Unstructured

💡 Your documents are more than text! Oftentimes they’re networks of interconnected knowledge waiting to be unlocked. Traditional RAG pipelines treat documents as isolated chunks. Works for simple queries, but they miss the relationships that actually matter: how products relate, which datasets drive results, or which procedures lead to outcomes. In our latest notebook we combine Unstructured + Neo4j to build GraphRAG: - Extract entities like models, datasets, metrics, tasks, and more - Map explicit relationships (trained_on, evaluated_on, achieves) in a knowledge graph - Traverse this graph to answer complex questions—not just find keywords We demoed this on the GPT-2 research paper, but the approach applies to: - Technical documentation → understand APIs, parameters, dependencies - Customer support → connect tickets, products, and account managers - Medical research → link patients, treatments, and outcomes Stop treating documents as isolated text blobs. Start uncovering the knowledge graphs hidden inside them. 🔗 Explore the full notebook and build your very own GraphRAG system: https://lnkd.in/eduJCzG3 #DocumentAI #IntelligentAutomation #DataAccuracy #StructuredData #DataQuality #RAG #AI #GenAI #ETL #UnstructuredData #LLM #MCP #TableTransformation #DocumentAI #VLM #EnterpriseAI #RAGinProduction #Transformation #Quality #LLMready #SourceConnectors #Parsing #Unstructured #TheGenAIDataCompany

We’re excited to announce a new OEM partnership between IBM and Unstructured. Together, we’re tackling one of the biggest barriers to enterprise AI: turning the 80% of data that’s unstructured into clean, AI-ready fuel. By combining IBM watsonx’s hybrid, open lakehouse with Unstructured’s document processing, enterprises can unify access, preparation, and governance for both structured and unstructured data—unlocking faster, more reliable AI. Learn how watsonx.data + Unstructured enable production-ready pipelines and RAG systems built on trusted, AI-ready data. And sign up for our webinar on 10/29 on building RAG pipelines with IBM watsonx and Unstructured. 🔗 https://lnkd.in/eF8sPYDM #IBM #watsonx #Unstructured #IBMwatsonx #GenAI #AI #ETL #ETLplus #RAG #LLM #DataTransformation #UnstructuredData #EnterpriseAI #AIready #RAGinProduction #TheGenAIDataCompany

Join us in just a few hours! 👇

Precision context engineering starts with robust, enterprise-grade RAG. When retrieval is accurate, structured, and governed, every downstream AI system becomes more reliable, explainable, and compliant. That’s the promise of RAG using the Unstructured ETL+ platform: - Process 65+ file types into structured, searchable context - Enrich with metadata and structure-aware chunking - Enforce SOC 2, HIPAA, and GDPR compliance at the source - Scale horizontally across workloads — securely and predictably Ready to level up your use case's context engineering? Join us for tomorrow's webinar: Context Engineering with Precision over Mixed Content. You'll learn how Unstructured enables precision RAG — turning messy, mixed enterprise content into production-grade context engineering. Because context quality is only as strong as its retrieval layer.

We're thrilled to be featured in this comprehensive guide from the Neo4j Developer Blog! Alex Gilmore's deep dive into Knowledge Graph Generation shows how Unstructured powers the lexical component of knowledge graphs for GenAI applications. Our document parsing capabilities help transform unstructured data into structured knowledge that enables GraphRAG — making it possible to answer complex queries that traditional vector search simply can't handle. The article details the complete journey from raw documents to production-ready knowledge graphs, with Unstructured handling the critical first step of document processing. Whether you're building medical Q&A systems, research assistants, or enterprise knowledge bases, this guide provides the strategies and best practices you need. 📖 Read the full article to learn the strategies and methods for knowledge graph development, which provides reliable and accurate context for GenAI: https://lnkd.in/e6S95YwY

“We already use Amazon Textract, which works well - why would we use Unstructured?” We’ve heard this line from a wide range of our customers during discovery calls: financial institutions, insurers, retail giants, and more. And more often than not, by the end of the call, they realize their document processing pipeline isn’t the off-the-shelf feature offered by their Cloud Service Provider (CSP) anymore—it’s an infrastructure project in disguise. The moment your roadmap includes PowerPoints, scanned images, RAG pipelines, or multi-cloud data sources, your neatly packaged Amazon Textract-based MVP solution starts requiring custom internal development in order to keep up with your requirements. That’s when a dedicated platform like Unstructured becomes the more flexible, cost sensitive option. To help you navigate the tradeoffs between using your CSP's default Document Processing solution vs a dedicated platform like Unstructured, our Principal Solutions Architect Daniel Schofield has provided a helpful summary of the factors and considerations that play into that decision in our most recent blog post: The Tradeoffs Between Using A Cloud Service Provider’s Document Processing Solution vs a Dedicated Document AI Platform 🔗 https://lnkd.in/eHpw-B3m #DocumentAI #IntelligentAutomation #DataAccuracy #StructuredData #DataQuality #RAG #AI #GenAI #ETL #UnstructuredData #LLM #MCP #TableTransformation #DocumentAI #VLM #EnterpriseAI #RAGinProduction #Transformation #Quality #LLMready #SourceConnectors #Parsing #Unstructured #TheGenAIDataCompany

And we're live! Come join us: https://lnkd.in/ei8CWinK

From humans to agents: the “user” of enterprise data is changing fast. Listen as Brian S. Raymond dives into agent auth, secure access to emails/CRMs, and the backbone Unstructured provides.

🚨 Big news from Unstructured: we've just open sourced our SCORE framework research, setting a new standard for evaluating generative document parsers! We're thrilled to announce we just published a new academic paper on arXiv: SCORE: A Semantic Evaluation Framework for Generative Document Parsing. Tomorrow, we’re pulling back the curtain in a live webinar to reveal the pioneering methodology we use internally to cross-evaluate diverse document transformation outputs from generative models. Why you should care: - Current benchmarks mislead—they over-reward neat text extraction while ignoring hallucinations, broken structures, and rich, diverse alternative representations. - Generative parsers are exploding in popularity, but we’ve had no fair way to measure them… until now. - SCORE redefines document parsing evaluation, surfacing the real trade-offs between models. This is a must-watch if you work in document AI, RAG, or intelligent automation. 📅 When: Tomorrow, Wednesday, October 8th 🎤 Hosts: Daniel Schofield & Antonio Jose Jimeno Yepes View comments below for the registration link! See you there. #StructuredData #DataQuality #DocumentAI #DataAccuracy #RAG #AI #GenAI #ETL #UnstructuredData #LLM #MCP #TableTransformation #DocumentAI #VLM #EnterpriseAI #RAGinProduction #Transformation #Quality #LLMready #SourceConnectors #Parsing #Unstructured #TheGenAIDataCompany

👋 We’re at IBM TechXchange 2025! Catch David Donahue, Head of Strategy at Unstructured, on stage with Edward Calvesbert, VP of Product Management at IBM watsonx. They’ll share how Unstructured and IBM are partnering to make enterprise data AI-ready — turning complex documents, PDFs, and images into structured, usable data inside watsonx.data to power the next generation of GenAI applications. 📆 Today 10/7 @ 10:30 AM 📍 Lake Nona, Lobby Level, Hilton #IBMTechXchange #watsonx #Unstructured #IBM #GenAI #AI #ETL #ETL+ #RAG #LLM #DataTransformation #TheGenAIDataCompany