Predict Future Events with your IoT Data

The Elipsa Engine allows users to quickly and easily build predictive models to predict events from their data.

An event is a binary occurrence.  The system can classify events such as whether a room will go over a certain level of Co2, whether a part will fail, or whether a finished product will be defective.

Utilizing your seemingly independent sensor data points, the Elipsa Platform enables users to build predictive models to avoid looking at alerts of the past and start looking at the state of the future.



Air Quality Monitoring

Monitor whether air quality metrics such as Co2, will exceed unsafe levels in a defined amount of time.  Smart spaces can ensure the comfort and safety of occupants. 

Defect Detection

Utilizing information such as machine settings and attributes of input materials, users can predict the output of their production processes.  Accurately predicting defects can help reduce costs, save time, and increase quality

Machine Monitoring

With recorded examples of historical failures, users can build predictive models to predict future failures.  These predictions can enable companies to perform maintenance in order to avoid unforeseen downtime and keep machines running


In a test use case of detecting whether products were defective in a raw material processing use case, Elipsa was able to quickly build a model that was 97% accurate in classifying output products as defective or not.


These predictions can allow organizations to optimize machine settings ahead of a production run to reduce defects thus reducing waste, saving time, and increasing quality.

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