Unstructured

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

Getting documents into AI-ready JSON is only part of the problem. The next challenge is actually making that structured output usable inside real workflows. We just published a walkthrough showing how to connect Claude Desktop directly to Google Drive locations containing Unstructured outputs. That means you can: * chat with parsed document outputs using natural language * explore metadata, structure, and extracted context * feed structured outputs into agentic workflows * do it all without writing custom code A lot of enterprise AI workflows still break down at the “now what do we do with the data?” stage. This is a simple way to make structured document outputs immediately more accessible and actionable. Try it here: https://lnkd.in/e83ZeGvp

#ETEBA attendees, come by booth 115 to discuss how Unstructured can transform ALL of your organization’s data to become #Agentic and #GenAI ready! 🪄

Newspaper layouts are brutal for document parsing. Multiple columns. Images interrupting the flow. Captions sitting beside unrelated text. Tiny reading-order mistakes that completely change the meaning of the extracted content. This is what it looks like when Unstructured processes a document: every element identified, labeled, and sequenced in the correct logical order so the output is actually usable downstream. Because in production AI systems, structure matters just as much as extraction. #AI #GenAI #RAG #UnstructuredData #DocumentAI #Unstructured #TheGenAIDataCompany

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 👉 https://lnkd.in/e_fEMc-n #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

#IYKYK 😎 Swing by Yardhouse if you’re in town!

Most agentic AI conversations focus on the models. Far fewer focus on the infrastructure underneath: how the data is governed, what breaks in production, and what enterprise systems actually need to support this stuff at scale. Christopher Maddock is digging into all of it today at AI & Big Data Expo 👇 📍 Booth #432 - Come say hi to the full team! (And grab a hat 😉)

Come join us! 👉 https://lnkd.in/eTr94-JA