Lium does real data work by conversation. It connects to whatever a team already has — databases, files, APIs, instruments, internal tools — indexes and profiles what it finds, then reasons across all of it at once: writing code, building reusable tools, blending sources, and returning an answer in plain language.
The interesting problems are the ones that never show up in a demo: bespoke formats, genuinely messy data, and terabyte scale. That is the work — geospatial and satellite imagery, energy, space, infrastructure monitoring, subsurface and geology, and scientific research.
As CTO and founding engineer I own both the architecture and the build: the integration layer that makes strange data legible, the agentic loop that writes and runs code against it, and the on-demand compute that provisions itself for heavy jobs so nobody has to think about infrastructure. Analyses and tools are saved as artifacts, so a team compounds its own work instead of redoing it.
