What we automate
We build AI for mining systems that integrate with the operational reality of Australian mining businesses. Common workflows we automate end-to-end:
- Fleet telemetry ingestion, analysis, and exception alerting
- Predictive maintenance scheduling for haul trucks, excavators, and processing plant
- Ore quality and grade monitoring with real-time blending decisions
- Safety incident reporting, root-cause grouping, and trend analysis
- Contractor compliance and induction tracking
- Environmental and water reporting automation
- Daily operations dashboards and shift-handover summaries
Built for the realities of mining
Patchy site connectivity demands offline-first systems that sync when uplink is available.
Harsh conditions and ruggedised environments require interfaces that work on gloved hands and through dust.
High-stakes safety implications mean every automated decision needs an audit trail and human escalation path.
Regulator-facing reporting (NOPSEMA, state mining departments) needs to be exportable and defensible.
Why work with us on AI for mining
We have worked with Australian mining operations on AI for mining workflows that span exploration, operations, and rehabilitation. Brisbane-headquartered with deep familiarity with Queensland and Western Australian mining context.
Where most AI in mining goes wrong
Most mining AI engagements get scoped against a single sensor stream or a single operational system and miss the cross-system synthesis that actually moves operational decisions at site. The fragmented patterns: AI deployed against the SCADA stream without integration to maintenance management or asset performance management; predictive maintenance models that don't surface in the shift-handover process; "AI for safety incidents" that doesn't pull from the JHA and incident reporting systems together; drone and remote-sensor analysis that produces inference but never reaches the right operational dashboard; AI deployed in head office that bears no relationship to what's happening at site. Mining has hard physical operational realities, intermittent connectivity from remote sites, and the most consequential safety case of any industry we work in.
Systems we integrate against
Mining AI engagements typically integrate against Pronto, SAP for mining, JD Edwards, Ellipse, Maximo, Pulse, Aveva PI / OSIsoft PI for the historian layer, Hexagon mining suite, Modular Mining Dispatch, Wenco fleet management, Cat MineStar, ABB Ability mining solutions, and the various GIS/geological packages (Vulcan, Surpac, Datamine, Leapfrog). On-site SCADA, OT systems, and edge sensor networks add another layer that AI workflows must respect without becoming a vulnerability path back into the OT environment. We design integration patterns that respect the IT-OT boundary and the safety-instrumented system isolation.
If your stack isn't listed above, reach out anyway. The systems vary by industry but the integration patterns don't. We can usually work with whatever you're running. Tell us your stack.
Regulatory and compliance landscape
AU mining operations sit under a stack of regulation: state-based work health and safety legislation (with mining-specific obligations through state mines departments), environmental approvals administered by state EPAs and federal DCCEEW, and SOCI Act obligations where mining operations qualify as critical infrastructure (most large operations do). Indigenous land use agreements add cultural heritage obligations. The Australian Privacy Act governs workforce data. APRA CPS 234 applies if the mining operator has financial services subsidiaries. We design AI architecture against this stack with explicit attention to the SOCI Act reporting obligations and the operational safety case requirements.
Operational outcomes we move
Defensible outcomes we have moved on mining engagements: shift-handover preparation time reduced; predictive maintenance early-warning rate improved on rotating equipment; field-report-to-decision cycle time compressed through automated summarisation; environmental monitoring exception detection and routing; safety observation triage volume handled per supervisor; ore-grade reconciliation evidence preparation time reduced. Mining operators measure value in tonnes moved, downtime avoided, and safety incidents prevented. AI engagements that don't connect to those metrics tend to be the ones that get quietly shelved.
Common deployment patterns
Common deployment patterns for AU mining: edge inference at site with periodic synchronisation back to head office, designed for intermittent connectivity; document-Q&A across geological reports, operational guidelines, and internal SOPs; predictive maintenance agents that surface in CMMS workflow rather than as a separate dashboard; field-report agents that turn voice or text shift logs into structured operational data; environmental compliance evidence aggregation; SOCI Act-aligned deployment that respects the critical infrastructure obligations on cyber resilience and reporting.
Related Bedstone services
Mining operators typically pair this work with on-site, edge, and hybrid infrastructure, custom operational tools, and predictive maintenance and anomaly-detection agents. Or look at Bedstone OS for an internal AI workspace connecting your ERP, asset management, and field reports.
How we engage
Five-step delivery, scoped to fit. Audit, scope sprint, proof of concept, verification, rollout. Wrapped as a fixed-scope sprint, monthly retainer, fractional engagement, or one-off audit. See services for the full process and commercial structures.
Common questions about AI for mining
How is AI for mining different from generic enterprise AI?
Mining systems operate under safety-critical and environmentally regulated conditions with patchy connectivity. AI for mining has to be offline-first, fully auditable, and integrated with site-specific OT systems (Modular DISPATCH, Wenco, etc.). We build to those constraints rather than retrofitting generic AI tools.
Do you work with contractors and OEMs as well as mine owners?
Yes. We have engaged with mine owners directly, with EPCM contractors, and with OEM service providers. The right entry point depends on whose budget the project sits under and who owns the workflow being improved.
Can you integrate with our existing fleet management and ore-control systems?
Yes. We build against APIs, file drops, or OPC-UA where the systems support it. For older systems we use middleware or scheduled ETL. Integration risk is part of every audit before we scope the build.
Is mining AI eligible for the R&D Tax Incentive?
Most agentic AI and novel integration work on mining operations qualifies. We document sprints so your tax specialist can file with confidence.
Across Australia
We work with mining and resources operators across the country. See city-specific context: AI agency Australia, Brisbane, Sydney, Melbourne, Perth.