MINML is an AI-native geoscience company using deep learning to de-risk mineral exploration and optimise target selection.
Traditional prospectivity mapping relies on manual weighting of geological criteria. MINML instead uses deep learning to discover complex, nonlinear relationships between multi-modal exploration data and mineralisation that are difficult to capture with manual analysis alone.
Our models are trained on known deposits and their geological context. They learn what mineralisation looks like in your data, then apply that knowledge across your project area to generate high-resolution prospectivity maps, ranked targets and quantified uncertainty estimates. The result is objective, testable predictions that help exploration teams allocate drilling budgets with greater confidence.
What we deliver
• High-probability target maps for subsurface resource systems (national to tenement scale)
• Integrated modelling across multi-scale, multi-modal exploration datasets
• Ranked target portfolios with performance metrics (e.g. ROC AUC, top-k capture, efficiency vs random)
• Decision-ready outputs that plug directly into existing GIS and technical workflows
Who we work with
• Exploration and production companies focusing drilling capital
• Junior and mid-tier explorers prioritising ground and licences
• Strategic investors and partners evaluating exploration portfolios
