COLIGO EdgeStack

COLIGO EdgeStack is based on real-time Linux and Docker containers. Its plug-in design enables its functionality to be extended while reusing the existing data flow architecture.

Artificial Intelligence

We believe that AIoT can be best put in place in the field, by the Automation Engineer. Indeed, he is the one that best knows the processes, the machines and the automation systems. Therefore, we have developed COLIGO to enable AIoT from the field, with no specific knowledge for data analytics.

COLIGO implements a Streaming Learning Methodology which allows no-code MACHINE LEARNING applications. The streaming data pipeline continuously provides machine and process data, which is used by the selected MACHINE LEARNING model to train itself vs pre-training with available and pre-processed data.

Using this method, many MACHINE LEARNING applications like predictive analytics or anomaly detection can efficiently and easily be handled.

Customer specific ML applications

The architecture of COLIGO EdgeStack was developed for customer specific applications. More complicated ML applications may not be possible with streaming learning and may require specific development, model selection, data identification, model training and deployment. Our engineers are here to support any specific request so please do not hesitate to contact us to share your ideas, needs and requirements.