IoT & Sensor Analytics

Every Sensor. Every Signal.
Understood.

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.

<2s Detection 70% Downtime Reduction 72hr Advance Warning 10× Device Scale
KenIoT sensor analytics dashboard
What It Does

Complete IoT Intelligence

Six AI capabilities that turn sensor noise into operational clarity.

Multi-Protocol Ingestion

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.

Predictive Failure Detection

Machine learning models trained on equipment telemetry patterns predict failures 72 hours before they occur. Schedule maintenance before downtime; not after.

Real-Time Anomaly Alerts

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.

Automated Workflows

Build rules-based response workflows that automatically trigger maintenance tickets, alerts, equipment shutdowns, or API calls when anomalies are detected; no coding required.

Edge + Cloud Hybrid

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.

Telemetry Dashboards

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.

How It Works

How KenIoT Works

From device connection to predictive automation — industrial intelligence in three steps.

01

Connect Every Device

Ingests data from PLCs, sensors, and industrial systems via MQTT, OPC-UA, Modbus, and REST — without replacing existing hardware.

MQTT / OPC-UA Modbus / REST PLCs & Sensors
02

AI Learns Your Systems

Baseline models learn normal behaviour for every asset. Deviations in temperature, vibration, pressure, or throughput trigger anomaly detection before failure.

Anomaly Detection Baseline Learning Predictive Alerts
03

Predict & Automate

Maintenance tickets fire 72 hours before breakdown. Dashboards show every asset in real time. Your team fixes issues before they become outages.

72hr Warnings Auto Tickets Downtime Reduction
Industries

Where KenIoT Delivers

Sensor intelligence that connects, monitors, and predicts across every critical system.

KenIoT Results

Intelligence Before the Breakdown

70% Downtime Reduction Unplanned vs planned maintenance
72hr Early Warning Before equipment failure
<2s Alert Speed Anomaly detection latency
10× Device Scale Per single deployment
Get Started

Connect Every Sensor. See Everything.

Book a 30-minute demo and see how KenIoT can reduce unplanned downtime across your connected infrastructure.

FAQ

Frequently Asked Questions

How does KenIoT build a failure-prediction baseline for equipment it has never monitored before?

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.

What is the false-positive rate on KenIoT's 72-hour failure predictions?

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.

Can KenIoT monitor equipment that does not have standard sensor outputs — older machinery with no digital interface?

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.

How does KenIoT differentiate a genuine anomaly from normal operational variance — like a machine running hot on a summer day?

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.

Can we set different alerting thresholds for different assets — a critical line versus a non-critical pump?

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.