Unstructured

Happy Thanksgiving! 🦃 🍽️ 🍂

Traditional parsers miss the details that matter. We don’t. Unstructured’s advanced high fidelity pipeline breaks your document into individual elements, then applies targeted refinements to improve the final quality of the output. Tables get table-specific enrichment. Images get image-specific enrichment. Text gets generative OCR enrichment. Each enrichment layer enhances the parsed output ultimately giving you the highest quality results. In this example, the initial parse identified a table, but the part numbers were missing. This is where most solutions would stop. Not us! Unstructured fixed the issue by applying a VLM-based table enrichment, and recovered the missing part numbers to produce a complete, accurate table. ✨ Try it yourself! See how Unstructured’s enrichment pipeline can dramatically improve the quality of your document processing: https://lnkd.in/ebhGexr9 #AI #GenAI #UnstructuredData #DocumentAI #RAG #Unstructured #TheGenAIDataCompany

🚧 If your RAG pilot is stuck, you’re not alone. *And* it’s fixable. Most RAG pilots stall because teams underestimate what it actually takes to make these systems production-ready. The solution is getting the fundamentals right: security, governance, evaluation, alignment, and a real business case. So we set out to clarify what “good” actually looks like - and the patterns that make RAG work at enterprise scale. We covered all of this in our recent webinar on How to Build Enterprise-Ready RAG Systems, and pulled together the 5 key takeaways for anyone who missed it: 1. Despite periodic "RAG is dead" proclamations, the RAG market is expected to grow at a 40% compound annual growth rate through 2030, reaching over $10 billion. As long as LLMs remain stateless and enterprises need secure, auditable access to proprietary data, RAG remains the dominant pattern for enterprise AI—including in the emerging agentic era. 2. Find the high-friction, high-value use case - that’s your ROI sweet spot. The biggest reason RAG pilots fail to move into production is that they lack a compelling business case. Success means identifying where your most expensive employees waste the most time searching for critical information, then measuring against actual business KPIs 3. Choose your security model based on use case requirements. For simple use cases, go with RBAC. If you need to mirror data permissions from source systems, then you will likely need a combination of retrieval pre-filtering and post-filtering via a separate authorization service. 4. Build a golden set of 100-300 critical Q&A pairs that runs against every code change (CI/CD safety net), then couple it with thumbs-up/thumbs-down feedback in production. Failed responses trigger triage to identify retrieval vs. generation failures, and validated fixes expand your golden set, continuously strengthening your system over time. 5. Without alignment, enterprises end up with thousands of fragmented RAG pilots across departments. The solution: align your organization around one secure, stable ingestion and RAG ETL layer, eliminating the DIY chaos and enabling developers to build actual AI products instead of maintaining custom pipelines. Check out the full webinar recording here → https://lnkd.in/eyP9wkFs #AI #GenAI #RAG #EnterpriseAI #LLM #ETL #RAGinProduction

📊 Tables test a document parser’s real strength: merged cells, nested structure, odd layouts, and more. In our recent benchmarks, Unstructured leads with the highest scores on table-related metrics that capture the full-stack performance: content accuracy, spatial fidelity, and cell detection. Reliable, format-agnostic and accurate table extraction drives better search, RAG results, and other downstream applications. Explore the methodology and check out the benchmarks 👉 https://bit.ly/3M3XyJs

Ever wonder how your parser actually sees a document? 👀 Unstructured’s new document transform experience features visual bounding box overlays to help you quickly see the various content pieces that make up your documents. Unstructured uses these bounding boxes behind the scenes to identify, label, extract, and process different kinds of content within your documents. These bounding boxes help improve the accuracy, quality, relevance, and context of each piece of content before they are sent over into your databases and vector stores for use by your chatbots and agents. These enhancements enable your chatbots and agents to find relevant information faster and easier and provide you with deeper business insights vs. what you get by doing traditional full-text, keyword, or lexical searches alone. The new document transform experience also has this handy feature: you can select a bounding box on the left and see how Unstructured processed that piece of content on the right! Try out Unstructured’s new document transform experience today! https://lnkd.in/ebhGexr9

Document parsing hallucinations silently break RAG and downstream AI systems.
That’s why we track Tokens Added, a metric that quantifies spurious content generation. In our recent benchmarks, Unstructured achieves the lowest hallucination rate across all tools benchmarked. Fewer phantom tokens, more trustworthy data. Full results 👉 https://lnkd.in/eg2f2hGH

Traditional text-matching metrics for document parsing break down when output formats differ. Adjusted CCT fixes that by recognizing semantic equivalence even when structure changes. In our recent benchmarks, Unstructured leads with a 0.917 Adjusted CCT, proving our pipelines preserve meaning, not just characters. Check out the benchmarks 👉 https://lnkd.in/eg2f2hGH

Connecting autonomous agents to enterprise data unlocks great potential but also introduces profound risk. Data-connected agents can amplify productivity and decision-making, but they also expand the attack surface and complicate governance. Our latest blog post breaks down how enterprise architects can navigate that tension — capturing the upside while controlling the downside — by mastering the canonical data provisioning patterns. You’ll learn: * Why context engineering (not fine-tuning) is the foundation for 95% of enterprise use cases * How RAG, GraphRAG, and Text-to-SQL patterns fit into agentic architecture * How retrieval, tool integration, and event-driven triggers form the backbone of the agentic data fabric Read the blog post to discover a blueprint for safely and effectively connecting agents to enterprise data systems and ensure a clear path from pilot to production. https://lnkd.in/ep_FGVdD

Couldn't be more excited to share v1 of our industry benchmarks. New models and preprocessing strategies are announced almost every week, and up until today, it's been almost impossible to draw conclusions with any confidence. What we found: - Different model pipelines excel at different tasks: Some configurations optimize for table extraction, others for content fidelity, and others for structural understanding - Trade-offs exist: A pipeline with the highest table accuracy might have slightly different hallucination characteristics than one optimized for element alignment - Consistent leadership: Across the diversity of configurations, Unstructured pipelines consistently outperform competitors on key metrics - Informed choice: You can select the pipeline that best matches your specific document types and downstream requirements Early next month we will be open sourcing our labeled dataset so that everyone can independently evaluate the performance of their preprocessing pipelines. No cherry picking, overfitting to specific docs, or obfuscation. With Unstructured you can trust us for maximum transparency for which models we're using, how we're using them, and how they're performing. Deedy Das Kevin Petrie Edward Calvesbert Maria Pere-Perez Prasad Kona Dan Lucarini