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.
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!
The part that doesn't show up in the demo but ate most of our engineering time: making "ask a question in plain English" actually reliable on terabyte-scale, multimodal data. Anyone can wire an LLM to a SQL generator. The hard problems are the unglamorous ones — connecting to formats that were never meant to be queried conversationally (3D seismic volumes, hyperspectral imagery, the NOAA climate formats nobody enjoys parsing), keeping analysis reproducible and reusable instead of one-off, and building guardrails so the model says "I don't know" instead of confidently hallucinating on high-stakes data.
Scaling the processing platform was its own beast. A single natural-language question can fan out into a workload that touches terabytes, so the engine has to parallelize across compute, scale elastically with demand, and handle backpressure gracefully instead of falling over on the heavy queries — all while keeping cost sane so you're not paying for a cluster that sits idle between questions. Getting that to feel instant from the user's side while it's churning underneath was a genuinely hard line to walk.
The design goal we kept coming back to: an analysis you run today should be a reusable, inspectable workflow your teammate can build on tomorrow — not a screenshot in Slack.
Hey ya'll, Aron here from LiumAi growth team.... .8 reasons I think Lium should exist: yes I am biased :)
1. Messy, massive, multimodal data shouldn't stand between humanity and its next breakthrough.
2. The answers to some of humanity's hardest problems are trapped inside that data.
3. Data shouldn't require a PhD in SQL to understand.
5. Humanity reached the moon 🌔 . Enterprise data is still a disaster.
6. Breakthroughs happen when curiosity moves faster than complexity.
7. Some of the world's most valuable discoveries are hiding at the intersection of data that's never been connected.
8. It's fun to support startups trying to do a little good in the world. It's even more fun to watch good people win.
If any of that resonates, we'd love your support today.
Clean launch for Lium Ai: Ai for Complex Data. How are you measuring whether it is working for people?
Nice concept. What's the biggest type of data complexity it handles that traditional BI tools consistently fail at?
Congrats on the launch! The idea of making large-scale multimodal data accessible through collaborative AI workflows is really compelling.
About Lium AI on Product Hunt
“AI for Complex Data”
Lium AI launched on Product Hunt on June 11th, 2026 and earned 122 upvotes and 11 comments, placing #13 on the daily leaderboard. 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.
Lium AI was featured in Artificial Intelligence (470.9k followers), Science (1.4k followers) and Data Science (3.8k followers) on Product Hunt. Together, these topics include over 100.8k products, making this a competitive space to launch in.
Who hunted Lium AI?
Lium AI was hunted by Ryan Thill. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Lium AI stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
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!