How does predictive AI benefit mining companies in Australia?

Australia's mining sector — one of the world's largest and most technologically advanced — is at the forefront of AI adoption. PresciaIQ works with mining companies across the Pilbara, Hunter Valley, and Queensland coalfields to deploy predictive intelligence that improves safety, productivity, and cost efficiency. **Equipment Reliability and Predictive Maintenance** Mining equipment — haul trucks, draglines, conveyors, crushers, and processing plant — represents billions of dollars of capital investment. Unplanned failures are catastrophically expensive: a single haul truck breakdown can cost $100,000–$500,000 in lost production and emergency repair. PresciaIQ's predictive maintenance models integrate vibration, temperature, oil analysis, and operational data to forecast component failure probability by asset — typically providing 2–4 weeks of warning before failures occur. **Ore Grade and Recovery Prediction** Predicting ore grade variability ahead of mining enables processing plant optimisation — adjusting reagent dosing, grinding parameters, and flotation conditions to maximise recovery from variable feed. PresciaIQ's ore characterisation models analyse drill core data, geophysical surveys, and historical processing records to forecast grade and mineralogy by mining block. **Safety Risk Prediction** Mining safety is a non-negotiable priority. PresciaIQ's safety risk models analyse site conditions, equipment status, worker fatigue patterns, and environmental factors to predict elevated risk periods — enabling targeted safety interventions. Sites using predictive safety models typically reduce incident rates by 20–35%. **Operational Cost Optimisation** Fuel, explosives, and reagents are the largest variable costs in mining operations. PresciaIQ's cost optimisation models analyse operational patterns to identify opportunities to reduce consumption without impacting production — typically delivering 5–15% reduction in variable operating costs.