Real Time Asset Intelligence
Continuous intelligence across fleets, vehicles, energy assets and operational infrastructure, using telemetry, IoT data, anomaly detection and predictive signals to detect failures, inefficiencies and risks before they impact operations.
What is Real Time Asset Intelligence?
Real time asset intelligence uses continuous telemetry, IoT sensor data and AI anomaly detection to monitor the health, location and performance of operational assets, vehicles, equipment, chargers, industrial machinery, and predict failures before they occur. It moves fleet and infrastructure management from reactive maintenance to predictive operations, reducing unplanned downtime and extending asset life.
Assets fail reactively. Intelligence should be predictive.
Organisations managing large fleets, energy infrastructure or industrial assets typically discover problems after they happen, through a breakdown, a missed SLA or a maintenance backlog. The cost of reactive operations is a multiple of the cost of predictive operations.
Telemetry ingestion at scale
Real Time Asset Intelligence begins with connecting every asset to a live data layer. Infarsight Data Engineering builds the IoT ingestion pipeline that captures vehicle telemetry, equipment sensor data, energy meters and environmental feeds, processed at scale with sub second latency.
- Vehicle telemetry. engine health, fuel, location, speed, driver behaviour
- Equipment sensors, vibration, temperature, pressure, cycle counts
- Energy meters, consumption, load, fault codes, charging state
- Environmental feeds, route conditions, weather, traffic impacting asset performance
- Every asset emitting a live signal, no dark fleet or unmonitored equipment
- Data normalised across different asset types and OEM data formats
- Historical telemetry stored for pattern analysis and model training
Anomaly detection, prediction and alerting
AI models continuously analyse telemetry streams for anomalies, degradation patterns and failure precursors. When a pattern indicates an emerging failure, vibration drift, temperature spike, oil pressure drop, the agent predicts the remaining useful life, calculates operational impact and triggers the appropriate response before the asset fails.
- Mechanical degradation, vibration, temperature and pressure deviations before failure
- Fuel and energy inefficiency patterns indicating route, load or asset issues
- Battery health deterioration in EV fleets, charge cycle degradation and range impact
- Driver behaviour patterns correlating with vehicle wear and accident risk
- Failures predicted days or weeks ahead, maintenance scheduled before breakdown
- Operational impact calculated before a maintenance decision is made
- AI separates genuine alerts from noise, maintenance teams act on signals that matter
Automated maintenance and dispatch workflows
When AI identifies a maintenance need, Intelligent Automation executes the response, creating work orders, scheduling technicians, notifying dispatch, updating asset availability and propagating the change across all dependent operational systems. No manual ticketing. No missed handoffs.
- Work order generation and technician scheduling from predictive alerts
- Spare parts and inventory reservation triggered ahead of scheduled maintenance
- Dispatch and route reallocation when an asset is pulled for maintenance
- Asset availability status propagated across fleet management, dispatch and customer systems
- Maintenance response time reduced from days to hours
- Spare parts availability aligned with predicted maintenance schedule
- Operational disruption minimised, maintenance planned, not emergency
Asset intelligence platform
A purpose built asset health platform showing live status, predictive health scores, maintenance schedules and operational KPIs across every asset in the fleet or infrastructure estate, in a single, real time view accessible by operations, maintenance and management teams.
- Live asset map. every asset with real time location, status and health score
- Predictive health dashboard, risk scored assets with projected failure timeline
- Maintenance planner, upcoming scheduled work aligned with operational demand
- Utilisation analytics, efficiency, fuel, route and driver performance trends
- Fleet and maintenance managers operate from one source of truth
- Maintenance decisions made with operational impact visibility
- Asset utilisation optimised continuously, not reviewed quarterly
Continuous telemetry reliability and data governance
For Asset Intelligence to be trusted, the telemetry pipeline must be reliable. Platform Ops governs every data feed, detecting gaps, latency anomalies and sensor outages before they corrupt the predictive model or trigger false alerts. Data governance ensures asset data is accurate, complete and audit ready.
- Telemetry feed completeness, every asset signal gap detected and flagged immediately
- Data quality validation, values within expected range, anomalies distinguished from sensor faults
- Pipeline latency monitoring, sub second data delivery maintained across all feeds
- Compliance and audit, asset data retained, traceable and accessible for regulatory requirements
- Predictive models trained on clean, complete data, not corrupted by sensor noise
- Operations teams trust the alerts because the data is governed
- Regulatory compliance for asset data maintained without manual effort
Asset intelligence across sectors.
Fleet & EV Networks
Vehicle health monitoring, predictive maintenance, EV battery degradation tracking and charging network optimisation, reducing downtime, fuel costs and unplanned service events across large fleet operations.
Energy & Industrial Assets
Power generation, pipeline, turbine and industrial equipment monitoring, failure prediction, load optimisation and maintenance scheduling across geographically distributed infrastructure.
Port & Logistics Equipment
Crane health monitoring, container handling equipment condition tracking and port yard asset intelligence, maximising throughput by keeping critical equipment operational and maintenance aligned with vessel schedules.
Frequently asked questions: Asset Intelligence
What data does asset intelligence use to predict failures?
Asset intelligence ingests telemetry from vehicle CAN bus and OBD II interfaces, IoT sensors (temperature, vibration, pressure, current), GPS and GNSS location feeds and scheduled maintenance records. ML models trained on historical fault patterns detect anomalies in these real time streams, flagging at risk assets typically 24 to 72 hours before a fault would cause a service disruption.
Which sectors does real time asset intelligence apply to?
Asset intelligence programmes have been delivered for fleet operators (commercial vehicles, logistics), EV charging network operators, port terminal equipment operators (cranes, straddle carriers, gate systems), energy and industrial infrastructure and airline ground equipment. The underlying approach, continuous telemetry, anomaly detection, predictive alerting, applies wherever assets have sensor data and downtime has a material cost.
Does Infarsight provide the IoT platform or does it connect to existing infrastructure?
Both. Infarsight can deploy the full IoT data stack using Condense (the Zeliot real time data platform) for data ingestion and streaming, or connect to existing IoT platforms including AWS IoT Core, Azure IoT Hub and Siemens MindSphere. The integration services practice handles device connectivity across MQTT, OPC UA, Modbus and proprietary telematics protocols.
Ready to build predictive asset intelligence?
We start with an asset audit, mapping your current monitoring coverage, telemetry sources and highest cost unplanned maintenance events.
Book an Asset Audit →