Unstructured @UnstructuredIO

The agentic enterprise runs on data, and most of that data is a mess. Messy PDFs, scanned images, complex tables. Before any agent can act, that has to become something it can actually use. 📍 Catch us live in Austin this week. We’ll get into why agent-ready data is the real starting line for agentic workflows, and what it takes to get unstructured data production-ready. #AgenticAI #EnterpriseAI #GenAI #Unstructured

Check out our blog post here: unstructured.io/blog/your-lake…

The biggest blocker to enterprise AI isn't always the model. Sometimes it's a 200-page contract from 1997 sitting in SharePoint. Or a scanned invoice buried in an email thread. Or last week's sales call sitting in a recording platform. Your agents can't use knowledge they can't access. 👇

Managing user access across multiple systems gets messy fast. The fewer places admins have to manage access manually, the better. Keep access to Unstructured aligned with your IdP as users join, leave, or change groups. 👉 docs.unstructured.io/business/idp/o…

Parsing PDFs sounds easy until you try it. A scanned invoice and a digital manual might share the same file extension, but they shouldn't be parsed the same way. Pick the wrong strategy and you'll either lose information or waste money. Here's our framework for choosing👇 unstructured.io/blog/mastering…

Most document pipelines are quietly English-only. They work fine until someone uploads a Japanese manual, an Arabic contract, or a Chinese report and then the whole thing falls apart. Non-Latin scripts, right-to-left text flows, mixed character sets in a single document. Each one becomes a separate engineering problem, and before long you're maintaining different parsing logic for every language your users actually work with. Unstructured's partitioner handles this automatically so you get the same json schema out the other side regardless of what language went in. Your pipeline doesn't need to know the difference The output from the Japanese document below looks exactly like what you'd get from any English PDF. #MultiLingualData #RAG #AI #GenAI #DataEngineering #UnstructuredData #Unstructured #LLMs #AgenticAI #VectorDB

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And it doesn't stop at just financial documents! We structure all sorts of file types. Learn more here: unstructured.io/?modal=contact…

Getting data into AI-ready JSON is only half the battle. The next problem: actually making those outputs usable inside real workflows. We just published a walkthrough for connecting Claude Desktop directly to Unstructured outputs stored in Google Drive. Chat with parsed docs. Explore extracted metadata + structure. Feed outputs into agentic workflows. No custom UI or glue code required. Try it yourself: docs.unstructured.io/examplecode/to…

Try it yourself 👉 unstructured.io/?modal=try-for…

Newspaper layouts are where a lot of parsers fall apart. Columns. Captions. Images breaking reading flow. Text extracted in the wrong order. And once the structure breaks, the downstream AI output usually does too. This is what it looks like when Unstructured processes it 👇✨

Enterprise knowledge doesn't live in one place. Sales decks are in OneDrive. Contracts are in Azure. The important context from that client call is buried in an Outlook thread somewhere. That's not bad organization. That's just how work actually happens. The problem is when your RAG system can only see one of those places at a time. Connecting to multiple sources is the easy part. The harder part is what comes after — making sense of a chart buried in slide 47 of a PowerPoint, pulling a commitment out of an email chain, extracting the right figure from a complex Excel model without losing context. Every file type is a different problem. We wrote a walkthrough for building a pipeline that handles exactly that. Azure Blob Storage, OneDrive, Outlook — three sources, multiple file types, one workflow. Unstructured processes all of it into a universal format so when you ask "What did we promise the healthcare client?" the answer can draw from a presentation, a contract, and an email thread all at once. Try it yourself 👉 unstructured.io/blog/everythin… #RAG #AI #GenAI #DataEngineering #UnstructuredData #Unstructured #LLMs #AgenticAI #VectorDB

Happy hour with LMI is in full swing! 🍻 Come join us at:
 📍Yard House
50 Channelside Dr

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Most agentic AI conversations skip the hard part: what the enterprise infrastructure underneath *actually* needs to look like. Governance. Messy data systems. Reliability. Infrastructure that actually holds up in production. @ctmaddock is digging into all of it today at AI & Big Data Expo 👇 (And yes, we’ll also be at Booth #432 with swag 👀)

Unstructured @UnstructuredIO

4 weeks ago

Join @ctmaddock next Monday 5/18 at 2:10 PM at the AI and Big Data Expo. He'll be digging into the stuff most agentic AI conversations skip over: how do you actually govern this in an enterprise, what does the data infrastructure underneath need to look like, and what should a

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Parsing moves fast. New models, new techniques, something worth paying attention to seems to drop every few weeks. But Chunking is different. The questions you're working through are always the same: how big should a piece of context be before the embedding gets too coarse? Where should boundaries fall so you're not splitting ideas in half? How do you make sure what gets retrieved is actually what matters? Those questions don't change, no matter what's happening on the parsing side. And getting them right has a huge impact on how your downstream systems perform. We wrote a blog going back to basics on all of this. Check it out 👉 unstructured.io/blog/chunking-… #RAG #AI #GenAI #DataEngineering #UnstructuredData #Unstructured #LLMs #AgenticAI #VectorDB

RAG gets a lot harder once the data stops being clean text. PDFs. Images. Tables. Audio. Messy enterprise systems. That’s the real challenge behind production AI. Next week, we're joining @Teradata to break down what it *actually* takes to move from RAG demos → scalable agentic AI. 🔗 linkedin.com/events/7454926…

Join @ctmaddock next Monday 5/18 at 2:10 PM at the AI and Big Data Expo. He'll be digging into the stuff most agentic AI conversations skip over: how do you actually govern this in an enterprise, what does the data infrastructure underneath need to look like, and what should a C-suite do tomorrow morning vs. in 18 months. We'll also be at Booth #432 - equipped with fire swag and great convos. Swing by! #AIBigDataExpo #GenAI #EnterpriseAI