Key Takeaways

  • GeoMet reporting — turning geometallurgical sample results into predictive performance reports — has a real dedicated platform: Cancha.
  • Cancha integrates sample selection, prediction modelling, and automated reporting into one system, rather than leaving geologists to stitch spreadsheets together.
  • This space is still “Emerging” — Cancha is the clearest dedicated product, but the category isn’t yet as saturated with competitors as mainstream mine-planning software.
  • The AI value is in the prediction modelling step: forecasting recovery, hardness, and throughput from geomet sample data rather than manually cross-referencing lookup tables.

TL;DR

Automate GeoMet reporting with Cancha, a dedicated geometallurgy platform that runs sample selection, predictive modelling, and reporting in one integrated workflow instead of spreadsheets.

How Do I Automate GeoMet Reporting With AI?

Traditional GeoMet reporting is a manual bottleneck: a geologist pulls sample results (hardness, clay content, recovery tests), cross-references them against lookup tables or regression fits built in Excel, and writes up a report for the mine-planning team. That process doesn’t scale well as sample volumes grow, and it’s slow to update when new data comes in.

Cancha replaces that pipeline with a purpose-built application. You feed it your geometallurgical sample results — comminution indices, recovery tests, mineralogy — and it handles sample selection (which samples are representative enough to model from), builds prediction models that map sample-scale results onto your block model, and generates the reporting output your mining and metallurgy teams actually consume. Because it’s built specifically for this workflow rather than being a general BI tool bolted onto mining data, the prediction step benefits from domain-specific model choices (e.g. appropriate regression/classification techniques for recovery and hardness variables) rather than a generic linear fit.

Worth being honest about maturity here: this is still an emerging category. Cancha is the clear leading dedicated tool we found, but you won’t find the dozen competing SaaS platforms you’d see in, say, mine scheduling software. If you’re evaluating it, expect to validate its predictions against your own historical reconciliation data before trusting it as your sole reporting pipeline — see our companion post on geometallurgical reconciliation for how mines are already doing that recalibration step.

Try It With Geocluster

Evaluating whether a platform like Cancha’s prediction models actually fit your deposit — versus building something custom — is a research question worth doing rigorously. The Geocluster Research Harness helps you run that kind of comparative AI-tooling research systematically instead of ad hoc.