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 offers out-of-the box ML models that requires nothing else but a simple configuration of the data to analyze.
Anomaly detection identifies abnormal events, malfunctions, or deviations from expected patterns, reducing downtime and enhancing safety and efficiency.
Time series forecasting is the practice of using historical data from sensors and devices to predict future trends, enabling proactive decision-making and enhancing operational efficiency.
Object detection/recognition involves the real-time identification and tracking of objects within video feeds. This enables IoT systems to understand and respond to the visual information, making it valuable for applications like security, surveillance, and smart environments.
Noise detection/recognition in audio streams allows the real-time analysis and identification of unwanted sounds or disruptions within the ambient audio data. This ensures operational integrity, safety, and quality control by promptly flagging and addressing any undesirable auditory elements.
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.