It’s like giving geologists a super-smart metal detector that has read every map, satellite image, and drilling record on Earth, and can point to the few places most worth digging next.
Traditional mineral exploration is slow, expensive, and hit-or-miss. This use of AI aims to quickly pinpoint high-potential mineral deposits from massive, complex geological and geospatial data, reducing wasted surveys and drilling.
Access to large, curated geological datasets and domain-labeled exploration outcomes, combined with SRI’s geoscience expertise and long-running government/industry relationships, can create proprietary training data and workflows that are hard to replicate.
Hybrid
Vector Search
High (Custom Models/Infra)
Availability and quality of labeled geological and drilling data across regions; integrating heterogeneous data types (geology maps, satellite imagery, geophysics, drill logs) at scale; and compute/storage demands for large-area inference.
Early Adopters
Positioned at the intersection of AI research and applied geoscience, likely using multi-modal data (maps, remote sensing, historic drill results) and custom models rather than generic off-the-shelf tools, which can give better localization accuracy and domain fit than standard GIS plus ML approaches.