IoT & Sensor Analytics
Predictive intelligence for connected infrastructure. KenIoT ingests data from any sensor, detects anomalies in real time, and warns of equipment failures well in advance.
From device connection to predictive automation in three steps.
Works with your existing sensors and industrial systems without replacing any hardware.
Baseline models learn normal behaviour for every asset and flag deviations before they become failures.
Maintenance tickets fire in advance of breakdown. Your team fixes issues before they become outages.
Sensor intelligence that predicts and prevents failures across every critical system.
Monitor interlocking panels, track occupancy, and detect anomalies before failures disrupt operations.
Multi-protocol sensor fusion for perimeter control, environmental monitoring, and tamper detection at secure installations.
Connect soil, weather, and crop sensors to drive precision irrigation and early pest detection.
Monitor sensor data from medical equipment and facility systems with real-time alerts.
Monitor engine health and driver behaviour to predict failures and optimise fleet routes.
See how KenIoT reduces unplanned downtime across your connected infrastructure.
KenIoT runs an initial learning period on new assets, collecting vibration, temperature, pressure, and current signatures during normal operation. It builds an asset-specific baseline rather than using generic industry templates which is why its false-positive rate is significantly lower than threshold-based alerting systems. After the learning period, anomaly detection and failure prediction are active.
Across production deployments, KenIoT's failure predictions run at a low false-positive rate after the initial learning period. The system applies a confidence threshold before alerting below that threshold, it logs the anomaly for review rather than issuing a maintenance alert. You can adjust the threshold per asset class based on the cost trade-off between unnecessary maintenance and unexpected failure.
Yes. For equipment without native digital sensors, KenIoT supports retrofit sensor kits that attach externally. We advise on the right configuration at onboarding based on your asset type and the failure modes you need to detect.
KenIoT's baseline model accounts for environmental variables including ambient temperature, production load, and time-of-day patterns. It does not alert on a machine running at its expected summer temperature it alerts when temperature behaviour deviates from what the model predicts given those conditions. Environmental context is baked into the anomaly detection, not stripped out.
Yes. KenIoT supports per-asset alert configuration. You can set alert priority, notification routing, and prediction confidence thresholds independently for each monitored asset. A failure on your primary production line triggers an immediate escalation; a non-critical pump triggers a scheduled maintenance recommendation. Asset criticality tiers are defined at onboarding.
