Manufacturing & IoT · Solution we build

Predictive Maintenance (IoT + ML)

Predict machine failures before they stop production. Predictive maintenance combines IoT sensors (vibration, temperature, current) with machine-learning models that detect early signs of failure, so maintenance teams fix problems before breakdowns happen.

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Predictive Maintenance (IoT + ML): Predict machine failures before they stop production.

Sensor integration Edge gateway 12–22 weeks typical timeline

Typical timeline12–22 weeksfor a first production release

PlatformsWeb dashboard, mobile alerts

IndustryManufacturing & IoTManufacturing plants, Utilities and pumping stations

Key features

What your Predictive Maintenance (IoT + ML) includes

Sensor integration

Vibration, temperature, pressure and current sensors.

Edge gateway

Local data collection and buffering at the plant.

Anomaly detection

ML models learn normal behaviour and flag deviations.

Failure prediction

Remaining useful life estimates for critical assets.

Alerts & work orders

Automatic alerts and maintenance tickets.

Asset health dashboard

Health scores and trends across the plant.

Who it's for

Manufacturing plants
Utilities and pumping stations
HVAC and facility operators
Heavy equipment owners
Fewer unexpected breakdowns
Lower maintenance costs
Longer equipment life
Data-driven maintenance planning

Business benefits

Fewer unexpected breakdowns
Lower maintenance costs
Longer equipment life
Data-driven maintenance planning

Technology stack

Built on proven, scalable tech

Typical stack shown. We choose technologies based on your scale, team and existing systems.

Python (scikit-learn, PyTorch)MQTTInfluxDBGrafanaAWS IoT / Azure IoT

How we deliver

From discovery to launch

STEP 01

Discovery

Workshops to understand your workflows, users and must-have features.

STEP 02

Design

Wireframes, clickable prototypes and a technical architecture you approve.

STEP 03

Build & test

Agile sprints with demos, code reviews and automated QA.

STEP 04

Launch & support

Cloud deployment, training, monitoring and ongoing improvements.

FAQ

Frequently asked questions

How long does it take to build a predictive maintenance (iot + ml)?

A typical first version takes around 12–22 weeks, depending on the features, integrations and platforms you need. We share a detailed timeline after the discovery phase.

Can the features be customised for our business?

Yes. Every solution is built for your workflows. The features on this page are a starting point; we add, remove or adapt modules based on your requirements.

How much historical data is needed?

Anomaly detection can start within weeks of sensor data; failure prediction improves as more operating and failure history is collected.

Who owns the source code?

You do. On project completion the source code and intellectual property are handed over to you, and we can continue with support and enhancements if you wish.