Wildlife and Infrastructure: An Ongoing Challenge
When I was eight, I remember our train to Mumbai came to an abrupt halt with a loud noise. After a delay of nearly eight hours, someone pointed out a mangled bison tail hanging from a branch near the tracks. The train had collided at full speed with a wild bison, damaging the engine severely enough that it needed to be towed for repairs. For a long time, I assumed such incidents were unique to rural India, but I later discovered that wildlife and infrastructure conflicts are a global issue.
The Federal Aviation Administration (FAA) estimates that wildlife strikes have cost U.S. civil aviation approximately $272 million annually on average between 1990 and 2025. Similarly, in France, the rail operator SNCF reports that wandering animals interfere with over 8,000 trains each year, making animal strikes one of the top five causes of delays. In Brittany alone, 120 trains collided with animals in 2023, disrupting 660 services and causing over 15,000 minutes of delay.
Innovating Wildlife Deterrence: The Flox Intelligence Approach
These alarming statistics inspired Sara Nozkova, who has a background in transportation and autonomous vehicles, to develop a novel wildlife deterrent system. Unlike traditional methods—such as sirens, lights, fences, falcons, and culling—her system adapts deterrent responses based on the behavior and species of nearby animals.
“I was convinced the answer was not a louder deterrent,” Nozkova told TechCrunch. After 13 years of experience in transportation, she witnessed how quickly machines designed for controlled, engineered environments fail in nature’s complexity. “We have taught machines to be brilliant, but only inside the environments we engineered for them,” she said. “Cities, factory lines, warehouses. Fixed geometry, known objects. Stepping outside into nature, I saw firsthand how the same machines quickly go blind. Because nature is complex and always alive.”
Nozkova joined the AI lab at KTH Royal Institute of Technology in Stockholm and developed a system capable of dynamically adjusting its deterrent signals based on real-time animal behavior and species recognition. This innovation led to the creation of Flox Intelligence, which spun out of KTH in 2020.
How Flox Intelligence Works
Flox’s edge devices process data locally, similar to autonomous vehicles, but are specifically trained to interpret wildlife behavior. A motion sensor activates the device upon detecting movement, triggering a camera that uses on-device AI models to identify nearby animal species. The device then emits species-appropriate sounds to safely scare animals away without habituating them to repeated signals.
Nozkova emphasizes that the system learns from experience and varies its responses to maintain effectiveness. The company offers battery-operated devices that can be installed on poles, trees, and other structures. Alongside the hardware, Flox provides a software platform that tracks device deployment and wildlife activity, aiding clients in regulatory compliance and biodiversity monitoring. The long-term vision includes making the software portable for integration into vehicles such as trains, drones, and cars.
Currently, about 150 Flox devices are active across North America, Europe, and Australia, deployed in diverse environments including airport perimeters, road and rail corridors—especially at fence ends and wildlife crossings—national parks, agricultural lands, urban areas, mining sites, and critical infrastructure like electrical substations.
Business Model and Market Position
Flox sells its edge units upfront and charges an annual subscription for software and AI services after the first year. The company also maintains OEM partnerships, including with train manufacturer Alstom, licensing its software for vehicle integration. Nozkova projects that Flox will generate approximately $1.5 million in revenue by the end of 2026.
Though Flox’s approach is unique, it faces competition from companies like Robin Radar, whose Scarecrow system uses species-specific distress calls to deter birds, and iCHASE, which employs computer vision and moving lasers to detect and repel birds effectively.
Nozkova highlights Flox’s competitive edge as the system’s breadth and the extensive field data it has collected, which provides “ground truth on what actually makes a moose or a grizzly bear move safely.” She believes that “physical AI is walking out of the factory and into the messy, alive outdoors,” and that “every machine that operates outside—trains, trucks, drones, tractors, robots—will need to understand nature, and nature intelligence is the next layer of the perception stack. Almost nobody is building it.”
Funding and Future Plans
To date, Flox Intelligence has raised about $5 million through pre-seed and seed funding rounds, led by Unconventional Ventures and Tilia Impact Ventures, with co-investors including GENIUS NY, Norrsken, Satgana, and various angel investors. The company is currently opening a $10 million Series A round, aimed at scaling deployment from 150 devices to several thousand units and expanding the system’s applications beyond wildlife deterrence.
Through its innovative use of AI and edge computing, Flox Intelligence offers a promising solution to the global challenge of wildlife-infrastructure conflicts, with potential benefits for animal welfare, transportation safety, and infrastructure reliability.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
Source: Here
