The Edge Signal platform facilitates the deployment of AI and machine learning algorithms directly at the edge, enabling intelligent decision making and automated actions without relying on cloud or centralized infrastructure.
This brings AI processing closer to the data source, reducing the need to transmit vast amounts of data to centralized cloud servers for analysis. Instead, the data is processed locally on the edge devices, enabling quicker response times, and reducing the burden on the network infrastructure.
The advantages of edge AI are multifold, including low latency, increased privacy and security, and bandwidth efficiency. Edge AI enables real-time or near-real-time decision-making, making it ideal for applications that require immediate responses, such as facial recognition, object detection, autonomous vehicles, industrial automation, natural language processing, and healthcare monitoring.
Arda Ozgun, Managing Director at Edge Signal, explains:
Artificial intelligence capabilities within Edge Signal enable our clients to do more with less. Imagine thousands of connected remote devices. Monitoring them all to analyze logs and metrics is not only time consuming but also costly. Artificial intelligence can find relevant logs that users might be interested in by searching for anomalies or highlights, or organizations can simply review a summary of the past days.
Edge Signal customers are utilizing the AI capabilities of the platform for object recognition, predictive maintenance, and anomaly detection. Each use case is vastly different. Whether it’s industrial IoT, wearable health trackers, retail analytics, energy management, etc., Edge Signal enables them all.
We utilize AI for our own internal purposes as well, for example for advanced log grooming and intelligent alert management.
Edge AI is applicable to a wide range of industries, including manufacturing, healthcare, transportation, agriculture, smart cities, and more. As adoption grows, it will empower a new generation of intelligent and autonomous devices.
However, before investing in edge applications, organizations must tackle edge infrastructure complexities, such as the management and updating of distributed edge devices and software systems, connectivity to remote nodes, security and privacy concerns with an expanded attack surface, monitoring and troubleshooting of highly complicated systems, and more. These are complex tasks.
Edge Signal addresses these challenges by providing an abstraction layer that removes these technological and organizational complexities and makes building and operating edge applications much easier. It provides a toolset featuring a low code/no code environment with numerous IoT and cloud integrations, AI runtime, cloud agnostic application plane, etc. to connect thousands of remote on-prem systems out of the box and to manage network connectivity whether it is 5G, WiFi, or other.
The heart of the Edge Signal platform is a single pane of glass management system with advanced monitoring, alert, log, metric, and remote control capabilities.
Edge Signal empowers organizations to quickly and efficiently harness the power of edge computing to enable new business models, enhance user experiences, and optimize resource utilization.