detection before reaction
Turn existing optical cameras into a passive drone detection system.
Vision-AI analyses camera feeds continuously and detects drones in the low-altitude airspace — including small and autonomous ones.
A smoke detector for aerial threats: always on, passive and the first to tell you something is there.
the challenge
The low-altitude airspace is a new security layer.
Small drones are accessible, autonomous and hard to detect with conventional security systems. They approach critical infrastructure, borders and government sites low and from a distance.
Covered by conventional radar and RF systems.
Small, slow, low-flying, often autonomous. No transponder, frequently no signal. No alert. No record. No evidence.
The site that finds out afterwards.
- Spy on your premises
- Paralyse production processes
- Analyse security structures
- Prepare sabotage
- Operate unnoticed
There are 125,000 sites to protect worldwide.
Hardened at ground level, open above it. A €600 quadcopter crosses a fence line in four seconds.
drone incidents
It stopped being hypothetical in September 2025.
Drone disruptions at European airports quadrupled between January 2024 and November 2025.
4–5 Aug 2026 · Leipzig/Halle, DEAn armed quadcopter spends four hours on the apron800 g PETN
4 Nov 2025 · Brussels & Liège, BETwo airports stop in one evening23 flights
Dec 2018 · Gatwick, UKThe one everybody still cites140,000 pax
Every one of these sites had cameras. Almost none had cameras looking up.
how it works
Detection pipeline.
An intelligent detection layer on top of optical surveillance infrastructure that is already deployed — not a replacement for the camera network.
What range can you expect?
Detection range is not a product number, it is the product of target size and optics. Pick a target and a camera to see the honest figure.
| Vision-AI | Radar | RF | |
|---|---|---|---|
| Non-cooperative drone, no transponder, no signal | Yes | Yes | No |
| Visual evidence for verification and record | Yes | Track only | Signal only |
| Uses the cameras already on site | Yes | No | No |
| Emission and licensing | Passive, none | Active emitter | Passive, none |
| Adverse weather, glare, darkness | Reduced, IR mitigates | Robust | Robust |
We do not win on range. We win on cost, deployability, evidence value and airspace coverage — and we complement radar, RF and EO/IR rather than replacing them.
explainer video
Vision-AI explained.
detection proof
Real detections from real camera footage.
Drones detected and marked by Vision-AI in the camera image.
the next step
A technical session, then one real location.
The recommended starting point is a technical discussion. The objective is to identify one real-world location where Vision-AI can demonstrate its value.
- Existing camera infrastructure
- Operational environment
- Security requirements
- Potential pilot location
- Integration requirements
- Evaluation criteria
- Deployment timeline
- Potential scalability