Edge AI Execution
Deploy AI on the edge devices to realize i.e. predictive maintenance, anomaly detection or process control.
Adapt and fine-tune your AI models on scale to account for individual local circumstances and requirements.
Optimize your AI across facilities without exchanging raw data by utilizing federated learning
Organizations that want to share data, but are concerned about privacy, should explore a federated learning approach. [...] There is a small yet growing list of vendors using various approaches in that space, including [...] prenode
Features of our Edge AI solution
Real-time processing of data on the edge
Our Edge AI Solution empowers your devices to analyze data instantly, enabling immediate decision-making without delays.
Reduced reliance on cloud services
Data is processed directly on your devices without the need for constant cloud connectivity, giving the ability to operate in offline or low-connectivity environments and enhancing security.
Improved efficiency and resuced costs
By leveraging Edge AI, your business achieves better performance and accuracy while minimizing costs for data processing, data transfer, infrastructure, and energy.
Hardware-agnosticEdge AI Software
Experience seamless integration, flexibility, and compatibility across diverse hardware platforms, ensuring easy deployment and operation on a wide range of devices.
Fine-turning AI models locally
By adaptively fine-tuning your AI models directly on edge devices, you improve accuracy using local data tailored to individual circumstances and requirements.
Optimize your AI across facilities and devices without exchanging raw data, enhancing security and privacy.
Utilizing AI and computer vision technologies to monitor and optimize industrial processes based on real-time visual data analysis.
Applying AI and sensor technologies to continuously monitor the condition and performance of equipment or systems, facilitating predictive maintenance and minimizing downtime.
Identifying and flagging unusual or abnormal patterns in data, enabling early detection of anomalies and potential problems in complex systems.
Analyzing data and recommending optimal operating parameters for various processes or systems, optimizing efficiency and production quality while minimizing manual intervention.
Continuously monitoring and analyzing energy consumption patterns to optimize energy usage resulting in cost reductions and improved sustainability.
Leveraging decentralized AI to predict and forecast the usage and availability of consumable resources to optimize supply chain management and production planning.