Intelligent and adaptive data processing on your edge devices
Build and manage machine learning models in a scalable way on distributed systems with full control over your data.
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
Realize real-time data processing directly at 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 full data control
Your decide which data to process locally and which to 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 reduced costs
By leveraging Edge AI, your business achieves better performance and accuracy while minimizing costs for data processing, data transfer, infrastructure, and energy.
Hardware-agnostic edge AI software
Experience seamless integration, flexibility, and compatibility across diverse hardware platforms, ensuring easy deployment and operation on a wide range of devices.
Fine-tuning AI models locally
Adaptively fine-tune your AI models directly on the edge devices to enhance accuracy with local data based on individual local circumstances and requirements.
Federated Learning
Optimize your AI across facilities and devices without exchanging raw data, enhancing security and privacy.
Fueled by
Use Cases with Adaptive Edge AI
Discover how industrial edge AI is transforming 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 different processes or systems, optimizing efficiency and production quality while minimizing manual intervention.
Vision-based Collision Control
Using computer vision and AI algorithms to detect and prevent potential collisions in real-time, enhancing safety and efficiency in various applications such as machine tools or robotic systems.
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
Revolutionizing Predictive Maintenance for WEISSER Precision Turning Machines with prenode Decentralized Machine Learning Solution
Challenge
Traditional predictive maintenance solutions require companies to share their sensitive machine data, often compromising their privacy and security. Additionally, gathering sufficient data from a single machine alone may not generate accurate predictions.
WEISSER faced these challenges when seeking to implement an effective predictive maintenance service for their customers.
How we helped:
To address these issues, we employed federated learning, a cutting-edge approach in the field of artificial intelligence. With prenode mlx, we can make use of multiple data sources and create robust machine learning models. Moreover, it allows the ML models on each machine continuously learn from each other, enhancing their predictive capabilities over time.
Our solution ensures privacy, security, and accurate predictions by sharing knowledge across multiple machines while keeping the data on edge at individual sites.
Learn more about Edge AI
July 26, 2021
prenode receives Award as AI Champion Baden-Württemberg 2021
October 14, 2021
Robin Hirt presents prenode at VDMA STARTUP THE FUTURE 2021
May 19, 2023
prenode and TRUMPF Win the "SCALE!" Category of 2023 Microsoft Intelligent Manufacturing Award (MIMA)
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