IoT Sensors

Integrations & API

Alerts & automation

Maintenance & work orders

Predictive Maintenance

Predictive maintenance (PdM) uses real-time data from sensors, IoT devices, and analytics to forecast when an asset is likely to fail, enabling maintenance to be performed just before a breakdown occurs. Techniques include vibration analysis, oil analysis, thermal imaging, and machine-learning models trained on historical failure data. It represents the most advanced tier of proactive maintenance strategies.

Condition-Based Maintenance

Condition-based maintenance (CBM) is a strategy that triggers maintenance actions based on the actual measured condition of an asset rather than fixed time intervals. Condition indicators may include vibration levels, temperature, pressure, fluid analysis results, or visual inspections. It sits between simple preventive maintenance and fully predictive maintenance on the maturity spectrum.

OEM Telematics

OEM telematics refers to the factory-installed tracking and diagnostic systems built into vehicles, plant, and heavy equipment by the original equipment manufacturer. These systems collect and transmit data including GPS location, engine hours, fuel consumption, fault codes, idle time, and operating parameters. Major OEMs such as Caterpillar, Komatsu, John Deere, Volvo, and Hitachi each offer proprietary telematics platforms.

GPS Tracking

GPS (Global Positioning System) tracking uses satellite signals to determine and record the real-time geographic location of assets, vehicles, or equipment fitted with GPS receivers. Tracking data is transmitted to a central platform via cellular or satellite networks, providing continuous visibility of asset movements, routes, and dwell times. GPS tracking is fundamental to fleet management and high-value mobile asset monitoring.

Asset Tracking

Asset tracking is the process of monitoring the location, status, custody, and condition of physical assets throughout their lifecycle. It combines identification technologies (QR codes, barcodes, RFID, GPS) with software to maintain a real-time or near-real-time record of where assets are and who is responsible for them. Asset tracking applies to tools, equipment, plant, fleet, IT hardware, and any other tangible items of value.

Bluetooth Beacon

A Bluetooth beacon is a small, battery-powered device that periodically broadcasts a Bluetooth Low Energy (BLE) signal containing a unique identifier. Nearby receivers, such as smartphones, gateways, or dedicated readers, detect the signal and use it to determine the presence and approximate location of the beacon. In asset tracking, beacons are attached to tools, equipment, containers, or vehicles, and their signals are picked up by fixed gateways installed around a facility or by workers' mobile devices as they move through the space. The location of the asset is then estimated using techniques such as signal strength (RSSI) trilateration, angle of arrival, or simple zone-based proximity detection. BLE beacons are valued for their low power consumption (battery life of one to five years), small form factor, and low unit cost, making them practical for tagging large volumes of assets across warehouses, workshops, construction sites, hospitals, and offices. Beacon form factors include adhesive-backed discs, rugged industrial housings rated for dust and moisture exposure, and compact tags that attach to tools via zip ties or lanyards, ensuring a suitable option exists for virtually any asset type or operating environment.

Digital Twin

A digital twin is a virtual representation of a physical asset, system, or process that is continuously updated with real-time and historical data from sensors, inspections, maintenance records, and operational systems. The digital twin mirrors the current state and behaviour of its physical counterpart, enabling engineers, operators, and maintenance teams to monitor performance, simulate scenarios, predict failures, and optimise operations without physically interacting with the asset. Digital twins range in complexity from simple data models that aggregate sensor readings and maintenance history for a single piece of equipment to sophisticated, physics-based simulations of entire facilities or infrastructure networks. The concept originated in aerospace and manufacturing but is now applied across industries including mining, energy, transport, construction, and facility management. At its simplest, a digital twin can be thought of as a comprehensive, live asset record that goes beyond static data by incorporating real-time condition feeds, enabling the system to reflect the asset's current health rather than just its last known service state.