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Iotron AI

Iotron Sentinel · Live · AIoT · Predictive maintenance

Maintenance is either scheduled too often or triggered too late. Sensor data exists but sits in silos, and alerts are either too noisy to trust or too late to matter.

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Iotron Sentinel interface
Illustrative interface of Iotron Sentinel, with example data; not a live system or customer data.

01How it works

Data in, intelligence applied, action out.

Iotron Sentinel interface
Illustrative interface of Iotron Sentinel, with example data; not a live system or customer data.
  1. 01

    Connect

    Ingest telemetry from sensors, PLC gateways and existing systems over common industrial and IoT protocols.

  2. 02

    Understand

    Edge and cloud models learn each asset's normal behaviour, detect drift and estimate remaining useful life.

  3. 03

    Act

    Prioritised alerts, work-order suggestions and dashboards route the right task to the right team.

02Capabilities

What it does.

Configured to your assets, documents or systems — and integrated with the tools your teams already use.

  1. 01

    Asset registry and health scoring by machine, line and site

  2. 02

    Real-time anomaly alerts with the contributing signals shown

  3. 03

    Remaining-useful-life and failure-risk forecasts

  4. 04

    Maintenance recommendations and work-order hand-off to your CMMS/ERP

  5. 05

    Fleet-wide comparison and threshold management

  6. 06

    Edge deployment for low-latency, low-bandwidth or offline sites

AI inside

  • Unsupervised anomaly detection on multivariate time series (for example isolation-based and autoencoder approaches)
  • Supervised failure prediction and remaining-useful-life models where failure history exists
  • Signal processing features from vibration, current and temperature data
  • Model compression for gateways and edge devices, with versioned over-the-air updates

03Architecture & deployment

Built to fit what you run.

  1. 01Sensors and gateways
  2. 02edge inference
  3. 03secure message broker
  4. 04time-series store
  5. 05model training and evaluation
  6. 06alerts, dashboards and APIs.
Deployment
  • Cloud (SaaS)
  • Your private cloud
  • On-premises
  • Hybrid with edge inference
Integrations
  • MQTT, OPC UA and Modbus (via gateways)
  • REST and webhooks
  • Kafka
  • CMMS/ERP and ticketing systems
  • Email, Teams and Slack alerts
Security
  • Role-based access
  • Audit logging
  • Encryption in transit and at rest
  • Per-tenant data separation
  • Regional hosting options

04Business benefits

Why teams use it.

  • Earlier warning of degradation
  • Maintenance effort focused on the assets that need it
  • One consistent view of equipment health across sites

Use cases by industry

  • Manufacturing

    Motors, pumps, compressors and production lines

  • Logistics

    Vehicle and refrigeration-unit condition

  • Real estate and facilities

    HVAC and building systems

  • Telecom

    Tower and power-system monitoring

Iotron Sentinel: common questions

Do we need failure history to start?

No. Anomaly detection begins with normal-behaviour data; failure-prediction models improve as events are recorded.

Can it run without internet?

Yes. Edge inference can run locally and sync when connectivity returns.

What sensors are supported?

Most common industrial sensors through gateways; we assess your equipment during onboarding.

Get in touch

See Iotron Sentinel working on your data.

Tell us about the problem behind your interest in Iotron Sentinel. We will set up a demo with an engineer who knows the product and talk through deployment, integration and pricing.