TARGETED OUTCOME
5-10% Reduced maintenance costs INDUSTRY · MINING
Rapidly compose AI-powered solutions across the mining value chain at scale.
XMPro is the agentic operations platform that lets mining organisations rapidly compose, govern and scale intelligent operations — from decision support through to autonomous AI agents — while maintaining trust, flexibility and deep alignment with how the business actually works.
*Targeted outcomes with indicative ranges · actuals vary by environment.
THE IMPACT
What changes when XMPro lands in mining.
THE OPERATING LOOP
Detect. Decide. Coordinate. Execute.
Most platforms stop at Detect. XMPro completes the loop.
Solution categories for mining.
The disciplines XMPro deploys across this industry — each chip filters the solutions library to use cases of that type.
Pre-scoped use cases for mining.
Each one names the operational problem, the signals the platform watches, and the governed action it drives. Filter by category or solution type — or search.
Mainline Conveyor Condition Monitoring
Section-by-section monitoring of mainline conveyors with predicted failure modes ranked by service criticality.
Surface Processing Plant Condition Monitoring
Plant-wide condition monitoring with predicted failure, synchronised maintenance and a digital twin tying it together.
CHPP Throughput Loss Monitoring
Decompose CHPP throughput loss into specific equipment, process and feed-quality causes — with ranked recovery actions.
Condition Monitoring
Bad actors first, hybrid engineering + ML models, asset-class templates — condition monitoring run as a programme.
Cyclone & Slurry Pump Condition Monitoring
Real-time condition monitoring for cyclones and slurry pumps across processing plants and utility networks.
Excavator Efficiency Optimisation
Idle, cycle, fuel and operator performance under one operational picture — with shift-actionable recommendations.
Loading & Hauling Optimisation
Real-time productivity, cycle-time analysis and predicted condition across every excavator and haul truck.
Multi-Site Iron-Ore Optimisation
National operating picture across every mine site, with process-stage drill-down and predictive intervention.
Haul Truck Predictive Maintenance
Engine, drivetrain, hydraulic and tyre telemetry fused into ranked predictions per truck — synchronised with the maintenance plan.
Mining Mobile Asset Predictive Maintenance
Mixed-fleet mobile asset prediction — engine, hydraulic and OEM telemetry fused into one condition view per asset class.
Predictive Maintenance
Three-step PdM: prioritise bad actors, predict with hybrid models, scale through reusable asset-class templates.
Open-Pit Iron Ore Process Optimisation
Integrated optimisation across drilling, hauling, crushing and milling — production losses caught in the seams between stages.
Mining Strategic Performance & Safety Oversight
Multi-site mining oversight tying production, safety and environmental KPIs into a single executive view.
Agent teams, AI agents and assistants for mining.
Pre-curated agents from the AI Agent Marketplace, scoped to mining — filter by type, search, or browse the full marketplace.
AvailableAgent Team
Agentic Automation Team for Liner Analysis and Scheduling For Crushers in The Mining Industry
Target Outcome
CRUSHER DOWNTIME
Significant Reduction
Predictive scheduling · liner wear rate management
AvailableAgent Team
Autonomous Agentic AI Team for Advanced Predictive Maintenance
Target Outcome
UNPLANNED ASSET FAILURES
Significant Reduction
Predictive intervention ahead of failure · 100% compliance review
AvailableAgent Team
Autonomous Supply Chain Optimization Team
Target Outcome
EXCEPTION RESPONSE TIME
Hours → Minutes
Routine exceptions resolved in minutes · structured escalation for high-impact
AvailableAI Agent
Agentic Anomaly Detection & Root Cause Analysis Agent
Target Outcome
ANOMALY DETECTION LEAD TIME
Hours—days early
Before production impact · automated causal reasoning
AvailableAI Agent
Agentic Compliance and Safety Officer Agent (Standards Guardian)
Target Outcome
POLICY COMPLIANCE RATE
99%
Continuous automated monitoring · 95% predicted incident prevention
AvailableAI Agent
Agentic Energy Management Agent (Efficiency Expert)
Target Outcome
ENERGY COST REDUCTION
Measurable reduction
Proactive load management · peak demand penalty avoidance
AvailableAI Agent
Agentic Equipment Monitoring Agent (Health Monitor)
Target Outcome
ANOMALY DETECTION LEAD TIME
70% earlier
vs threshold monitoring · 80% nuisance alarm reduction
AvailableAI Agent
Agentic Equipment Performance Agent (Availability Specialist)
Target Outcome
UNPLANNED DOWNTIME
Measurable reduction
Proactive condition-based interventions · extended asset life
AvailableAI Agent
Agentic Knowledge Synthesis & Decision Support Agent
Target Outcome
DECISION CYCLE TIME
Same-session
Synthesised multi-agent intelligence · OEE-aligned priorities
AvailableAI Agent
Agentic Maintenance Coordinator Agent (Predictive Maintenance Reliability Strategist)
Target Outcome
UNPLANNED DOWNTIME
Significant reduction
Emergency repairs cost 2—3x planned · condition-based prevention
AvailableAI Agent
Agentic Maintenance Schedule Planning Agent (Schedule Optimizer)
Target Outcome
OVERTIME COSTS
Significant reduction
Optimised scheduling · improved equipment availability
AvailableAI Agent
Agentic Predictive Analytics Specialist Agent
Target Outcome
UNPLANNED FAILURE RATE
Significant reduction
Days—weeks advance warning · risk-based intervention timing
AvailableAI Agent
Agentic Production Rate Agent (Performance Optimizer)
Target Outcome
PRODUCTION THROUGHPUT
Measurable increase
Bottleneck resolution · reduced WIP variability and overtime
AvailableAI Agent
Agentic Quality Control Agent (Quality Guardian)
Target Outcome
SCRAP & REWORK COSTS
Measurable reduction
Predictive defect detection · RCA cycle 4—48 hrs → automated
AvailableAI Agent
Agentic Root Cause Analysis Agent (Failure Investigator)
Target Outcome
INVESTIGATION CYCLE TIME
Significant reduction
Initial findings within hours · recurring failure rate reduced
AvailableAI Agent
Agentic Simulation & Scenario Analysis Agent
Target Outcome
IMPLEMENTATION FAILURE RATE
Significant reduction
Pre-implementation simulation · high-confidence decision support
AvailableAI Agent
Reporting and KPI Tracking Agent (Performance Analyst)
Target Outcome
REPORT PREP TIME
80%
Reduction in manual compilation · predictive visibility into trends
AvailableAI Agent
Equipment Manual Content Agent
Target Outcome
SEARCH TIME PER QUERY
>70%
Reduction vs 15—30 min manual search · instant via natural language
AvailableAI Agent
Executive Briefing Agent
Target Outcome
TIME TO INSIGHT
Days → Minutes
From manual data gathering · to continuous automated synthesis
AvailableAI Agent
Root Cause Report Generator Agent
Target Outcome
RCA REPORT TIME
4—8 hrs → Minutes
Draft generated in minutes · engineer reviews, not writes
AvailableAI Agent
Shift Handover Briefing Agent
Target Outcome
HANDOVER PREP TIME
20—45 min → 5 min
Auto-compiled draft · supervisor review only
AvailableAI Agent
Standard Operating Procedure (SOP) Creator Agent
Target Outcome
SOP CREATION TIME
2—4 Weeks → 30 min
Compliance-checked draft · 90% backlog reduction in year one
AvailableAI Agent
Supply Chain Demand Planner Agent
Target Outcome
FORECAST ERROR RATE
Below 15%
Down from 20—35% baseline · within 6—12 months
AvailableAI Agent
Supply Chain Financial Performance Agent
Target Outcome
COST VARIANCE DETECTION
Real Time
Down from days—weeks lag · automated threshold alerting
Not a concept. In production.
XMPro is deployed at Tier 1 global operators across asset-intensive and mission-critical industries — delivering measurable results across predictive maintenance, process optimisation and operational intelligence.
Now pushing the frontier.
MAGS agents are achieving what no other industrial platform has demonstrated — sustained autonomous operations at enterprise scale.
0+
Days Autonomous
Safety-critical petrochemical operations
3-0+
Agents Per Team
Specialized agents coordinating per use case
0+
Teams Deployable
Scale across sites and business units
0%
Governed
Every agent, every decision, every action — auditable
