Customers & Partners

When the going gets tough... Sensors usually fail

Robots today are smart. They can make sense of complex visual or auditory patterns, avoid obstacles, and move around autonomously. But when it comes to harsh conditions they are often fooled or fail completely. This is a problem that already affects robots in industrial applications, and it is preventing the development of new autonomous systems that could save lives across a wide range of applications, including infrastructure monitoring, transportation, and emergency services.

Conditions where sensors usually fail.

For example, we are developing software that allows sensors to accurately collect data in harsh environments. We are starting within challenging infrastructure environments across the water and rail networks. This will help to:

  • Identify defects before they become problems (predictive maintenance)
  • Improve worker health and safety by enabling robots to enter confined spaces
  • Save maintenance costs

Our patent pending AI software, SPADAR™, allows sensors to adapt in real-time to their environment, enabling them to work in conditions where they usually fail.

SPADAR™ Demo (Automotive Use-case)

Our patent pending AI software, See Less. Infer More. (SLIM)™ accelerates AI training and reduces memory footprint for Edge devices.

Run SLIM™ (Demo Code)

Mapping the unknown?
Our software ensures quality data

When we enter a new environment, our senses adapt. We think machines should do the same. Our patent-pending technology allows machines to adapt to their environment. This optimises both data collection and quality. We can help you get the most from your existing sensor network and integrate these sensors with our AI software. Alternatively, we can help you select new sensor systems that are compatible with our AI software, to ensure you can map any unknown environment.

Accelerated AI on the Edge

Running AI software locally (on the edge) is more complex than across a network (e.g. in the cloud). You need energy efficient algorithms, especially for light weight machines (e.g. drones). We are developing efficient deep learning algorithms that can run on low power devices (e.g. FPGAs). This extends the operational life of machines that are reliant on batteries, and allow them to keep making intelligent decisions. For example, to save power in ideal conditions, to report a defect on a safety critical asset, and to adapt to their environmental conditions.

Enabling Machines to 'See Beyond'

We provide automated reporting software for infrastructure monitoring. This solution can help you to monitor your safety critical assets and provide optimised maintenance schedules, meaning the right people can get to the right place at the right time. Our current software can operate in complex locations; deep underground, in confined spaces, and across high-speed transport networks (e.g. rail).

We can enable you to see in the dark or in low visibility, build digital catalogues of your assets in real-time, and scale solutions from edge devices to the cloud. In the cloud, we can provide further tools to post process your data and conduct further analysis and reporting.