IoT & Sensor Analytics
Predictive intelligence for connected infrastructure. KenIoT ingests data from any protocol, detects anomalies in under 2 seconds, and warns of equipment failures 72 hours before they happen.
Six AI capabilities that turn sensor noise into operational clarity.
Connect any device or sensor using MQTT, OPC-UA, Modbus, REST, CoAP, or custom protocols. KenIoT normalises heterogeneous data streams into a unified analytics layer.
Machine learning models trained on equipment telemetry patterns predict failures 72 hours before they occur. Schedule maintenance before downtime; not after.
Detect statistical anomalies across thousands of sensor streams in under 2 seconds. Context-aware alerts reduce false positives by 80% compared to threshold-based systems.
Build rules-based response workflows that automatically trigger maintenance tickets, alerts, equipment shutdowns, or API calls when anomalies are detected; no coding required.
Deploy inference at the edge for ultra-low latency and bandwidth efficiency, with cloud aggregation for cross-site analytics and fleet-level intelligence. Works offline too.
Custom real-time dashboards for every operator role. Drag-and-drop widgets, configurable KPIs, and one-click export to BI tools like Tableau, Power BI, or Grafana.
From device connection to predictive automation — industrial intelligence in three steps.
Ingests data from PLCs, sensors, and industrial systems via MQTT, OPC-UA, Modbus, and REST — without replacing existing hardware.
Baseline models learn normal behaviour for every asset. Deviations in temperature, vibration, pressure, or throughput trigger anomaly detection before failure.
Maintenance tickets fire 72 hours before breakdown. Dashboards show every asset in real time. Your team fixes issues before they become outages.
Sensor intelligence that connects, monitors, and predicts across every critical system.
Monitor water, electricity, and gas networks at scale. Detect leaks, outages, and anomalies instantly.
Eliminate unplanned downtime with predictive maintenance that gives teams time to schedule repairs without production loss.
Monitor engine health, fuel, and driver behaviour across entire fleets. Predict failures and optimise routes from real vehicle data.
Monitor interlocking panels, track occupancy, and signal states. Detect anomalies before failures disrupt operations.
Detect pressure drops, temperature spikes, and corrosion signals across pipelines and refineries before they become incidents.
Integrate BMS, HVAC, lifts, and security into one IoT fabric. Detect faults and optimise energy from a single dashboard.
Multi-protocol sensor fusion for perimeter control, environmental monitoring, and tamper detection at secure installations.
Track cold chain compliance, sterilisation cycles, and equipment uptime. Trigger alerts on any deviation from protocol.
Book a 30-minute demo and see how KenIoT can reduce unplanned downtime across your connected infrastructure.
KenIoT runs a 2-4 week 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 72-hour failure predictions run at under 8% 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 (vibration, temperature, current clamp) that attach externally and feed data via MQTT or Modbus. We specify the sensor configuration required at onboarding based on the asset type and 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.
