Reducing energy costs for consumers by using AI to model household energy use and identify targeted interventions, such as retrofitting and replacement.
Supporting emergency service response by bringing together a range of spatial data about the road and built environment to improve last mile routing.
Improving the response to extreme weather conditions by using AI and earth observation data to predict areas vulnerable to flooding, or to support better real-time spatial data of events such as wildfires and flash floods.
Reducing disruption to public services through predictive modelling of infrastructure resilience, with automated scheduling of maintenance, such as deploying teams to fix potholes or other traffic obstructions.
Enhancing food security by using earth observation and soil data to monitor and improve farming productivity and crop yield.
Improving efficiency and reducing resource consumption in manufacturing by using AI to optimise or automate energy-intensive processes.
