Systematic intelligence for asymmetric mineral claim acquisition.
Three structured stages, from continental-scale reconnaissance to claim-level decision support. Each documented, versioned, and reproducible.
Multi-scale targeting workflow
Regional Screening
We screen geochemical surveys, airborne geophysical grids, and multispectral imagery across hundreds of thousands of square kilometers, identifying regions with structural, lithological, and geochemical signatures associated with precious metal and critical mineral systems. Regions showing convergent anomalism across multiple data types are advanced; single-dataset anomalies are deprioritized.
District-Level Analysis
Priority regions are evaluated at higher resolution. Geochemical anomalies are cross-referenced against structural data, lithological contacts, alteration zones, and known occurrences. The platform identifies claim-scale targets within the most prospective corridors, with priority on claims in strategic positions along productive structural trends and adjacent to known mineralization.
Prospect Validation
Individual claims are evaluated at sample level: geochemistry, structural controls, deposit analogs in comparable geological settings. Every evaluation ends with a clear recommendation. Acquisitions are justified; deprioritizations are documented with equal rigor.
Public data. Systematic analysis.
The information advantage isn't data exclusivity. It's systematic integration at a scale and rigor the industry hasn't applied.
- National geochemical surveys: USGS, GSC, BGS, Geoscience Australia
- Airborne geophysics: magnetics, radiometrics, electromagnetics
- Multispectral and hyperspectral remote sensing
- Regional geological maps and structural compilations
- Historical mineral occurrence records and drill-hole databases
Production infrastructure
Reproducible Workflows
Every analysis is versioned. Every output carries a full audit trail from raw data to recommendation, regenerable exactly months or years later.
Distributed Processing
Continental-scale geospatial datasets processed in parallel across distributed infrastructure.
Systematic QA/QC
Analytical artifacts, sampling biases, and coverage gaps are flagged and handled explicitly. Decisions are based on validated data, not raw public datasets used uncritically.