Plegma labs S.A. (PLEGMA)
Plegma Labs S.A. was established by a team of seasoned professionals participating in the evolutionary transition toward the Internet of Everything. Our ever-evolving system overcomes fragmentation issues and reduces the complexity of acquiring, storing, and managing all this newly available data produced by sensors, smart devices, other systems, and real-life events. We bridge protocol barriers between different hardware/software vendors and apply meaningful rules and workflows that add intelligence to each of our system applications, ultimately leading to efficiency and optimization. Plegma Labs has inherited the expertise, technical know-how, and hands-on experience of our founders, who have participated in numerous large-scale commercial, governmental, and research projects.
Plegma IoT Platform, our flagship product, is a custom IoT cloud platform with a wide range 
of commercial, residential, and industrial applications. Being hardware agnostic, it collects data from smart meters and sensors (e.g. weather, environmental, and ambient sensors) and delivers custom analytics and visualizations providing valuable insight. The platform allows easy integration, management, control, and actuation of diverse devices, as well as device data sharing with other users, supporting third-party applications. Plegma IoT Platform offers multi-site visualization of actionable real-time and historical data, meter data-logging and multi-sensor management, multi-channel notifications, and alerts, with minimum hardware footprint and setup downtime.
Plegma Labs develops the environmental monitoring networks as well as the smart control and automation components for the RES4LIVE project on the project pilots to increase the penetration of RES technologies for livestock farming and optimize livestock conditions. Plegma provides a cloud-based solution that provides real-time remote monitoring of farm environmental conditions and facilitates data analytics and novel simulation methods to reduce energy consumption through optimal control/ load shifting techniques based on predictions about demand and production of energy through RES technologies, while at the same time offering a real-time remote-control system.
