A tool called assesses a property’s wildfire risk and assigns a score based on its age, roof type, materials, nearby vegetation and the slope of the surrounding land. The company intends to add functions that will measure risks from other natural disasters including flooding and hurricanes.



tanding on the outskirts of Oakland, California, Attila Toth takes in the nearby forested hills. The CEO looks out on what locals call “The Town” and, in the distance, San Francisco, or “The City.” Close by, Toth sees tangles of redwood, eucalyptus and oak trees – and the wildfire risk they pose.

This “wildland-urban interface” isn’t far from the site of the 1991 Oakland Hills Fire, which flared up suddenly in a heavily residential area. Over four days, 3,000 thousand homes were destroyed in one of the city’s wealthiest neighborhoods, causing an estimated $1.5 billion in damages ($3.2 billion in today’s dollars). Twenty-five people were killed. This area, Toth says, will almost certainly burn again.

The uncertainty is when, and what other areas are at risk. “The core is a lack of data-driven understanding that every single homeowner and business owner is facing,” says Toth, 49.

That is where Toth’s seven-year-old startup, comes in. His company has been gathering data and using it to train machine learning models to better assess risks caused by climate change, like wildfires, on behalf of its clients, mostly insurance companies.

“We take satellite imagery, we take building permit data, we take local weather station data, and we are using artificial intelligence in order to explain the impact of climate risk to every single property,” he says. There’s no shortage of need. In the Golden State alone, eight of the state’s 10-most destructive fires have occurred within the past five years, according to the California Department of Forestry and Fire Protection, or CalFire.