Machine learning and IoT for solar plant control and fault detection
A patented approach that combines IoT sensing with machine learning to control a solar plant, detect faults early and predict energy output.
Digital twin
Live virtual models of farms, facilities and assets that let you test a decision before you make it.
Overview
A digital twin mirrors a real farm, facility or asset by combining sensor, satellite and operational data with simulation. Teams use it to see current conditions at a glance, predict failures or yields, and try "what if" scenarios safely before acting in the real world.
What we build
- Data fusion
- Combine IoT sensors, satellite imagery, weather feeds and operational records into one live model.
- Simulation and what-if
- Test irrigation plans, maintenance schedules or capacity changes before committing to them.
- Predictive maintenance
- Spot early signs of equipment failure and schedule repairs before breakdowns.
- Operational dashboards
- Maps and dashboards that show the state of every field, machine or site in real time.
Use cases by industry
Where digital twin makes the biggest difference for our clients.
How we deliver
Discover
A short workshop to understand the problem, the data and what success looks like.
Prototype
A working proof of concept on a sample of your data, so you can judge results early.
Build
The full solution, tested against your data and integrated with your systems.
Deploy and support
Production deployment on your cloud, with monitoring and continuous improvement.
Questions
Do we need sensors to start?
Not always. Many twins start with satellite, weather and existing operational data, adding sensors where they add the most value.
How accurate is a digital twin?
We calibrate the twin against historical data and report its accuracy, then keep recalibrating as new data arrives.
More technologies
Explore digital twin for your organisation
Start with a free discovery call. We will tell you honestly what is possible with your data.