From predictive maintenance to ore grade prediction, we help Australian mining companies implement AI that reduces downtime, improves safety, and maximises resource recovery.
Reduction in unplanned downtime
Fewer safety incidents
Improvement in ore recovery
Fleet efficiency improvement
Proven applications of AI and machine learning for mining operations that deliver measurable improvements in safety, efficiency, and resource recovery.
Machine learning models that analyse equipment sensor data to predict failures before they happen, reducing unplanned downtime and maintenance costs.
AI-powered monitoring systems that detect safety hazards, monitor worker locations, and provide early warning for geological risks and equipment failures.
Machine learning models that analyse geological data, drilling results, and sensor readings to predict ore grades and optimise extraction strategies.
AI-driven optimisation of haul truck routes, loading sequences, and equipment allocation to maximise throughput and reduce fuel consumption.
Automated visual inspection of equipment, conveyor belts, and processing facilities using drones and fixed cameras to identify wear and damage.
AI-enhanced geological modelling and mine planning that optimises extraction sequences and maximises resource recovery over the life of mine.
We connect AI capabilities to your existing mining systems, from equipment sensors and SCADA to fleet management and ERP platforms, without disrupting operations.
Secure connections between operational technology and AI systems
Integration with MineStar, FrontRunner, and other platforms
AI models deployed at the edge for real-time decision making
We help mining companies across Australia implement smart mine initiatives, from pilot projects to enterprise-wide digital transformation.
Talk to Our Mining TeamThree fast-track packages to kickstart your AI journey. Roll out AI workspaces, deploy your first agent, or build a AI Product - all in just 14 days.
AI analyses sensor data from mining equipment including vibration, temperature, pressure, and operational parameters to identify patterns that precede failures. This enables maintenance teams to address issues before breakdowns occur, significantly reducing unplanned downtime and extending equipment life. Our models integrate with existing SCADA and historian systems.
Yes, AI dramatically improves mine safety through multiple applications: real-time monitoring of worker locations and fatigue levels, detection of ground stability issues, identification of equipment proximity hazards, and automated safety system triggers. Computer vision can monitor for PPE compliance and unsafe behaviours across the site.
We build AI solutions that integrate with all major mining equipment platforms and sensor systems including Caterpillar MineStar, Komatsu FrontRunner, Hitachi EH series, and Sandvik OptiMine. Our solutions connect to existing SCADA, historians, and fleet management systems to provide unified AI insights.
AI models analyse drilling data, geological surveys, assay results, and real-time sensor data from processing to predict ore grades more accurately. This enables better blast planning, improved stockpile management, and optimised processing parameters. Miners typically see 10-20% improvement in grade control accuracy.
Mining AI projects typically follow a phased approach: 4-8 weeks for proof of concept with historical data, 3-6 months for pilot deployment on specific equipment or processes, and 6-12 months for full-scale rollout. We work closely with mining operations to minimise disruption and ensure safety throughout implementation.
Book a consultation to explore how AI can reduce downtime, improve safety, and maximise resource recovery in your mining operations.
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