Mining
AI that moves ore faster and keeps your people safe
You get autonomous haulage optimisation, geological modelling agents, predictive maintenance for heavy equipment, safety monitoring, ore grade control, and environmental compliance workflows — all running on your cloud with full audit trails.
Built on your AWS, Azure or GCP tenant. Mining-safety compliant. Regulator-ready.
Proof of concept
To measurable ROI
AWS, Azure or GCP
In mining, the gap between data and decision costs lives and revenue
Unplanned equipment failures on a haul fleet cost $2 million per incident before anyone picks up a wrench. Safety incidents underground or on the pit bench create regulatory exposure that persists for years. Ore misclassification sends low-grade material to processing and high-grade material to waste dumps.
Your fleet telemetry, geological models, and sensor networks already contain the signals. We build AI agents that connect those systems, predict equipment failures weeks ahead, optimise ore recovery in real time, and generate the safety and environmental documentation your regulators require.
of mining fatalities linked to equipment and vehicles — McKinsey Global Mining Safety Report 2023
average cost of a single unplanned haul truck failure including lost production — BCG Mining Operations Analysis
ore recovery improvement achievable through AI-driven grade control — McKinsey Mining & Metals Practice
additional annual operating hours per autonomous haul truck vs manned — Rio Tinto Pilbara operations data
From pit sensor to compliance record — in one workflow
We connect your fleet management, geological modelling, SCADA, and safety systems to AI agents that predict equipment failures, optimise ore grades, monitor safety compliance, and generate the documentation your HSE and environmental teams require — all on your cloud
Fleet management, geological models, sensor arrays
- Approval gates
- Human-in-loop
- Audit logging
AWS, Azure or GCP
- Maintenance schedules
- Safety records
- Environmental reports
What we deliver for mining operators
Agents built for high-consequence, resource-critical mining operations
Predictive maintenance for heavy equipment
Predict heavy equipment failures 3-6 weeks ahead.
ROI
Process
Ingest fleet telemetry
Vibration, oil analysis, temperature, and load data from haul trucks, excavators, crushers, and conveyors
Predict failure windows
ML models forecast component degradation 3–6 weeks ahead with confidence scoring per asset
Schedule maintenance
Prioritised work orders aligned to production schedules with parts requirements and risk justification
Sources
Originally published: BHP Operational Review — AI & Automation; AWS Case Study — Caterpillar Mining — reproduced for illustrative purposes
Safety monitoring and incident prevention
Monitor pit conditions and PPE compliance in real time.
ROI
Process
Monitor site conditions
Pit cameras, vehicle proximity sensors, and wearable data processed in real time
Detect hazards
AI identifies PPE violations, unsafe proximity events, slope instability indicators, and fatigue patterns
Alert and document
Real-time alerts to supervisors and shift managers with timestamped records for HSE reporting
Sources
Originally published: Anglo American — FutureSmart Mining Safety Results; Azure Case Study — BHP Safety — reproduced for illustrative purposes
Ore grade optimisation
Optimise ore classification, blending, and processing.
ROI
Process
Integrate geological data
Block models, blast-hole assays, grade-control drilling, and real-time conveyor sensor data
Optimise classification
AI adjusts ore-waste boundaries and blending ratios to maximise recovery and minimise dilution
Control processing
Real-time feed-grade predictions for processing plant parameter adjustment
Sources
Originally published: Vale — Innovation & Technology; Anglo American — FutureSmart Mining — reproduced for illustrative purposes
Autonomous haulage optimisation
Maximise material movement per shift with optimised routing.
ROI
Process
Connect fleet systems
GPS, dispatch, fuel, and payload data from autonomous and manned haul trucks
Optimise routing
AI calculates optimal routes, speeds, and queue management to maximise tonnes per hour
Coordinate fleet
Real-time dispatch adjustments based on shovel availability, dump capacity, and road conditions
Sources
Originally published: Rio Tinto — Mine of the Future Programme; BHP Autonomous Haulage — reproduced for illustrative purposes
Environmental compliance monitoring
Track dust, discharge, and tailings against permit conditions.
ROI
Process
Monitor environmental data
Dust sensors, water quality monitors, tailings dam instrumentation, and satellite imagery
Detect threshold breaches
AI flags variances before they become reportable incidents, with trend analysis and forecasting
Generate compliance records
Automated environmental reports structured for mining regulator submission and audit
Sources
Originally published: Vale Environmental Compliance Programme; GCP Case Study — Anglo American — reproduced for illustrative purposes
Geological modelling AI
Enhance resource estimation and update block models continuously.
ROI
Process
Ingest geological data
Drilling logs, assay results, geophysical surveys, and historical production data
Enhance resource models
ML identifies geological patterns and structures that improve block model accuracy
Update continuously
Models refine as new drilling and production data becomes available, reducing estimation uncertainty
Sources
Originally published: BHP Geological AI Programme; Rio Tinto — Exploration Technology — reproduced for illustrative purposes
Audit and regulatory documentation
Answer mining regulator inquiries in hours with full trails.
ROI
Process
Capture decision data
Every AI-assisted operational and safety decision logged with data inputs and model outputs
Structure for audit
Records organised by regulation, asset, and time period for rapid retrieval during inquiries
Enable rapid response
Compliance team can answer mining regulator inquiries in hours with complete traceability
Sources
Originally published: Anglo American — Compliance & Governance; AWS Case Study — BHP Regulatory — reproduced for illustrative purposes
CEO, Rio Tinto
Jakob Stausholm
"Our autonomous haulage system in the Pilbara has now moved over 3.8 billion tonnes of material. Autonomous trucks operate 700 more hours per year than manned equivalents, with a 15 percent reduction in unit costs. This is not a technology experiment — it is a production system that has been running at scale for over a decade"
Originally published: Rio Tinto — Mine of the Future Programme — reproduced for illustrative purposes
How we work in mining environments
We do not deploy AI into mining operations without first understanding what a failure means — in production terms, in safety terms, and in regulatory terms. We map your use case to your mining licence conditions and HSE obligations before we write code.
Every agent runs on your cloud tenant. Your fleet telemetry, your geological data, your production records — none of it leaves your environment. Your safety officers, your mining inspectors, and your environmental regulators can inspect the system directly.
01
Operational risk mapping
We identify your use case, assess its safety, environmental, and regulatory implications, and define the governance framework before any build begins.
02
30-day proof of concept
A working AI agent on your cloud, connected to your fleet management and geological systems, with demonstrable output in 30 days.
03
Governance by design
Human-in-the-loop controls, audit trails, and explainable outputs — built so your mining safety board and environmental regulators can sign off.
04
Scale in 12–18 weeks
From one validated workflow to measurable operational ROI — with documentation your compliance team can present to mining authorities.
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Frequently Asked Questions
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