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AI for predictive building maintenance from Sitowie

Workers with hard hats in an industrial environment

“Our customers build up a well-structured, reliable and accessible property database. They know how their buildings are aging and how they will age over time.”


Constructing and maintaining buildings requires significant investment. The start-up Sitowie, winner of the third edition of the Orange’s Women Start program, has developed innovative technologies to anticipate the aging of real estate and to optimize the corresponding maintenance work.

For Pauline Koch, a professional architect and CEO & Founder of Sitowie (one of the six start-ups selected in the third edition of the Women Start program organized by Orange), it was becoming difficult to design, construct and even manage buildings without knowing how they would age. “In France, we don’t have the data to tell us how buildings are aging,” she explains. “Moreover, the current trend is to construct new buildings that are increasingly unsustainable even though our global context shows us that resources are becoming scarce,” she continues.
In the face of these findings, Pauline Koch decided to study a master’s degree at the École des Ponts ParisTech (a prestigious French grande école specializing in engineering) to hone her skills in new technologies and, in particular, work on building sustainability. It was then that she presented her first research project, which aimed to simulate the aging of reinforced concrete facades in 3D. This scientific approach later enabled her, with the help of a 3D model, to reveal the effects of wind on a facade as well as the consequences of solar radiation on joints.

From 2D to 3D

In an industry where work is often still carried out using 2D section drawings, “BIM” (“Building Information Modelling”) has introduced 3D models to the world of architecture. Many customers, mainly wealth managers, wish to simulate the aging of their real estate. “3D models allow us to create a property database, simulate aging and thus optimize the maintenance and investment budgets of customer’s real estate. Thanks to this new technology, we are moving from a curative to a predictive approach,” the entrepreneur explains. “Customers are no longer waiting for a problem to appear before they can fix it. Interventions are made well in advance, which has a significant impact on budgets. Savings can be as high as 40%,” she says.

SaaS Platforms, AI, machine learning and professional expertise

Sitowie’s solution is an SaaS (“Software as a Service”) platform, called “Prédibat”, which can be accessed via an Internet browser. It combines AI with professional expertise and other technological building blocks (materials science, civil engineering, life cycle analysis and so on). There are no mysteries. Degradation is explained.
To produce this result, several types of data are collected. Firstly, maintenance work previously carried out by customers is obtained from their accounts, which gives access to the details of operations. Then open source data is collected, such as the address, year of construction, types of materials and height of the building.
“Our customers therefore build up a well-structured, reliable and accessible property database. They know how each element of their buildings are aging, and how they will age over time,” says Pauline Koch.
Moreover, the platform integrates parameters such as the carbon footprint of the maintenance, for example, and then adopts the best maintenance and investment strategy.
These innovations are becoming vital in light of the increasingly restrictive regulations regarding the environmental performance of buildings. 

Decision-making support

Finally, Sitowie supports its customers, providing services that include supplying and optimizing maintenance plans. “Our ‘probabilistic’ approach provides an indication of when a problem will occur in a building,” explains Pauline Koch. This is essential information when you have real estate of several thousand square meters that requires you to justify costs (often millions of euros) and to convince financial backers to grant budgets for certain buildings, or parts of a building, rather than others.

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