When I was eight years old, I remember our train to Mumbai coming to a sudden, noisy stop. When we finally got moving again—a full eight hours later—someone spotted a mangled tail hanging from a branch near the tracks. Apparently, the train had struck a wild bison at full speed, and the collision had damaged the engine so severely that it needed to be towed for repairs.

For years, I assumed such incidents were unique to places like rural India. But a few years ago, I discovered that wildlife and infrastructure clash everywhere on Earth. The FAA estimates that wildlife strikes cost U.S. civil aviation roughly $272 million annually on average between 1990 and 2025. In France, the rail operator SNCF reports that wandering animals affect over 8,000 trains nationwide each year and rank among its top five causes of delays. In Brittany alone, 120 trains hit animals in 2023, disrupting 660 services and causing more than 15,000 minutes of delays.

Reading about all that destruction—to both infrastructure and wildlife—inspired Sara Nozkova (pictured above on the left) to create a wildlife-deterrent system that operates differently from the sirens, lights, fences, falcons, and culling traditionally used to keep animals away from roads, train tracks, airports, and other infrastructure. After 13 years working in transportation and autonomous vehicles, Nozkova had learned how quickly the systems we build to navigate roads and other controlled environments fail once nature enters the picture.

"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."

So she joined the AI lab at KTH Royal Institute of Technology in Stockholm and began developing a system that could alter its deterrent responses based on what a nearby animal is doing—and even what species it is. Flox builds edge devices that process data on-site rather than in the cloud. Like autonomous vehicles, they rely on perception systems to sense their surroundings, but theirs are trained on wildlife behavior. A motion sensor wakes the device when it detects movement, triggering a camera that uses on-device models to identify nearby animals. The device then emits a species-appropriate sound to scare them off. Nozkova says the system also learns from experience and varies its responses so animals don't become habituated to repeated signals.

The company currently sells battery-operated devices that can be mounted on poles, trees, and other structures. It also provides a software platform that shows clients where the devices are deployed and what's happening on the ground. The platform can generate reports for regulatory compliance and for purposes like biodiversity monitoring. The longer-term goal is to make the software portable enough to run on machines that work outdoors, including trains, cars, and drones.

Nozkova says about 150 Flox devices are live in North America, Europe, and Australia at airfields and airport perimeters, road and rail corridors (especially fence ends and crossings along wildlife corridors), national parks, agricultural land, urban areas, mining sites, and critical infrastructure such as electrical substations. The company sells edge units up front, then charges an annual software and AI subscription after the first year. Flox also has OEM partnerships with companies that pay an annual fee to deploy its software on vehicles, like trains. Train manufacturer Alstom is one such partner. The company expects to end 2026 with revenue of about $1.5 million, Nozkova said.

As unique as Flox's product and approach are, the startup is not without competition. Robin Radar's Scarecrow also uses species-specific distress calls to disperse birds and records the activity, while iCHASE sells a system that detects birds with computer vision and targets them with a moving laser, adjusting its response as needed. Nozkova says her moat is the breadth of the system and the data Flox has collected in the field, which she calls "ground truth on what actually makes a moose or a grizzly bear move safely."

"Physical AI is walking out of the factory and into the messy, alive outdoors," Nozkova says. "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."

The company has raised about $5 million across pre-seed and seed rounds so far, with the seed round led by Unconventional Ventures and Tilia Impact Ventures. Co-investors across the rounds include GENIUS NY, Norrsken, Satgana, and angel investors, Nozkova said. The company is now opening a $10 million Series A, which would mostly go toward growing from about 150 devices to several thousand deployed units and extending the underlying system beyond its current wildlife use cases.