ELIPSA AIoT ENGINE

PREDICT OUTLIERS

Monitor for abnormalities in your data

The Elipsa Engine allows users to quickly and easily build predictive models to detect outliers in their data.

Outlier detection using machine learning enables organizations to teach the system what normal looks like as it relates to their devices and machines.  With a trained understanding of "normal", the elipsa platform can monitor real-time streaming data to find abnormalities predicting problems before they occur.

SAMPLE USE CASE

PREDICTIVE MAINTENNCE

Smart Buildings

Air Duct

HVAC issues are persistent across buildings leading to unforeseen costs. Utilizing predictive maintenance can allow users to get ahead of problems before they occur

Manufacturing

Engine Block Pressure Testing

Machinery uptime is critical to the success of manufacturing firms.  Utilizing predictive maintenance can allow organizations to monitor the health of their factory floor reducing downtime and increasing revenue

Industrial Machinery

Heat Pump Service

Often times heavy machinery is running in remote locations that are difficult to assess.  Utilizing predictive maintenance can monitor remote systems more effectively to allow for better planning of maintenance.

30%

reduction in maintenance costs

30%

improvement in workforcce efficiency

25%

reduction in downtime