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:
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.
Discover how our expertise in Computer vision leads businesses to success
The AI driven model is a powerful tool in assisting journalists in their writing work by speeding up the summary creation as well as suggest alternative wordings. In that way, human creativity and AI can enhance each other’s complementary strengths and produce high quality results.
May 16, 2022
Our hardware and sensor solutions overcome even the most complex functional and technical obstacles, making sure you achieve all your business goals.
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.
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.
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.
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.
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.
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.