How Lium turns physical-world data into answers
About this episode
Josh Knutson and Ryan Thill discuss how Lium transforms complex physical-world data into actionable insights.
Josh Knutson and Ryan Thill are building Lium for a problem that sits just outside the usual AI demo. Most AI tools are very good at text, code, and spreadsheets. Lium is focused on the messier stuff, the huge physical-world data sets that sit inside farms, climate labs, energy systems, logistics networks, and other operations where the answers are buried under terabytes of data. Knutson, the CEO and co-founder, describes Lium as an “agent harness” or a cloud operating system for agents. The idea is to give language models the tools they need to work over large, complex data sets that they cannot handle well out of the box. Instead of asking a data scientist to build a pipeline every time someone has a question, Lium lets subject matter experts ask questions in natural language and then builds the tools and workflows needed to answer them. Thill, co-founder and president, said the core user is often not a software engineer. It is the person who knows the domain, knows the data matters, and knows there are answers inside it, but cannot easily get them out. He gave the example of a farm operator working with soil reports, NOAA data, tractor data, and crop performance information…
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