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Iotron AI

AI solution · Edge AI & AIoT

Run intelligence on devices and gateways, connect it to the cloud, and turn sensor data into predictions and real-time decisions — engineered end to end.

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01The problem

Most AI projects start after the data has been collected. In connected operations, the hard problems come first: getting clean data from devices, processing it close to where it is generated, and keeping models running reliably in the field.

The Iotron approach

We place each workload deliberately — on the device, at the gateway or in the cloud — based on latency, connectivity, cost and data sensitivity.

02Use cases

Where it makes a difference.

Each one scoped against a real decision, evaluated on your data and integrated with the systems you already run.

  1. 01

    Predictive maintenance — detect degradation patterns in vibration, temperature, current and other signals so teams service equipment before it fails

  2. 02

    Real-time anomaly detection — on-device or gateway inference that flags irregularities within seconds

  3. 03

    Visual inspection at the edge — cameras plus optimised models for quality control on the line

  4. 04

    Sensor analytics & forecasting — energy, capacity and utilisation insights from IoT telemetry

  5. 05

    Fleet and asset intelligence — location, condition and utilisation signals turned into decisions

03How it works

From data to decision.

The typical architecture, from your data to a decision people can trust. Every stage is evaluated, monitored and logged.

  1. 01InputSensors & devices
  2. 02Stage 2Edge/gateway (filtering, inference)
  3. 03Stage 3Secure transport
  4. 04Stage 4Data platform (time-series, storage)
  5. 05Stage 5Model training & MLOps
  6. 06OutcomeApplications, alerts & integrations

04Architecture & control

What it takes — and how it stays safe.

What goes in, what the system does with it, and the controls around every stage.

Inputs

Data you need

  • Time-series from the assets
  • Maintenance and failure history
  • Asset metadata
  • Access to domain experts

System

From data to decision

  1. 01Sensors & devices
  2. 02Edge/gateway (filtering, inference)
  3. 03Secure transport
  4. 04Data platform (time-series, storage)
  5. 05Model training & MLOps
  6. 06Applications, alerts & integrations

Controls

Security & governance

  • Device firmware and connectivity
  • Data quality and calibration
  • Model compression and hardware fit
  • Secure over-the-air updates
  • Fleet monitoring
  • Integration with existing OT/IT systems

Applied at every stage, not added at the end.

05Industries

Where it is applied.

Industries where we build this capability into operations.

Edge AI & AIoT: common questions

Do we need to replace our equipment?

Often not — many machines can be instrumented with retrofit sensors.

How do you keep edge models current?

Versioned models, staged rollouts and monitoring; updates are delivered securely.

Project scenario · Manufacturing & Industrial

Predicting failures on rotating equipment across three plants — without waiting for failure history.

See how the project runs

From our engineers

Edge AI vs cloud AI: how to decide

A practical framework for deciding what runs on the device, what runs at the gateway, and what belongs in the cloud.

Read the article

Get in touch

Ready to discuss Edge AI & AIoT?

Tell us where edge ai & aiot could change a decision or a workflow. We will give you an honest view of feasibility, data needs and the first step.