Applying AI to support better
mining decisions
De-blackbox AI through geological insights

Stratum AI Geospatial Estimator (SAIGE) creates a more accurate resource model with the data you already have

The Stratum Resource Model

Explore an example of our AI resource model and examine its depth profiles.

The Impact of SAIGE on Model Performance

Drilling Efficiency & Resource Growth

Traditional Models
Static targets from averaged continuity
Low ounces per drill metre
Slow feedback into models
SAIGE - (DATA DRIVEN MODELS)
+32% ounces per drill metre
7.7 kt Cu identified in waste blocks
−16–24% drilling via confidence spacing
Observed in:
Gold intrusions
IOCG
Manto copper

Resource & Grade Modelling

Traditional Models
Manual domain smoothing
High block-scale uncertainty
Frequent waste misclassification
SAIGE - (DATA DRIVEN MODELS)
+43–56% prediction accuracy
Higher block-scale confidence
Up to −73% misclassified waste
Observed in:
Copper Porphyry
IOCG
Gold

Short-Term Planning & Reconciliation

Traditional Models
Static monthly planning assumptions
Plan vs actual production gaps
Frequent re-handling & re-planning
SAIGE - (DATA DRIVEN MODELS)
+2–6% higher mined grade
3–11% lower strip ratio
32–55% lower deviation in reconciliation
Observed in:
Copper porphyry operations

Processing, Recovery & Throughput

Traditional Models
Limited block-scale recovery prediction
Conservative mill routing
Under-utilized plant capacity
SAIGE - (DATA DRIVEN MODELS)
−47% recovery prediction error
+7–10 pp recovery uplift
+5–8% mill capacity unlocked
Observed in:
Sulfur-constrained systems
Epithermal gold