Cognic Systems

MINING AI • PREDICTIVE MAINTENANCE • HAUL FLEET TELEMATICS • ESG COMPLIANCE

AI & Automation for Mining & Metals Operations

Cognic engineers custom AI agents, heavy fleet predictive maintenance, ore quality optimization models, workplace safety computer vision, and industrial software to maximize equipment uptime, protect workers, and lower environmental footprint.

Haul Fleet Telematics
SCADA & PLC Streams
Pit Camera Feeds
Assay Lab Data
Safety Incidents
Cognic
AI & Automation
Zero Fatalities
Uptime & Asset Life
Higher Ore Recovery
Dispatch Velocity
Heavy Fleet Predictive MRO
Site Safety Vision AI
Ore Grade & Mill Optimization
Autonomous Haulage AI
ESG & Compliance Reporting
Custom Industrial Software
THE CHALLENGE

Mining & Smelting Operations Face Harsh Environments and Cost Pressures

Mining enterprises, smelters, and metal processing mills operate in remote, hostile environments where a single critical haul truck breakdown or ball mill failure can cost tens of thousands of dollars per hour in lost throughput. Legacy mines struggle with fragmented telemetry across mixed-brand vehicle fleets, paper-based safety inspection sheets, unpredictable ore grade variability, and stringent environmental sustainability regulations. Cognic delivers rugged, enterprise-grade AI software and automation to solve these core operational challenges.

01
Unplanned Heavy Machinery Downtime

Unscheduled hydraulic, engine, or transmission failures on haul trucks, excavators, and draglines stall extraction cycles and inflate repair budgets.

02
Hazardous Worker Safety & Collision Risks

Blind spots, driver fatigue, heavy vehicle interactions, and hazardous pit conditions present ongoing workforce safety risks in open pits and underground mines.

03
Ore Grade Variability & Processing Inefficiencies

Fluctuating ore feed hardness and mineralogy lead to suboptimal grinding in ball mills, wasted energy, and reduced metallurgical recovery.

04
Complex ESG & Environmental Audit Reporting

Tracking water usage, tailings dam stability, diesel emissions, and rehabilitation obligations across disparate mine sites requires tedious manual consolidation.

WHAT WE BUILD

Enterprise AI Solutions Built for Mining & Metals

We build industrial AI platforms, computer vision hazard detection, and predictive maintenance engines that withstand the scale and complexity of global mining and smelting operations.

Heavy Equipment Predictive Maintenance

AI models that analyze vibration, temperature, oil telematics, and pressure sensor streams from haul trucks, crushers, and conveyors to predict catastrophic component failures.

Fleet TelematicsVibration AnalysisAsset Life Extension
Explore Solution →

Mine Safety & Hazard Computer Vision

Real-time video analytics detecting PPE non-compliance, worker proximity to heavy machinery, pit berm erosion, and conveyor belt tears before accidents occur.

PPE ComplianceProximity DetectionConveyor Belt Monitoring
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Ore Processing & Mill Yield Optimization

Machine learning models predicting optimal SAG mill speeds, reagent dosing, and flotation parameters based on real-time mineralogy and ore hardness feeds.

SAG Mill ControlReagent OptimizationYield Maximization
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Mine Dispatch & Fleet Workflow Agents

Autonomous agents optimizing truck-and-shovel allocations dynamically to minimize queuing at shovels, reduce empty haul miles, and lower diesel consumption.

Truck-Shovel MatchingFuel EfficiencyQueue Minimization
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Environmental & ESG Compliance Document AI

Automate data extraction and audit verification from tailings inspection reports, emissions logs, water monitoring sensors, and regulatory environmental filings.

Tailings Dam AuditsEmissions TrackingRegulatory Compliance
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Custom SCADA & Industrial Mining Software

Custom operational telemetry dashboards, underground communication gateways, and edge-deployed software connecting SCADA, OSIsoft PI, and SAP PM.

OSIsoft PI ConnectorsEdge AI IngestSAP PM Integration
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MINING USE CASES

Where Mining Operations Deploy AI & Automation

Haul Truck Engine & Tire Health Monitoring

Predicting haul truck tire failure and diesel engine degradation by fusing temperature, road roughness, payload tonnage, and pressure telemetry.

Conveyor Belt Rip & Overheating Detection

Thermal imaging and computer vision detecting splice defects, roller friction hotspots, and longitudinal belt tears before fires or snapped belts occur.

Flotation Circuit Reagent Dosing AI

Computer vision froth analysis and assay telemetry models adjusting chemical reagent addition dynamically to increase copper/gold concentrate grade.

Fatigue & Distraction Monitoring for Operators

In-cab computer vision analyzing eye blinks, gaze direction, and yawning patterns to alert drowsy heavy equipment drivers and prevent haul road collisions.

Blast Fragmentation Assessment from Drone Imagery

Automated 3D photogrammetry and image segmentation sizing rock fragments after blasts to optimize secondary crushing parameters.

Shift Handover & Safety Pre-Start Digitalization

Voice and mobile workflows enabling operators to log vehicle pre-start checklists and shift handover notes with automated maintenance defect extraction.

Smelter Furnace Energy Consumption Management

Optimizing electric arc furnace and potline electrical draw during peak industrial tariff windows without reducing metal tapping volume.

Tailings Storage Facility (TSF) Risk Modeling

Continuous ingestion of piezometer, InSAR satellite displacement, and weather data to provide early warning alerts for tailings dam embankment movement.

CONNECTED TECHNOLOGY

Enterprise Mining AI, Connected to Your Systems

Equipment Telematics, Sensors & SCADA
↓
Ruggedized Industrial Edge Gateway
↓
Cognic Industrial AI & Telematics Engine
↓
Safety & Equipment Operating Limit Engine
↓
Core Historian & ERP (OSIsoft PI, SAP, AspenTech)
↓
Integrated Operations Center (IOC) Dashboards
Discuss Integration →
  • OSIsoft PI Historian / AVEVA PI
  • SCADA & PLC Controllers (Rockwell, Siemens)
  • Fleet Management Systems (Modular, Wenco, Caterpillar MineStar)
  • Enterprise Asset Management (SAP PM, IBM Maximo)
  • Industrial IoT Protocols (Modbus, OPC-UA, MQTT)
  • Mine Planning Software (Vulcan, Datamine, Deswik)
  • InSAR Satellite Geotechnical Data Feeds
  • Assay Laboratory Information Management (LIMS)
  • Drone Photogrammetry & LiDAR Platforms
  • Underground Leaky Feeder & Wi-Fi Networks
SECURITY & COMPLIANCE

Rugged Industrial Security & Operational Resilience

OT / IT Network Segmentation

Strict adherence to Purdue Model architecture with DMZ firewalls isolating plant operational control from business analytics.

Edge-First Offline Operation

Critical hazard and predictive safety algorithms run directly on local edge hardware, functioning reliably even during satellite uplink loss.

High-Volume Historian Ingestion

Engineered to ingest thousands of high-frequency telemetry tags per second without dropped packets or historian lag.

Tamper-Proof Safety Logs

Immutable audit records for all equipment inspection sign-offs, safety override triggers, and maintenance releases.

Industrial Cybersecurity

IEC 62443 aligned protocols ensuring unauthorized firmware updates and rogue commands cannot reach physical mine controllers.

Maintenance Engineer Sign-Off

Automated work order suggestions require review and dispatch confirmation by certified mine maintenance planners.

RESPONSIBLE AI

Human-in-the-Loop AI for Mining

In harsh mining operations, physical equipment safety and human lives are paramount. Our AI models detect micro-trends and recommend operational adjustments, while mine superintendents and reliability engineers review and execute critical decisions.

High-Frequency Sensor Ingestion
AI Anomaly Detection & Remaining Useful Life
Operating Limit & Safety Rule Check
Reliability Engineer Review
Automated SAP PM Work Order Release
Shift Handover & Reliability Audit Trail
MINING AI IN PRACTICE

Mining Automation in Practice

68%
Fewer Catastrophic Failures
18 Days
Average Warning Window
Unplanned Downtime Hours per Truck

Illustrative operational metrics — representative outcomes.

Predictive Maintenance for Open-Pit Haul Fleet

Problem: An open-pit iron ore operation experienced frequent catastrophic differential and transmission failures across its 65-truck haulage fleet, leading to 240+ hours of annual unplanned downtime per vehicle and elevated maintenance costs. Solution: Cognic deployed an edge-and-cloud predictive telematics pipeline that streams CAN-bus and oil temperature/vibration sensors, calculating anomaly scores and predicting component breakdown 14 to 21 days in advance. Technology:
Edge IoTPredictive Failure MLOPC-UA / CAN-busSAP PM Integration
Operational Standards:
IEC 62443 AlignedRole-Based AccessAutomated Maintenance Logs
Result: Reduced catastrophic haul truck powertrain breakdowns by 68%, averted 3 major AOG-scale engine seizures, and shifted 85% of repairs to scheduled planned maintenance windows. View Case Studies →
WHY COGNIC

Why Mining Leaders Choose Cognic

We combine AI engineering with robust software development to automate mining workflows reliably.

01
Industrial Edge Engineering

We build systems that operate reliably in dust, vibration, and remote sites with intermittent satellite connectivity.

02
OT & Historian Native

Our engineers speak Modbus, OPC-UA, CAN-bus, and OSIsoft PI, bridging the gap between field PLC hardware and modern software.

03
Measurable Equipment ROI

We focus on concrete operational metrics: reducing component wear, extending tire life, avoiding unplanned shutdowns, and improving safety.

04
Enterprise Safety Commitment

Our AI is built around zero-harm safety principles, designed to reinforce established safety protocols without creating operational hazards.

MINING AI FAQ

Mining AI & Automation FAQs

How does Cognic handle edge deployment at remote mine sites with poor connectivity?
We deploy localized, industrial-grade edge computing nodes directly on site or within mobile equipment cabs. Critical computer vision models, anomaly detection algorithms, and safety alerts run locally in real time without needing an active internet connection. Non-urgent telemetry is compressed and queued for transmission when high-bandwidth uplinks become available.
Can this software integrate with our existing OSIsoft PI system and SAP PM?
Yes. We build bi-directional connectors for OSIsoft PI (AVEVA PI), AspenTech, Honeywell, and SAP Plant Maintenance (SAP PM). When our predictive models detect an imminent failure, the system automatically drafts a work order with the recommended parts and labor hours directly into SAP PM for planner review.
What sensor inputs are required for haul truck predictive maintenance?
We typically ingest CAN-bus vehicle telemetry (engine oil pressure, coolant temperature, transmission slip, exhaust gas temperature), supplemented where needed by third-party tri-axial vibration sensors and acoustic telemetry on rotating assemblies.
How does computer vision improve conveyor belt safety and uptime?
High-speed industrial cameras and thermal sensors monitor the conveyor surface, edges, and idlers. Our computer vision models detect belt rips, mistracking, hot idlers, and foreign material in real time, triggering automated horn alerts or interlocked emergency stops before catastrophic belt failure occurs.
Can AI help optimize mineral processing and grinding mills?
Yes. By fusing ore feed characteristics from blast models with SAG/ball mill power draw, acoustic sensors, and cyclone feed density, our optimization models recommend dynamic speed and water additions that maintain optimal grinding efficiency while reducing kilowatt-hour consumption.
How are operator fatigue detection and cabin privacy managed?
In-cab cameras process visual streams entirely in memory at the edge to detect closed eyelids, head nods, and micro-sleeps. Video frames are not recorded or streamed continuously unless a safety violation threshold is breached, respecting operator privacy while preventing serious incidents.
Does Cognic support environmental ESG monitoring for tailings facilities?
Yes. We integrate geotechnical instrumentation (piezometers, inclinometers), satellite InSAR displacement feeds, and local rainfall gauges into automated risk dashboards that monitor embankment stability and generate compliant environmental filings.
What is the implementation timeline for a mine site pilot?
A single-circuit or fleet-level predictive maintenance pilot is usually deployed within 6 to 8 weeks, including sensor data ingest and baseline model calibration. Site-wide enterprise rollouts typically span 3 to 6 months.
AUTOMATE MINING WORKFLOWS

Ready to Modernize Your Mining & Metals Operations?

Discuss your mobile fleet telematics, mill optimization, site safety, or asset management challenges with our industrial software engineers. We will evaluate how edge AI and predictive automation can maximize your site uptime.