Unstructured

Let's say you now have an Unstructured workflow that uses your source documents as input and delivers Unstructured's AI-ready outputs based on those documents into a DataStax Astra DB collection. But how do you unlock all of that output's rich metadata, context, relevance, and meaning to make quicker and more confident decisions and to complete related tasks faster and easier? Well, we've just published instructions showing you how to connect Claude Desktop to your Astra DB collection. Claude Desktop allows you to use natural language to start chatting with Unstructured AI-ready outputs or feeding those outputs into your agentic AI workflows—with no code or programming required! Try it today: https://lnkd.in/e83ZeGvp

A walkthrough of Unstructured's structured data extractor in the user interface is now available. This demo extracts outputs from a tax form with cleanly labeled fields like taxpayer name, dependents, and line-item tax values based on a JSON schema you define. Flip on "schema only output" to get just the extracted records with smaller payloads, intuitive column names, and ready to drop into a database. This walkthrough is a great example of quickly and easily turning a form-based document into clean, query-ready data. Watch the 8-minute demo at https://lnkd.in/e4cN5G4F

A new walkthrough of using the Unstructured API (via Postman) to create, list, and get both workspace-scoped and workflow-scoped webhooks has just been posted. This demo creates two notification channels and then runs a workflow to send two "job completed" payloads to the receiver. You can extend this demo to wire send event notifications to email, Slack, AWS Lambda, Azure Functions, or any receiver of choice. Watch the 6-minute demo at https://lnkd.in/eZC_pHVa

Webhooks are live in Unstructured. When your jobs run, complete, or fail, a signal fires automatically to any endpoint you control. Slack, Lambda, or anything that accepts a POST request. Your downstream systems react the moment it happens. Docs: https://lnkd.in/ePyA3Q_j

Check out this new episode featuring our own Lindsay Gonzalez on Culture Uncovered. Great convo on Unstructured, growth, and building a strong culture. 💥

When you combine two datasets to train a model, what could go wrong? Turns out, a lot. We've been working on something we think is genuinely new in document AI. It's called agentic label harmonization. And it came out of a problem we kept running into: even when you have great data, combining datasets from different sources can quietly break your model. It's not obvious or dramatic. The loss converges. The metrics look reasonable. But the model somehow learns to become confused. Our new paper, Improving Layout Representation Learning Across Inconsistently Annotated Datasets via Agentic Harmonization, digs into exactly why this happens and how we built a solution using an AI agent to reconcile annotations before training even begins. The result: better document detection, cleaner table extraction, and a model that actually understands layout instead of memorizing noise. Check out our latest paper → https://lnkd.in/eiC-UVcy Read the full blog → https://lnkd.in/eAT74S8M #MachineLearning #AI #DocumentAI #Finetuning #TrainingData #DataQuality #AnnotationConsistency #DocumentParsing #Unstructured

ICYMI: we put together a deep dive on advanced RAG techniques last year and it's still one of the most useful things we've published. Most teams get RAG working at a basic level pretty quickly. Vector search, chunking, a retrieval step. But then it plateaus. Responses feel off, retrieval misses things, and it's not obvious what to fix. That's what the guide gets into. Re-ranking, hybrid retrieval, query rewriting, multimodal enrichments, agentic workflows. The techniques that move you from "RAG is kind of working" to something that actually holds up in production for all of your use cases. Check it out 👉 https://lnkd.in/eKrCiyWk #RAG #AI #GenAI #DataEngineering #UnstructuredData #Unstructured  #LLMs #AgenticAI #VectorDB

We've just posted a quick walkthrough of setting up Unstructured webhooks via the user interface. You'll see how to specify a webhook URL, pick the "job completed" event, save it, and immediately see POST payloads land when a workflow run finishes. From there, you can wire it up to downstream actions like email, SMS, or Slack, or swap in receivers like AWS Lambda or Azure Functions. It's a great way to turn pipeline events into real-time notifications or trigger the next step in your stack. Check out the 3-minute demo at https://lnkd.in/ej5dDD8z

We're live today. Joining IBM at 10am PT / 1pm ET to talk about what happens when enterprise AI initiatives scale without a shared data foundation — and what it actually takes to fix it. If you're in, register below. See you there. Register here: https://lnkd.in/esfP-gbv #AI #EnterpriseAI #RAG #UnstructuredData #Unstructured #TheGenAIDataCompany #IBMwatsonx

ICYMI Make sure to sign up for our webinar tomorrow on Taming Enterprise AI Sprawl with IBM. Details below 👇 🗓️ Date: Tuesday, April 21 at 10am PT / 1pm 🎙️ Speakers: Austin Eovito (IBM) & David Donahue, (Unstructured) 🔗 Link: https://lnkd.in/esfP-gbv