The Unstructured Data Problem No One Solved Until Now
Автор: Context Window Podcast
Загружено: 2026-03-02
Просмотров: 5
Описание:
Ed Anuff and Anant Jhingran are kicking off the first podcast of the new year with a topic that’s quietly blocking most AI success stories: Unstructured data.
Not models. Not agents.
We’re joined by Peter Staar, Principal Research Scientist, AI for Knowledge at IBM, to talk about: 👉 Docling, Langflow, and what it actually takes to make unstructured data usable for AI.
If you’ve ever built a RAG pipeline that looked right but behaved… unhinged, assumed OCR + chunking = “good enough” or wondered why AI pilots stall after the demo phase: This one’s for you!
What we dig into:
👉 Why unstructured data isn’t a side quest, it’s the problem
👉 What most teams miss when they treat documents as “just text”
👉 How Docling changes ingestion by saving structure (layout, hierarchy, semantics)
👉 How Langflow fits into real, visual ingestion-to-agent workflows
👉 Why ingestion quality now determines AI ROI
Peter also shares why 2026 will be the year of applications built on Docling.
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