Summary
Flox Intelligence, a startup spun out of Sweden’s KTH Royal Institute of Technology, is developing AI-powered systems designed to prevent dangerous encounters between wildlife and human infrastructure. Its technology identifies nearby animals and uses sounds tailored to their species and behaviour to encourage them to move away from hazardous areas. The company is targeting locations such as railway lines, airports, roads, agricultural land, and critical infrastructure. With around 150 devices reportedly deployed and a $10 million Series A funding round in preparation, Flox wants to expand its technology to more locations and outdoor machines.
What Is Flox Intelligence?
Wildlife encounters can create serious problems for transportation networks and the animals themselves. A large animal crossing a railway line can damage a train and disrupt services, while birds near aircraft can create safety risks. Animals entering roads can also cause accidents and threaten both drivers and wildlife.
Traditional wildlife-control methods include fences, warning lights, sirens, and other deterrents. However, these approaches do not always work in every situation. Animals may become accustomed to repeated sounds, while physical barriers can be difficult to install across large or environmentally sensitive areas.
Flox Intelligence wants to address this challenge using artificial intelligence. Founded by Sara Nozkova, the company develops systems that detect nearby wildlife and adapt their responses according to the animals they identify.
The startup emerged from research at KTH Royal Institute of Technology in Stockholm in 2020. Its approach focuses on helping machines understand wildlife behaviour rather than treating every animal encounter in the same way.
How Does Flox Intelligence’s AI Technology Work?
Flox uses compact computing devices that process information locally instead of sending every piece of data to the cloud. This approach, known as edge AI, allows the system to analyse its surroundings directly at the installation site.
The process involves several steps:
- Movement detection: A motion sensor detects activity near the device and activates the system.
- Animal identification: A camera captures images, and on-device AI models analyse the nearby animal.
- Targeted deterrence: The system produces a sound selected for the species and situation, with the aim of encouraging the animal to leave the dangerous area.
- Adaptive responses: According to the company, the system varies its deterrent signals based on experience to reduce the likelihood that animals will become accustomed to repeated sounds.
This differs from conventional deterrents that rely on a fixed sound or signal regardless of what is happening nearby.
For example, a system installed near a railway crossing could detect an animal approaching the tracks and respond before the animal enters a potentially dangerous area.
The effectiveness of such a response depends on factors including the species, environment, distance, and animal behaviour. The objective is to discourage animals from approaching hazardous infrastructure without relying solely on physical barriers or harmful methods.
Where Is Flox Intelligence’s Technology Being Used?
Flox Intelligence says its devices are being deployed across North America, Europe, and Australia. Its systems are designed for environments where wildlife activity can create safety, operational, or environmental concerns.
Potential and reported application areas include:
Airports and airfields: Wildlife near runways and airport boundaries can pose risks to aircraft. AI-assisted detection may help airport operators respond to animal activity more selectively.
Railway lines: Animals crossing tracks can cause collisions, delays, and damage to trains. Devices placed near crossings and gaps in fencing could help discourage wildlife from entering dangerous sections.
Roads and wildlife corridors: Roads frequently intersect with animal habitats. Targeted deterrents could help reduce encounters between vehicles and wildlife in selected locations.
Agricultural land: Farmers may use wildlife-management systems to discourage animals from entering sensitive areas or damaging crops.
Industrial and critical infrastructure: Mining sites, electrical substations, and other outdoor facilities can also experience wildlife-related operational challenges.
According to Nozkova, approximately 150 Flox devices are already operating across these types of environments. The company also provides a software platform that allows customers to monitor deployments, review activity, and generate reports for regulatory compliance and biodiversity monitoring.
Why AI-Based Wildlife Protection Matters
Wildlife management is not simply about preventing animals from entering human spaces. It also involves balancing public safety, reliable transportation, and the protection of animal populations.
Collisions can damage infrastructure, interrupt transport services, and injure or kill animals. Traditional prevention methods may be expensive to maintain or unsuitable for locations where animals need to move freely through established habitats.
AI-powered detection could help make these systems more responsive by identifying what is approaching and selecting an appropriate response.
Another potential advantage is local data processing. Since Flox’s devices analyse information on-site, they can reduce the need to send every detection to a remote server. This can be useful in remote areas where internet connectivity may be limited.
The company’s software also provides information about wildlife activity, which could help operators understand where encounters occur most frequently and where additional prevention measures may be needed.
However, AI-based deterrents are not a guaranteed solution to every wildlife problem. Their effectiveness needs to be assessed in real-world conditions, and they may need to work alongside fencing, habitat planning, and other established measures.
Flox Intelligence’s Business Model and Partnerships
Flox combines hardware sales with recurring software revenue.
Customers purchase the company’s edge devices initially and then pay an annual subscription for software and AI services after the first year. This model gives the company an opportunity to generate continuing revenue from systems already installed at customer locations.
Flox also works with original equipment manufacturers, or OEMs, that want to integrate its software into their products. Under these arrangements, partners pay an annual fee to deploy the technology on equipment such as trains.
Train manufacturer Alstom is one of the company’s reported partners. Such partnerships could allow Flox to extend its reach through established equipment manufacturers rather than depending entirely on direct sales of standalone devices.
The longer-term plan is to make the software adaptable enough to operate on more outdoor machines, including trains, cars, drones, and other vehicles. If successful, this could expand the technology beyond fixed installations and make wildlife awareness part of the systems that operate in the field.
Who Founded Flox Intelligence?
Sara Nozkova founded Flox Intelligence after spending 13 years working in transportation and autonomous vehicles.
Her experience helped highlight a limitation in existing automated systems: machines can perform well in controlled environments but struggle when they encounter unpredictable natural conditions.
Roads, factories, and warehouses are generally designed around known layouts and operating requirements. Wildlife, by contrast, behaves in ways that can change quickly depending on the surroundings.
Nozkova’s work at KTH Royal Institute of Technology helped shape the idea of a system that could recognise wildlife and adjust its response according to the situation.
Rather than relying on louder alarms or one-size-fits-all deterrents, the company is building technology intended to interpret animal activity and respond more selectively.
Flox Intelligence Faces Competition
Flox is entering a market where other companies are also using technology to manage wildlife near infrastructure.
Robin Radar offers bird-detection and dispersal solutions, including its Scarecrow system, which uses bird-related signals to discourage birds and records activity. Another company, iCHASE, markets a system that uses computer vision to detect birds and direct a moving laser toward them.
These approaches demonstrate the variety of technologies being developed to reduce wildlife-related risks.
Flox’s stated advantage is its focus on multiple species and environments, together with data gathered from real-world deployments. The company believes this information can help it understand which responses are effective for different animals.
Whether this provides a lasting competitive advantage will depend on the quality of its field data, the reliability of its AI models, customer results, and how effectively it expands its deployment network.
Flox Intelligence Plans a $10 Million Series A Round
Flox Intelligence has raised approximately $5 million through pre-seed and seed funding rounds, according to Nozkova. The seed round was led by Unconventional Ventures and Tilia Impact Ventures, with other investors including GENIUS NY, Norrsken, Satgana, and angel investors participating across the rounds.
The company is now opening a $10 million Series A funding round.
According to Nozkova, the planned funding would primarily support an increase in deployments, from around 150 devices to several thousand. The company also intends to extend its underlying technology to additional outdoor applications beyond its current wildlife-focused use cases.
Flox expects revenue of approximately $1.5 million by the end of 2026, based on the company’s projection. That figure is a forecast rather than confirmed full-year revenue.
The next phase will test whether Flox can scale its hardware deployments, build recurring software revenue, and demonstrate reliable performance across different operating environments.
What Comes Next for AI-Powered Wildlife Protection?
Flox Intelligence’s longer-term vision extends beyond placing wildlife-deterrent devices along railway lines and airport boundaries.
The company wants its software to work on machines that move through outdoor environments, including trains, cars, drones, tractors, and robots. Such systems could potentially identify wildlife in their surroundings and use that information to support safer decisions.
This concept reflects a broader direction in artificial intelligence: bringing AI out of digital applications and controlled industrial settings and into the physical world.
For Flox, the main challenges will include expanding deployments, validating effectiveness across different species and environments, and making its technology practical for infrastructure operators.
If the company succeeds, its systems could become another tool for reducing wildlife-related disruptions while helping protect animals from dangerous encounters with human activity.
Conclusion
Flox Intelligence is developing AI-powered wildlife deterrents that identify nearby animals and adapt their responses to help keep them away from hazardous infrastructure. Its edge devices, software platform, and OEM partnerships offer a different approach to wildlife management around railways, airports, roads, and industrial sites.
With around 150 devices reportedly deployed and plans to raise $10 million in Series A funding, the startup is aiming to expand its reach and bring its technology to more outdoor machines.
Its future success will depend on proving that the system works reliably at scale, but the approach highlights how AI could help address a practical challenge at the intersection of transportation, infrastructure, and wildlife protection.