Swedish Startup Flox Intelligence Uses AI and Species-Specific Sounds to Deter Wildlife from Railways, Airports and Roads

A Swedish startup spun out of the KTH Royal Institute of Technology is using artificial intelligence to tackle a persistent infrastructure problem: wildlife collisions with trains, aircraft and road vehicles. Flox Intelligence, a finalist at this year’s TechCrunch Disrupt Startup Battlefield 200, has developed edge devices that identify animals in real time and emit species-specific sounds to drive them away from critical infrastructure.

The problem is global and costly. The US Federal Aviation Administration estimates that wildlife strikes have cost American civil aviation approximately $272 million per year on average between 1990 and 2025. In France, rail operator SNCF reports that wandering animals affect more than 8,000 trains annually, ranking among its five leading causes of delays. In the Brittany region alone, 120 trains hit animals in 2023, disrupting 660 services and causing over 15,000 minutes of delays.

Por que importa

Founder Sara Nozkova, who spent 13 years in transportation and autonomous vehicles, said she was motivated by the scale of the damage to both infrastructure and animals. Traditional deterrents — sirens, lights, fences, falcons and culling — often fail because animals become habituated to repeated signals.

“I was convinced the answer was not a louder deterrent,” Nozkova told TechCrunch.

Contexto

Datos clave
| Metric | Detail |
|——–|——–|
| Company founded | 2020, spun out of KTH Royal Institute of Technology, Stockholm |
| Devices deployed | ~150 across North America, Europe and Australia |
| 2026 projected revenue | $1.5 million |
| Total funding raised | ~$5 million (pre-seed and seed), opening $10 million Series A |

How the system works
Flox’s devices are battery-operated and can be mounted on poles, trees and other structures. They process data on-site rather than in the cloud, using perception models trained on wildlife behaviour. A motion sensor wakes the device when it detects movement, triggering a camera that uses on-device AI to identify the animal. The device then emits a species-appropriate sound — for example, a distress call specific to a moose or a grizzly bear — to scare the animal away.

Nozkova says the system learns from experience and varies its responses over time, preventing animals from becoming habituated to repeated signals. A software platform shows clients where devices are deployed and what is happening on the ground, and can generate reports for regulatory compliance and biodiversity monitoring.

Deployment sites and partnerships
Flox says about 150 of its devices are currently live at sites that include 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 upfront and charges an annual software and AI subscription after the first year. It also has OEM partnerships with companies that pay an annual fee to deploy its software on vehicles. Train manufacturer Alstom is one such partner.

The longer-term goal is to make the software portable enough to run on machines that work outdoors, including trains, cars and drones. “Physical AI is walking out of the factory and into the messy, alive outdoors,” Nozkova said. “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.”

Competition and differentiation
Flox is not alone in the wildlife-deterrent market. Robin Radar’s Scarecrow uses species-specific distress calls to disperse birds and records 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 Flox’s competitive advantage lies in the breadth of its system and the field data the company has collected, which she describes as “ground truth on what actually makes a moose or a grizzly bear move safely.” The company’s models are trained on wildlife behaviour across multiple species and environments, giving it a wider scope than bird-specific systems.

Funding and growth plans
Flox has raised about $5 million across pre-seed and seed rounds. The seed round was led by Unconventional Ventures and Tilia Impact Ventures, with co-investors including GENIUS NY, Norrsken, Satgana and angel investors. The company is now opening a $10 million Series A round, which it plans to use to grow from roughly 150 deployed devices to several thousand, and to extend the system beyond its current wildlife use cases.

Why this matters for India
India’s expanding railway network and growing number of airports face similar wildlife collision risks. Indian Railways operates one of the largest rail networks in the world, much of it passing through forested and wildlife-rich areas. Animal-vehicle collisions on roads are also a significant safety and ecological concern. While Flox has not announced deployments in India, its edge-based, low-power approach could be relevant for remote monitoring stations along railway corridors and near airport perimeters where cloud connectivity is limited. The company’s presence as a TechCrunch Disrupt finalist signals growing investor interest in AI solutions for infrastructure safety.

Source: TechCrunch, “Flox Intelligence is building AI to keep wildlife away from trains, airports, and roads” – https://techcrunch.com/2026/10/13/flox-intelligence-is-building-ai-to-keep-wildlife-away-from-trains-airports-and-roads/

Datos clave

Punto	Detalle
Fuente	TechCrunch AI
Fecha	2026-10-13T18:30:00+00:00
Tema	Flox Intelligence is building AI to keep wildlife away from trains, airports, and roads