Transforming soil from a static assumption into a manageable risk layer.
Modern civilization treats soil as a static and sacrosanct substrate, something to be certified once and trusted indefinitely. In reality, soil is a dynamic system whose properties evolve with stress, time, and environment. One of its most consequential failure modes is soil liquefaction.
Today, liquefaction risk is evaluated on a site-by-site basis using static snapshots, conservative assumptions, and fragmented heuristics developed by the geotechnical community over decades. These approaches work locally, but they do not scale. They are not continuous, not adaptive, and not globally coherent.
Every road, bridge, port, data center, factory, and city rests on soil. By virtue of this, soil is the physical backbone of the global economy. Yet it remains the least instrumented, least modeled, and least understood component of modern infrastructure systems.
This creates a structural paradox. For the first time in human history, despite the Lindy effect favoring accumulated knowledge, humanity is constructing infrastructure at planetary scale without a coherent and data-driven understanding of the ground beneath it.
Our mission is to transform soil liquefaction from a static, episodic assessment into a dynamic, data-driven, and continuously improvable modeling problem. We integrate geotechnical domain knowledge, empirical observations, and modern machine intelligence to build models that learn across sites, regions, and time, while remaining interpretable, transparent, and grounded in physical reality.
We encode it, scale it, and make it computable.
By doing so, we turn soil from an implicit assumption into an explicit, measurable, and manageable risk layer, one that infrastructure systems can reason about with the same rigor applied to materials, loads, and design.
Because infrastructure is only as resilient as the ground it stands on.