Ryan Thill

Lium Ai - Ai for Complex Data

Lium is a collaborative AI platform that helps domain experts get reliable answers from messy, massive, multimodal datasets. Connect terabyte size data in any format, ask questions in plain English, generate knowledge artifacts, and turn verified analysis into reusable workflows your team can build on. Lium brings together data across geospatial, energy, space, and other complex domains so work that once required weeks of engineering can happen in a single conversation.

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Ryan Thill

Hey Product Hunt! Ryan here, one of the co-founders of Lium.

A quick story on how we got here.

A few years ago, my co-founder and I had been working with AI long enough to see both its immense potential and its limitations. The thing we kept coming back to was that much of the world’s most important data is still incredibly hard for AI to work with: too large, too complex, too multimodal, too domain-specific. That’s what led us to build Lium.

Lium is an agentic harness purpose-built for large, complex, multimodal data. It helps teams connect with terabyte-scale datasets, ask critical questions where the answers can’t be hallucinated, generate meaningful knowledge artifacts, and turn ad hoc analysis that used to take weeks or months into repeatable, collaborative workflows in minutes.

We sometimes describe it as: if Cursor and Notion had a baby, it would be named Lium.

We built Lium to be easy enough for anyone to use, including scientists, analysts, operators, domain experts, and data teams working with the messy, massive, high-stakes data that powers the real world.

We’ve poured our hearts into this and would genuinely love your feedback.

Please give Lium a try and let me know what you think!

Alina Tyslenok

Congrats on the launch! The idea of making large-scale multimodal data accessible through collaborative AI workflows is really compelling.

Ryan Thill

@alina_tyslenok_ Thank you Alina, appreciate the kind words.

Dhiraj Patel

Nice concept. What's the biggest type of data complexity it handles that traditional BI tools consistently fail at?

Ryan Thill

Hi @dhiraj_patel5 , great question!

We work with a wide range of complex datasets from advanced industries, including 3D seismic volumes, NDT/inspection data for semiconductor and manufacturing workflows, hyperspectral and remote-sensing imagery, and other large multimodal scientific and engineering datasets.

We also see teams use Lium on large, messy business data, especially in areas like marketing and finance, when the data is spread across files, systems, and formats that are hard to analyze together.

A concrete example: Lium connects to multiple NOAA sources, so you can query terabytes of climate and weather data, often stored in obscure formats, just by asking questions in natural language. To try it, you can sign up for free and turn on the Weather & Climate domain pack, which includes the data connections and a set of pre-built tools.

Hope that helps, and I’d love to hear your feedback if you test it out!