Christopher Maddock

You’ve known Unstructured as the company to power your RAG applications; first we transformed your PDFs to JSON, then we built ETL pipelines for delivering chunked, embedded JSON to Delta Tables that enabled LLMs with low-latency RAG. Now we’re changing the model entirely. Imagine if agents could take context from your whole enterprise, without having to copy every document into Databricks? Foundation builds enterprise-wide context on demand. Source documents stay where they are. Extracted data lands in ephemeral Postgres provisioned via Neon, with pointers for the agent back to the originals. All your data outside the Databricks ecosystem is now workable from inside it. This talk is for every team still maintaining a pipeline.

LLM agents aren’t just answering questions — they’re running entire workflows. In this talk, we’ll show how agents can autonomously ingest, process and structure unstructured data using Unstructured, with outputs flowing directly into Databricks. Powered by the Model Context Protocol (MCP), agents can interface with Unstructured’s full suite of capabilities — discovering documents across sources, building ephemeral workflows and exporting structured insights into Delta tables. We’ll walk through a demo where an agent responds to a natural language request, dynamically pulls relevant documents, transforms them into usable data and surfaces insights — fast. Join us for a sneak peek into the future of AI-native data workflows, where LLMs don’t just assist — they operate.