Natural Resources

GeonatIQ applies AI across the natural resources value chain, from early-stage exploration through to development, environmental monitoring, and investment analysis.

In mining and minerals, we have built AI approaches that integrate geochemical, geological, geophysical, and spatial data to predict the distribution of subsurface resources in high-dimensional space. This work has covered lithium, rare earth elements, base metals, and, more recently, precious metals, where our methods have been tested in the field in South America in collaboration with academic and industry partners. These projects focus on improving discovery probability, target ranking, and capital efficiency rather than replacing domain expertise.

Underpinning this work, we have created a large international database combining public and private data sources, including geochemical surveys, geological maps, remote sensing products, historical exploration data, and proprietary datasets. This allows models to be trained and validated across multiple geological settings rather than tuned to a single basin or district.

Beyond mining, we have developed AI for groundwater and aquifer assessment, subsurface storage screening, and resource-linked environmental monitoring, including impacts on lakes and water bodies. We also support natural resource investors and operators with AI-driven data integration, screening, and decision-support systems, turning fragmented technical data into actionable insight across exploration, development, and portfolio strategy.