names and addresses to event dates, permissions, and handwritten fields — all from a curved form with small font and light print. 👉 Try it yourself: 🔗 More real-life examples:
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📣 Documentation Roundup – July 2, 2025. 🧩 Chat the Docs with AI:.- New buttons let you chat within documentation using ChatGPT, Claude, or the Ask AI tool. ✨ Element Metadata Reference:.- Now includes all available metadata fields for full transparency →
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Check out our new blog post on how to build a fully automated @awscloud S3 → @qdrant_engine pipeline with no code. 🔹 Pull docs from S3.🔹 Enrich with captions, summaries & more.🔹 Embed & push to Qdrant — no orchestration required. 👉 Perfect for getting your data RAG-ready in.
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Today's real-life transformation: a Quaker Oats container. Complete with a nutrition facts table, marketing claims, ingredients, and customer support info—this example shows how we easily partition visually dense layouts. See more real-life examples in our transformation
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Make sure to check out our latest e-book for real-world tips and insights on advanced RAG techniques 👇. #AI #GenAI #ETL #ETL+ #RAG #UnstructuredData #LLM #MCP #EnterpriseAI #RAGinProduction #LLMready #Unstructured #TheGenAIDataCompany.
Want to push GenAI performance further than basic vector search and chunking? Our new guide breaks down the latest RAG techniques—from smarter chunking and metadata filtering to GraphRAG, hybrid search, and agentic workflows. What’s inside:.🔹 Why naive RAG fails and how to fix
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Most GenAI pipelines don’t fail at the model, they fail on messy, inconsistent documents. We handle the hard part: parsing 60+ formats (PDFs, HTML, scans, slides), retaining layout like tables and lists, adding metadata like page numbers and element IDs, and processing massive.
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Check out our latest document transformation! We parsed this illegible doctor’s note from scribbles to structured data. See more real-life examples in our transformation gallery 👉 #AI #GenAI #ETL #ETL+ #RAG #UnstructuredData #LLM #MCP #EnterpriseAI
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📣 Documentation Roundup – June 25, 2025. 🧩 Enhanced Chunking Strategy:.- Updated character examples → - Updated title examples → - Updated page examples → - Updated similarity examples →
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Want to push GenAI performance further than basic vector search and chunking? Our new guide breaks down the latest RAG techniques—from smarter chunking and metadata filtering to GraphRAG, hybrid search, and agentic workflows. What’s inside:.🔹 Why naive RAG fails and how to fix
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📣 Documentation Roundup – June 19, 2025. 🖼️ New Visual Examples:.- See how Unstructured partitions images and tables in PDFs → - Explore visual examples of different chunking strategies → 🔗 Connector & Endpoint Updates:.-
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RAG chunking isn’t one-size-fits-all. Maria shares the right questions to ask so you can get it right for your data 👇.
Asking “What is the best chunk size for RAG?” without any additional context is like asking, “What’s the best thing to wear?” Wear where? What’s the weather like? What size are you? Are you going to a wedding or hiking a trail? There’s no single answer that works for every.
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