Key Takeaways

  • Production prediction reporting is consolidating into web-based platforms: Datamine Syncromine Core and Deswik.OPS.
  • Datamine acquired Mineware/Syncromine in 2026, signaling active consolidation in this specific niche of production scheduling and reporting.
  • Marked “Emerging” — these platforms consolidate reporting well, but the predictive (vs. purely descriptive/reporting) capability is still developing across the category.
  • If your current mining predictions report is a manually-assembled spreadsheet, moving to one of these platforms is the realistic first step before layering AI forecasting on top.

TL;DR

Consolidate daily/weekly/monthly mining production predictions into a web-based platform like Datamine Syncromine Core or Deswik.OPS rather than manually assembled spreadsheets — the reporting infrastructure is mature even though true predictive AI on top of it is still emerging.

How Do I Predict Mining Production With AI?

A mining predictions report — the recurring document that tells the mine planning team what production to expect from current operations — has historically been assembled from multiple disconnected sources: grade control data, drilling and blasting weekly plans, short-term scheduling outputs, and whatever the site superintendent knows anecdotally about equipment availability. That fragmentation is the actual problem to solve before AI forecasting is worth adding.

Datamine’s Syncromine Core (following Datamine’s 2026 acquisition of Mineware/Syncromine) and Deswik.OPS both address this by consolidating production scheduling and reporting into a single web-based platform, pulling from the same underlying data sources your block model and short-term planning tools already use rather than requiring a separate manual reconciliation step. That consolidation itself is valuable even before you add predictive modelling — it means your “predictions report” is generated from live operational data rather than assembled by hand from five different exports.

The actual predictive layer — forecasting near-term production based on current operational state rather than just reporting the plan — is where this category is still maturing. Some operations layer their own time-series or ML forecasting on top of the consolidated data these platforms expose; that’s a build decision more than a buy decision right now, which is part of why this is flagged “Emerging” rather than a fully mature off-the-shelf capability.

Try It With Geocluster

If you’re deciding whether to build a custom forecasting layer on top of your consolidated production data or wait for vendors to ship one, that’s a research question worth answering systematically. The Geocluster Research Harness helps teams evaluate AI tooling decisions like this for their own mine planning stack.