COLIGO Lite uses streaming learning to detect anomalies and generate forecasts in real time - without any prior data collection.
Communication with the rest of the firmware takes place efficiently via MQTT.
COLIGO offers out-of-the box ML models that require nothing else but a simple configuration of the data to analyze.
Anomaly Detection
Anomaly detection identifies abnormal events, malfunctions, or deviations from expected patterns, reducing downtime and enhancing safety and efficiency.
Predictive Analytics
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.
Configuration
The COLIGO lite user interface allows the user to specifically configure the data to be analyzed.
Deployment
The container is available to the user for flexible use in their systems. Users are responsible for connecting to data sources and databases.
Supported platforms
COLIGO lite is available directly from our sales department (sales@coligo.ai) or via industrial marketplaces.
You can find a suitable marketplace here.
COLIGO lite is currently available on Flecs.
