Industry · Mining & Resources
AI for ore-body modelling, predictive maintenance, fleet optimisation and safety — built for the site and operations environments, not the demo room.
Mining and resources AI engagements operate under constraints that aren't on a slide deck: intermittent connectivity on-site, FIFO operating rosters, equipment that wasn't designed for telemetry, WHS obligations that don't bend, and ore-body realities that make every site differently complex. Our resources work accounts for these from day one. The Pilbara operators, the WA gold producers, the Queensland coal sector — different challenges, same engineering discipline.
Regulatory context
Every AI engagement we run in Mining & Resources produces documentation that explicitly maps the work to the obligations below. The risk register, the control framework, the board pack — they reference these by name, so internal audit and compliance teams can adopt the artefacts directly without translation.
Key challenges
AI systems on-site can't assume reliable connectivity to a cloud LLM. We design for the connectivity reality — edge inference, cloud sync, conflict resolution patterns, ops-friendly diagnostic interfaces.
The "operator" of a mining AI system is often a rotating roster of FIFO workers. Training and adoption need to work for crews that may not see the system for 4 weeks at a time. Documentation is short; the UI does the heavy lifting.
Anything that could materially affect site safety has WHS implications. AI systems that surface safety signals are valuable; AI systems that obscure or delay them create exposure. The architecture includes explicit safety-signal handling from the design stage.
AI engagements in the resources sector frequently touch environmental monitoring, water management, or native title-adjacent operations. The compliance posture for these is real and gets folded into the engagement scope upfront, not retrofitted.
Use cases
Sensor data + LLM-assisted failure-mode analysis. Measurable: unplanned downtime reduction, MTBF improvement, maintenance cost per operating hour.
LLM augmentation of geological models. Pattern recognition across drill core data, exploration history, comparable deposits. Augmenting, not replacing, the geologist.
Near-miss analysis, hazard identification, safety-bulletin generation. Designed to surface signals to safety teams, not to automate decisions.
Truck routing, dispatch optimisation, fuel + maintenance scheduling. Particularly impactful at large-scale operations where small percentage improvements compound.
Services most relevant here
From "we should do something with AI" to a prioritised, costed roadmap your team can actually deliver — with the people who would run it sitting in the workshop.
Explore practice
RAG, agents, evaluations and observability designed for the realities of running LLMs in production — cost, latency, accuracy and drift, all measured.
Explore practice
AWS, Vercel, Postgres, modern web platforms — designed for the load you actually have and the team that has to maintain it next year.
Explore practice
FAQ
Yes. Most discovery and design work happens at head office (typically Perth or Brisbane). Implementation and operator training is scheduled at site over defined windows that work with FIFO rotation schedules.
Yes — we design for it from the architecture stage when the use case requires. Edge inference + cloud sync, conflict resolution patterns, and ops-friendly diagnostic interfaces are part of the standard Design phase output for site-bound systems.
Both. Operator engagements are larger and more governance-heavy; contractor engagements are tighter-scoped and faster-moving. The methodology adapts to either.
Next step
We’ll come ready with questions specific to your industry and your regulator environment. 30 minutes, conversational, no commitment.