AI and automation that makes Perth's toughest industries safer, faster and smarter

Unplanned equipment failure is the most expensive event in mining operations. When a haul truck engine fails mid-shift in the Pilbara, the direct cost of the repair is compounded by lost production, logistics complications and potential cascade effects on downstream processing. For offshore oil and gas platforms, the consequences of unexpected equipment failure can be even more severe — safety risks, environmental hazards and production shutdowns that cost millions per day.

Digital Nachos builds predictive maintenance models that analyse equipment sensor data to forecast failures before they occur. Our models ingest vibration, temperature, pressure and electrical data from equipment telematics systems and process historians, identifying the subtle patterns that precede specific failure modes. A bearing that will fail in two weeks exhibits different vibration characteristics than one with six months of remaining life — and our models learn to distinguish between them.

We train models on historical failure data specific to each client's fleet and operating conditions. A haul truck operating in the Pilbara's 45-degree heat experiences different stress patterns than one operating in a temperate climate, and a generic predictive model will produce poor results. Our approach uses transfer learning to accelerate model training while ensuring the predictions reflect the actual conditions at each Perth client's operations.

The output is actionable — maintenance planners receive prioritised work orders with estimated time-to-failure, recommended actions and confidence levels. This allows maintenance to be scheduled during planned shutdowns rather than reacting to breakdowns, improving equipment availability and reducing maintenance costs.

AI-powered safety systems are transforming how Perth resources companies protect their workforces. Computer vision, in particular, offers the ability to monitor compliance with safety rules continuously and consistently — something that human supervisors cannot do across a sprawling mine site operating around the clock.

We build computer vision systems that monitor CCTV and equipment camera feeds for safety compliance. Applications include detecting workers without required PPE in designated zones, identifying unauthorised personnel in restricted areas, monitoring vehicle separation distances, detecting fatigue-related behaviours in vehicle operators and tracking exclusion zone compliance around operating equipment.

These systems do not replace human safety oversight — they augment it. When the system detects a potential safety breach, it generates a real-time alert to the site safety team, captures a timestamped image for investigation and logs the event in the safety management system. Over time, the data generated by these systems reveals patterns — specific locations with frequent PPE non-compliance, times of day when fatigue incidents increase, equipment types that workers routinely approach too closely.

For Perth oil and gas companies, we also build AI-powered process safety systems that monitor operational parameters and detect conditions that may lead to process safety events. These systems analyse hundreds of process variables simultaneously, identifying combinations of conditions that human operators would not recognise until an alarm activates.

Perth has a growing AI and machine learning ecosystem. UWA's Centre for Transforming Maintenance through Data Science, Curtin University's AI and machine learning research programmes, and the presence of the CSIRO's Data61 in Western Australia provide a strong foundation of research talent and industry collaboration.

Digital Nachos draws on this ecosystem while maintaining a pragmatic approach to AI implementation. We are not a research lab — we build AI systems that solve specific operational problems for Perth companies and deliver measurable returns. Every project begins with a clear definition of the business problem, the available data, the success criteria and the integration requirements.

Our approach favours proven techniques over cutting-edge experimentation. Gradient boosting models, random forests and well-architected neural networks solve the vast majority of industrial AI problems. When a simpler model delivers adequate performance, we recommend it over a more complex alternative — simpler models are easier to maintain, explain and trust in safety-critical environments.

We also recognise that AI is only as good as the data it trains on. A significant portion of our engagement with Perth clients involves data preparation — cleaning historical records, establishing reliable data pipelines from source systems and implementing the data quality processes that ensure models remain accurate over time. This is unglamorous work, but it is the foundation on which every successful AI deployment is built.