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We helped a major Western Australian resource firm eliminate millions in heavy machinery downtime—deploying an autonomous, AI-powered predictive maintenance suite for their entire remote vehicle fleet.

Autonomous Mining Operations
  • Predictive Maintenance
  • Computer Vision
  • Autonomous Scheduling

WA Resources Firm

Tier-1 Mineral Extraction Operator, launched 2025

Outcomes

  • Cut unpredictable heavy vehicle downtime by a massive 38%.
  • Generated immediate eight-figure cost savings within the first operating quarter.
  • Eliminated completely 40+ hours per week of manual fleet diagnostic audits.
  • Dramatically improved site safety records through autonomous dispatch warnings.

Challenge

A prominent Western Australian resource extraction firm was suffering from highly expensive, unpredictable breakdowns within their heavy machinery fleet. Operating in harsh, remote conditions meant that when a vehicle failed unexpectedly, the logistical cost of halting the supply chain and rushing spare parts was astronomical. Their maintenance schedule was entirely reactive, relying on manual inspections that frequently missed micro-stress indicators on industrial components.

Solution

We designed and deployed a comprehensive IoT-driven predictive maintenance neural network.

We built custom ingest pipelines that pulled continuous telemetry data (vibration, heat, acoustic signatures) directly from the fleet into a centralized cloud reservoir.

We deployed proprietary machine learning models that analyzed this data in real-time to identify the precise signatures of imminent component failure weeks before they occurred.

We wired these diagnostic dashboards into an automatic scheduling system that seamlessly routed equipment to maintenance bays during non-critical windows, achieving true zero-touch predictive operations.