Unstructured

Building production RAG? Don't miss this game-changer: Delta-Aware Document Processing Just set `reprocess_all` flag to False in your Unstructured workflow and watch your processing costs plummet. Our platform tracks document state across runs and only processes what actually changed. Key benefits:
 ✅ 90%+ cost reduction for incremental updates
 ✅ Automatic change detection for new/modified files
 ✅ Zero processing costs for unchanged documents
 ✅ Keeps knowledge base fresh without breaking the bank Check out the notebook that walks you through an example: https://lnkd.in/e-DVp6kC #EnterpriseRAG #GenAI #RAG #Unstructured #TheGenAIDataCompany

If you’ve ever tried to compare document parsing systems and thought, “None of these benchmarks look anything like the documents we have,” you’re not alone. So… we built a new one. Meet SCORE-Bench: an open, expert-annotated dataset designed for real world with real documents. Not clean PDFs. Not synthetic labels. Actual messy, multi-domain, multi-format, occasionally painful-to-read documents - the kind that make evaluation interesting. SCORE-Bench includes:
 • complex tables (nested, irregular, multi-page)
 • handwritten and scanned forms
 • domain-heavy (finance, healthcare, legal, etc.)
 • dense layouts
 • all annotated by human experts It’s paired with SCORE, our evaluation framework that doesn’t punish generative models just because they choose rich representation instead of plain text. Everything is fully open: data, annotations, metrics, code, evaluation results. Learn more: https://lnkd.in/etjCUHv6

Check out SCORE-Bench on Hugging Face! Most benchmarks are built on clean PDFs that don’t reflect real-world complexity. SCORE-Bench changes that. It’s a diverse dataset of complex documents manually annotated by experts to be paired with SCORE, our interpretation-agnostic evaluation framework for generative parsers. Early benchmarking highlights that Unstructured pipelines lead across content fidelity, hallucination control, and structural understanding, especially for complex tables. Full blog + dataset: https://lnkd.in/etjCUHv6

If your RAG system slows down as your knowledge base grows, this is why. Enterprise knowledge doesn’t sit still. Documents get edited, files are replaced, and content evolves - and most RAG pipelines aren’t built to keep up. They reprocess *everything* instead of focusing on what actually changed, which slows teams down and inflates costs. In today’s webinar, we’ll break down how incremental processing keeps enterprise RAG fresh, accurate, and efficient by updating only what’s changed instead of reprocessing everything. Ajay Krishnan and Paul Cornell, Jr. will walk through: - What incremental processing is - and when you don’t need full reprocessing - How connectors detect new, updated, or replaced documents - Why versioning strategies matter (latest-only vs. historical) - A practical demo of RAG staying current as documents evolve Join us TODAY at 10a PT / 1p ET to learn more! 🔗 Register: https://lnkd.in/e8PPxWJn What part of keeping RAG “fresh” has been the hardest for your team? 👇

Build your entire AI workflow inside Azure: Azure Blob Storage is now a supported Unstructured destination! We’ve expanded our catalog of connectors to support Azure Blob Storage as a destination, making it easy to move structured, GenAI-ready outputs directly into your Azure environment. This unlocks: - End-to-end ETL in Azure with minimal setup and zero connector maintenance - Produce high-quality AI-ready data that downstream Azure services can immediately consume for search, retrieval, and AI workflows If your GenAI systems run  on Azure, this makes data ingestion and transformation smoother, faster, and easier. Get started today: https://lnkd.in/eeRcy3i8

Today, we’re releasing SCORE-Bench: a diverse, expert-annotated dataset for benchmarking real-world document parsing systems. Designed to reflect the complexity of production documents, it includes: • Complex tables with nested structures and merged cells
 • Diverse formats: scanned documents, forms, reports, and technical manuals
 • Real-world challenges: handwriting, poor scan quality • Multiple domains: healthcare, finance, legal, public sector, and more SCORE-Bench pairs with SCORE, our interpretation-agnostic evaluation framework for generative parsers, to enable fair, reproducible benchmarking across modern document parsing systems. Early results highlight that Unstructured pipelines deliver the strongest balance of content fidelity, low hallucination, and accurate structural understanding, especially for complex tables. Read more in our blog: https://lnkd.in/etjCUHv6

Most RAG demos assume documents never change. Real systems don’t get that luxury. Docs get rewritten. PDFs get replaced. Teams reorganize content. And the naïve solution of reprocessing everything on every update gets brutally expensive when each file requires VLM partitioning, chunking, embedding, and vector DB writes. In this new notebook, we walk through how to make RAG pipelines change-aware using Unstructured’s built-in document state tracking. With a single configuration flag (reprocess_all: False) your pipeline automatically: * Detects new or modified documents in S3 * Skips unchanged files entirely * Regenerates embeddings only when needed * Cuts processing costs dramatically * Keeps your downstream collection always fresh If you’re running or planning to run RAG in production, this approach is essential for keeping costs sane while maintaining up-to-date knowledge. Check it out and see how delta-aware RAG actually works in practice. https://lnkd.in/e-DVp6kC

Struggling to make sense of a chart or graph? Your chatbots hit that same wall - unless they’ve got Unstructured! 🤓 Unstructured’s image description enrichment can serve as a “first take” on these kinds of graphs and charts (and other image types as well). We send the images to a VLM to get back a summarized, plain-language description of what is going on in each graph, chart, or image. These image descriptions can help you (and your chatbots and agents) get quick context and develop deeper insights, even when you’re not a domain expert, helping you to make better decisions faster! Try it yourself for FREE today! 👉 https://lnkd.in/ebhGexr9 #AI #GenAI #UnstructuredData #DocumentAI #RAG #Unstructured #TheGenAIDataCompany

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