Know where
gold is
before you drill.
Atlas uses machine learning to predict the precise land squares most likely to contain gold — with a probability, an explanation, and a certainty score no human geologist can match.
Billions spent.
Almost nothing found.
The mining industry spent $12.5 billion on exploration in 2024. The average cost to discover a single new deposit has tripled to $218M. Companies spend a decade securing permits and millions on baselines — only to drill and find nothing.
Modern exploration generates terabytes of data per site — satellite imagery, geochemical assays, magnetic surveys, drill logs. But that data sits in silos while decisions are made by intuition. AI-driven targeting has pushed drill success rates from 0.5% to approximately 75% in early deployments.
timeline
timeline
timeline
sitting unused
A neural network
trained to find gold.
Our model ingests every available signal — geophysics, geochemistry, remote sensing, structural geology, drill logs — and fuses them into a unified geological intelligence layer. It trains from as few as 3,000 labeled examples and works even when 20% of data layers are missing.
Six outputs per analysis.
Caltech & IPP
trained. Production proven.
Core ML team trained at Caltech and Institut Polytechnique de Paris — with expertise across machine learning, computational geoscience, and applied mathematics. Our AI systems have been deployed to industrial scale for major clients across healthcare, infrastructure, and resource extraction.
Probability map in one month.
We just need to
show you where.
Stop spending $218M per discovery. Get a probability map of your entire land package in under a month.