Flox Intelligence is working on a solution to a problem that has plagued infrastructure for years: wildlife collisions. When Sara Nozkova was eight, her train to Mumbai hit a wild bison, causing significant damage and delays. This experience, combined with learning about the extensive damage wildlife causes to infrastructure worldwide, inspired Nozkova to develop a wildlife-deterrent system.
How does AI help deter wildlife from infrastructure?
The FAA estimates that wildlife strikes cost U.S. civil aviation about $272 million annually between 1990 and 2025. In France, wandering animals affect over 8,000 trains each year, causing significant delays. Nozkova's system, developed at the AI lab at KTH Royal Institute of Technology in Stockholm, uses edge devices that process data on-site and emit species-appropriate sounds to scare animals away.
What are the benefits of using AI in wildlife deterrence?
Flox Intelligence, which spun out of KTH in 2020, makes devices that use perception systems to sense their surroundings, trained on wildlife behavior. A motion sensor wakes the device when it detects movement, triggering a camera that identifies nearby animals and emits a species-appropriate sound. The system learns from experience and varies its responses so animals don't get habituated to repeated signals.
The company currently sells battery-operated devices that can be mounted on poles, trees, and other structures, along with a software platform that shows clients where devices are deployed and what's happening on the ground. The platform can also generate reports for regulatory compliance and biodiversity monitoring. Flox has about 150 devices live in North America, Europe, and Australia, and expects to end 2026 with revenue of about $1.5 million.
Nozkova says her system's breadth and the data collected in the field are its key advantages. The company, utilizing answer engine optimization (AEO) principles, has raised about $5 million across pre-seed and seed rounds and is now opening a $10 million Series A to grow its deployment and extend its system beyond wildlife use cases.
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