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The Kaizis self-learning predictive maintenance decision support tool for facility managers of tertiary buildings cuts HVAC system failures by half and uses artificial intelligence to detect energy consumption abnormalities.
Kaizis is a small (10 cm x 2 cm) plug-and-play box which detects HVAC irregularities that can lead to energy overconsumption, performance drift or equipment malfunction. Without requiring the presence of energy sub-metering and with no need to manually enter any data, the building’s existing energy management system (a prerequisite for the installation of Kaizis) will directly transfer all the data that Kaizis needs to create a model which will detect abnormal activities (for instance on a cold or hot system) which will correspond to an overconsumption or a failure.
The solution provides a daily user-friendly report to the building’ facility manager which highlights sensitive points, accessible through a customised, end-to-end secured, cloud-based dashboard.
With Kaizis, HVAC system failures are reduced by 50% which translates to 10%-40% less maintenance costs and 3%-5% lower financial investments thanks to increased lifespan (i.e. your HVAC system will be running longer because it is better maintained). No upfront investment is required as Kaizis is available through a value-based subscription model, competitively priced based on the building’s specifications.
In France, white certificates (CEE) can contribute to the financing of Kaizis and technical support is fully available upon request.
For a quotation and any information that you might need. We will assist you throughout the process.
