Hardware & Sensors
Compute, i.e. digital brainpower, and data are the main resources for the development and use of AI solutions and Machine learning models. In most cases, cloud providers supply these resources.
However, many complex business situations require additional compute resources to be installed and managed on-premise. Examples are:
- Machine learning for automatic quality assurance in manufacturing. Sorting products on a high-speed conveyor belt requires fast and accurate decision-making. Any latency in data processing would result in wasted materials and high costs.
- Processing highly sensitive data that requires private, on-premises servers to meet GDPR requirements.
Our hardware solutions help you overcome these challenges, but we take it a step further with our sensor expertise. This unlocks a whole new set of AI business opportunities. Our use of depth cameras, temperature and pressure sensors adds - literally - a new dimension to your data.
Typical challenges
Our hardware and sensor solutions overcome even the most complex functional and technical obstacles, making sure you achieve all your business goals.
Limited compute power and memory
When the unlimited scaling potential of the cloud isn’t accessible, we carefully select and tune our AI models to stay within our own hardware's capabilities so we don’t compromise on accuracy or performance.
Diagnosability and transparency
Built to be an indispensable part of your installation, we always make sure our solutions are transparent so you can diagnose and troubleshoot problems yourself.
Noisy sensor data and sensor drift
Effective integration with physical sensors requires checking the sensory input for any noise, drift or outliers. We make this possible through alerting mechanisms or by using an AI solution that is robust enough to deal with these.
Security
Using on-premises hardware poses additional security risks because you cannot rely on a cloud provider's managed security services. However, we always take this into account when solving your business problem. In addition, we advise you to maintain a separation between IT and OT in manufacturing environments.
High level outline of the solution
We typically recommend a hybrid architecture in which we only deploy what is absolutely necessary on-premise. All other solution components that can be moved to the cloud will be moved.
Using the conveyor belt example from earlier, we would keep our trained AI model and data collection on-premises, but the collected data would be uploaded to the cloud for storage, human annotation, and retraining of a superior AI model. The superior model would then be used on-premises. Since the uploaded data is in the cloud, it allows us to test a wide range of solutions without being limited by hardware.
Contact our experts in Hardware and Sensors
Let us help you unlock new business opportunities with our advanced AI solutions, combining Hardware and Sensor Expertise for accurate, high-speed decision-making in manufacturing and beyond.