Intelligent and adaptive data processing on your edge devices
Edge AI Execution
Deploy AI on the edge devices to realize i.e. predictive maintenance, anomaly detection or process control.
Adaptive AI
Adapt and fine-tune your AI models on scale to account for individual local circumstances and requirements.
Decentralized Learning
Optimize your AI across facilities without exchanging raw data by utilizing federated learning
Why Edge ai?
Build and manage Machine Learning Models in a scalable way on distributed systems
Local, Decentralized, and Fast
Implement real-time data processing directly on the individual machine. This keeps your data within the company. React to changes in milliseconds.
Keeping your company activities running smoothly
ML-based features and services can continue to operate seamlessly even when machines or services are offline. By deploying ML models directly on the edge, our solution ensures that your operations remain unaffected.
Ensuring complete data control
You decide which data you process locally and which you can optionally transfer to a cloud for further processing.
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
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.
Federated Learning
Optimize your AI across facilities and devices without exchanging raw data, enhancing security and privacy.
Fueled by the latest technology
Use cases with Adaptive Edge AI
Discover how Industrial Edge AI is changing the manufacturing industry
Vision-based Process Control
Utilizing AI and computer vision technologies to monitor and optimize industrial processes based on real-time visual data analysis.
Condition Monitoring
Applying AI and sensor technologies to continuously monitor the condition and performance of equipment or systems, facilitating predictive maintenance and minimizing downtime.
Anomaly Detection
Identifying and flagging unusual or abnormal patterns in data, enabling early detection of anomalies and potential problems in complex systems.
Operation Parameter recommender
Analyzing data and recommending optimal operating parameters for various processes or systems, optimizing efficiency and production quality while minimizing manual intervention.
Energy Management
Continuously monitoring and analyzing energy consumption patterns to optimize energy usage resulting in cost reductions and improved sustainability.
Consumables Forecast
Leveraging decentralized AI to predict and forecast the usage and availability of consumable resources to optimize supply chain management and production planning.
Case studies
We guide you on the path to Industry 4.0 based on your individual needs
Integrated chip detection at Watts Industries Germany
Challenge
Chips (swarf) on the threads of underfloor heating manifold pipes cause leaks and require reworking or generate rejects. Manual spot checks are inefficient. Defective workpieces indicate wear on the machining tools, for example, so early detection is crucial. Every further process step with defective workpieces causes unnecessary costs. The aim is to automatically detect chips (swarf) on the workpiece within a few seconds without prolonging the overall process.
How we helped
We developed a pick-up station for distribution pipes with Watts and collected images of faulty and defect-free workpieces. With this data, an AI model for automatic chip recognition was trained and evaluated. After successful integration into the production process, the AI model enables efficient and reliable quality control.
Learn more about Edge AI
November 8, 2024
prenode’s AssetCore Monitoring live at SmartFactory-KL
September 3, 2024
prenode honored as top AI startup for the fourth time
April 18, 2024
Factory-X: The digital ecosystem for manufacturing — and prenode is on board!
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