Data to Decisions in a Blink: Real-Time Intelligence at the Edge
In today’s fast-paced world, enterprises need real-time, actionable insights to optimize their operations and make critical decisions directly at the edge. These insights are usually derived by combining sensor information with data analytics. However, traditional data analytics often fall short for scenarios where latency and large amounts of data are prominent.
That’s where VersaSense Edge comes in, an innovative platform that extends to on-premise deployment of real-time, AI-driven insights. VersaSense Edge combines the power of IoT-based sensing with cutting-edge edge AI to provide instant insights directly where you need them. The platform supports integration with various kinds of sensors or sensor data sources, such as cameras, microphones, or vibration sensors. Translate raw data into actionable intelligence instantly, empowering your team to optimize operations, improve efficiency, and gain a competitive edge.
With VersaSense Edge, you can transcend the limitations of traditional analytics by harnessing the full potential of real-time data processing at the edge. Say goodbye to delays and inefficiencies, and embrace a future where decisions are made swiftly, intelligently, and precisely where they matter most.
implement real-time productivity insights
Connect. Train model. Improve.
By combining the right sensors with AI running on the edge, enterprises can detect production issues more rapidly and immediate decisions before they hit your operations with 10x.
Select and deploy sensors
Depending on the quality control problem, select the correct sensor, such as sound or 2D imaging. Install sensor on the production line. Using a suitable network technology the sensor will connect to the model backend upstream and provide measurement sets fed into the analytics.
Tune model and operate
Based on the labeled measurement sets tune one of the shrink-wrapped models of the VersaSense Product Quality solution. Operation can start as soon as the model has learned sufficient data. Results are fed back to the OT infrastructure to redirect defects to rework or scrap.
Refine and improve
Minor product variations can easily be incorporated in the constructed analytics, as can additional defect modes. Track production defect performance by identifying trends and alert on quality anomalies. Link defects to root cause to improve actual production line performance.