Unstructured

The AI industry moves at light speed. Can you keep up? New research papers, product announcements, and breakthroughs drop constantly across multiple platforms and in different formats (HTML blog pages, PDFs with research papers, newsletters in emails, etc). Staying informed means hours of manual aggregation and reading. What if you had autonomous agents simply prepare a weekly TLDR for you? In the latest notebook, we show how you can build two autonomous agents that run the entire TLDR pipeline. You’ll learn how to: ✓ Scrape ArXiv papers and AI blogs ✓ Process PDFs, and HTML pages with Unstructured and stores structured content in MongoDB ✓ Build an orchestrator agent that can autonomously manage data processing workflows in Unstructured ✓ Build a summarizer agent that can autonomously generate weekly content summaries Built with Unstructured, LangChain, MongoDB, and OpenAI. Check out the notebook to see how to build your own Agentic TLDR: https://lnkd.in/eihZdEed #AI #MachineLearning #Automation #DataProcessing #AgenticAI #AutonomousWorkflows #GenAI #ETL #ETL+ #RAG #SCORE #Benchmarks #UnstructuredData #LLM #MCP #EnterpriseAI #RAGinProduction #LLMready #Unstructured #TheGenAIDataCompany

Today’s multimodal models parse documents well, but their outputs vary widely by prompt. Old metrics expect one "right" format, so they mis-score valid interpretations. That’s why we built SCORE: Structural & Content Robust Evaluation. SCORE is the first framework designed for the unique evaluation challenges of generative document parsing. SCORE: - Leverages existing metrics with more flexibility, enabling more fair content accuracy evaluation - Separates omissions from hallucinations - Checks hierarchy consistency - And more! The result: fair, interpretable benchmarks for modern document AI—so teams can compare different models and parsing systems more honestly, without the bias of using metrics designed for the previous generation of OCR models. Renyu Li #AI #GenAI #ETL #ETL+ #RAG #SCORE #Benchmarks #UnstructuredData #LLM #MCP #EnterpriseAI #RAGinProduction #LLMready #Unstructured #TheGenAIDataCompany

RAG systems are only as strong as the data they’re built on. Enterprise teams are under pressure to make GenAI *actually* reliable, not just impressive in a demo. The problem? Most retrieval pipelines weren’t designed for the complexity of real enterprise data. PDFs, emails, reports, tables, HTML, images – each one breaks the consistency and structure these systems rely on. So while everyone’s talking about tuning prompts or swapping models, the real challenge (and opportunity) is building retrieval layers that are accurate, explainable, and compliant from the start. That’s what unlocks trustworthy AI. And that’s what we’re digging into next week with IBM watsonx 💪 Join us next Wednesday, October 29 at 10am PT for a live webinar on building RAG+ pipelines for more reliable AI. 🎙️ Speakers: - Stevan Slusher, Product Management, IBM watsonx - David Donahue, Head of Strategy, Unstructured - Paul Cornell, Senior Staff Technical Writer, Unstructured We’ll walk through: - Integrating unstructured data directly into watsonx.data - Building retrieval pipelines that are accurate, explainable, and compliant - Delivering enterprise-grade AI systems with reliable context - Plus, a live demo and Q&A with the team! If you’re working on retrieval or data architecture, you won’t want to miss this one. #AI #GenAI #ETL #ETL+ #RAG #UnstructuredData #LLM #MCP #EnterpriseAI #RAGinProduction #LLMready #Unstructured #TheGenAIDataCompany

Join us in just a few hours as Simon Coombes and Kevin Krom walk through how to securely process unstructured data at scale with the right deployment model 👇

Email processing for RAG may seem straightforward, until you realize how much structure gets lost in translation. Most approaches flatten everything:  • Rich HTML formatting becomes plain text  • Email metadata is lost  • Nested structures and attachments are ignored That means manual preprocessing, custom parsers, and fragile regex patterns just to extract clean content for your RAG pipeline. 🚀 Unstructured's email partitioner does it differently. It intelligently separates and structures email components automatically.  • Headers are preserved as metadata  • Body content maintains hierarchical structure  • Processing also incorporates email attachments to preserve context • HTML formatting is converted to semantic chunks 🔧 Test your emails:  • Log in to platform.unstructured.io and create a workflow • Upload an .eml test file to Unstructured.  • Set the partitioner node to VLM • Run the workflow and see structured chunks appear with proper metadata ✅ Our API endpoint also handles any email: newsletters, support tickets, marketing campaigns. Your RAG system gets clean, contextual chunks without the preprocessing headache. 👉 Try it for free: https://lnkd.in/ebhGexr9 #EmailProcessing #RAG #DocumentAI #Unstructured #MLEngineering #AI #GenAI #ETL #ETL+ #UnstructuredData #LLM #MCP #EnterpriseAI #RAGinProduction #LLMready #TheGenAIDataCompany

SaaS convenience or in-VPC control? When it comes to processing sensitive unstructured data, the answer is determined by your risk tolerance, compliance needs, and operational reality. On one hand, a SaaS solution gets you running in minutes with zero DevOps overhead. On the other, locking down your pipeline completely within your own VPC gives your security team peace of mind but can saddle your engineers with a significant operational burden. The reality is that most teams need to operate somewhere on this spectrum. The key is understanding all the options and their specific implications for security and operational load. At Unstructured, we offer three core deployment models that each come with their own set of tradeoffs: ☁️ Shared Multi-tenant SaaS: The "hands-off" model. Ideal for speed and convenience, but requires you to fully trust our security architecture, as data is processed in a shared, multi-tenant environment. 🏢 Dedicated SaaS Instance: The balanced approach. You get dedicated hosted resources and enhanced security through network isolation, striking a middle ground between convenience and control. 🔒 Fully In-VPC: The maximum control model. The entire data pipeline runs inside your network. It's the gold standard for compliance and sensitive data, but it makes your team responsible for all provisioning, scaling, and maintenance. Choosing the right model has massive implications for your security posture and your team's workload. There's no single "best" answer—only the one that's right for your data and your organization. #UnstructuredData #DataEngineering #CloudSecurity #DevOps #SaaS #VPC #DataGovernance #Scalability #RAG #RAGPipelines #AI #GenAI #ETL #ETL+ #RAG #UnstructuredData #LLM #MCP #EnterpriseAI #RAGinProduction #LLMready #Unstructured #TheGenAIDataCompany

💡 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.