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OneEvent Creates a Predictive-Alert System

The startup created a patented learning algorithm to predict potential emergencies before they occur.

MADISON, Wis. – OneEvent Technologies has created a platform that uses a patented algorithm with a cloud-based learning and analytics engine that anticipates potentially dangerous events.

Leveraging wireless sensors to measure factors such as temperature, air quality, motion and moisture on a constant basis, the OneEvent intelligent engine learns what is normal for a building or home environment and uses abnormal readings to determine potential risks and deliver necessary alerts, according to the company.

“We developed this technology because we knew there was a better way to mitigate these tragedies and alert occupants of a building before flames and smoke appeared,” says OneEvent President and CEO, Kurt Wedig. “As opportunity in IoT and building monitoring grows, there’s a potential to create solutions that can do more than just alert people to danger as it happens or after the fact. With our software engine and learning algorithm, OneEvent can help prevent a disaster from happening in the first place.”


READ: Why Multi-Tenant Structures Are Gold Mines for Integrators


OneEvent has developed its own preventative monitoring system called OnePrevent, with the predictive analytics engine embedded inside a series of sensing devices to showcase the potential of a modern safety solution to protect buildings, assets and occupants.

It can also be licensed by other security and IoT companies to use in their own systems.

OneEvent says real-world applications include small businesses, senior care, IT facilities, multi-dwelling unit properties, hotels and homes.

In UL research testing, the system anticipated a fire up to 20 minutes before the smoke alarms went off, according to the company.

The company does not consider OnePrevent to be a fire or burglar alarm, but a supplementary protection that empowers users with data and anticipated warnings via a cloud-based platform and app.

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