Flox, a Madrid-based startup founded in 2024, has closed a $1.5 million (≈€1.395 million) funding round led by Kibo Ventures to scale its artificial intelligence platform for detecting and managing wildlife in airport environments. The company announced the round on October 13, 2026, after twelve months of pilot operations at Barajas Airport and two regional airports.
- Funding: Flox raised $1.5M led by Kibo Ventures to expand its AI wildlife detection system.
- Technology: The platform processes feeds from over 1,200 cameras with an 800-millisecond detection latency, using thermal and optical cameras plus weather data.
- Market Need: Wildlife collisions caused 263 incidents at European airports in 2025, with global costs exceeding €800 million annually.
A Predictive Approach to a Costly Problem
The problem is real and expensive. Aircraft-wildlife collisions—primarily birds, but also medium-sized mammals on runways—caused 263 declared incidents at European airports in 2025. Repair costs and delays are estimated at over €800 million annually worldwide. Existing wildlife management combines human patrols, acoustic deterrents, and bird-tracking radar, but these offer partial coverage and reactive responses. Flox instead uses a predictive approach based on computer vision and animal behavior models.
Real-Time Behavior Classification: The Core Innovation
The key technical difference is real-time behavior classification. Detecting a bird in the maneuver area is not enough; risk depends on whether it is landing, overflying, flocking, or perching on the runway. Current radar systems detect mass and speed but cannot classify species or predict short-term trajectories. Flox’s model fuses three real-time data sources: thermal cameras for detection in any light and fog (critical for northern European airports), conventional optical cameras for species classification and behavior analysis, and local weather data to correlate wildlife activity with environmental conditions—starlings flock more frequently under certain temperature and wind windows. The AI was trained on 4.7 million annotated images of 87 common bird species at European and American airports, plus a category for ‘mammals in the maneuver area’ covering foxes, rabbits, and wild boars. Species classification accuracy in validation tests reaches 91.3% for the 20 highest-risk species at European airports.
SaaS Business Model and Expansion Plans
Flox operates a hybrid business model. Detection and analysis software is offered as SaaS priced per active camera per month; the company does not require its own hardware and can integrate with existing camera networks at most modern airports. For airports with older infrastructure or areas lacking camera coverage, Flox offers a package of its own thermal cameras on a rental basis, with the cost included in the monthly subscription. Prices are not public, but industry sources estimate typical contracts between €15,000 and €80,000 annually depending on airport size and number of integrated cameras. With 263 annual incidents in Europe and an average cost per incident of €50,000 to several million euros, the economic case for prevention is straightforward.
The $1.5 million round is not enough to scale internationally without further funding. Flox confirms it is preparing a $5 million (≈€4.65 million) Series A for the first quarter of 2027, aiming to reach 50 active airports by the end of that year. To achieve operational profitability in 2028, the company estimates it needs 30 to 40 mid-scale contracts. The processing infrastructure is compute-intensive for real-time vision; Flox processes data mostly in the cloud with analysis latency under one second, raising relevant questions about the energy footprint of AI scaling.
Regulatory Tailwinds Accelerate Adoption
The European Union Aviation Safety Agency (EASA) published an update to its airport wildlife management guidelines in March 2026 that, for the first time, explicitly mentions computer vision systems as recommended active monitoring technology. Although not mandatory, the directive sets a regulatory precedent that several airport operators are using to justify budgets for such solutions. The timing is no coincidence: Flox was one of the companies that submitted technical data to the EASA public consultation preceding the guideline update. Regulatory lobbying by a seed-stage startup is unusual, but Flox did so deliberately to create a normative environment that accelerates its market.
Wildlife management at airports rarely makes tech headlines, but it is a real safety issue affecting hundreds of millions of passengers annually. A two-year-old Spanish startup tackling it with well-trained AI and a sensible business model is exactly the kind of application that makes this sector worth watching. As AI’s energy dilemma continues to shape infrastructure decisions, solutions like Flox will also need to account for their own compute footprint while scaling.