See the drone before it reaches you.
DroneWatch AI is a software intelligence layer that fuses radio-frequency, acoustic, telemetry, live air-traffic, live Remote ID and human-report signals, applies AI correlation, and raises an explainable alert only when independent sources agree. It makes existing sensors smarter, without new hardware.
Technical documentation is available to authorised parties once the required export authorisations are in place.
The problem
Commercial drones now threaten airports, critical infrastructure, public events and borders. Traditional counter-drone detection relies on expensive, specialised hardware, so wide-area, layered coverage is hard to afford and hard to scale.
Single sensors are noisy. RF alone, acoustics alone or radar alone each generate false alarms, and operators are flooded with unfused, unexplained detections they cannot act on with confidence. Fusion is the answer.
Capabilities
Several independent sources
Several independent signal sources in one explainable picture.
AI correlation
Explainable assessments in plain operator language.
Live tactical map
Real air traffic from a live ADS-B feed, plotted in real time around the protected site, plus real drones that identify themselves over network Remote ID, worldwide, shown as context and never as a threat.
False-alarm discipline
Alerts are explainable and raised on agreement between independent sources.
Integration
Two public feeds are live today: ADS-B air traffic, and network Remote ID shown as context. Integration with existing operator systems would be defined in a pilot.
Defensive
Detection and early warning only.
Integration with existing systems
Two public feeds are live today: ADS-B air traffic and network Remote ID (drones that identify themselves, context only). Integration with existing operator systems would be defined in a pilot.
Where this applies
A small drone creates the same problem for a forward site and for a regional airport: single sensors are noisy, and an operator flooded with unfused alerts cannot act on any of them with confidence. The fusion approach is the same in both settings. So is the stage: everything below is where this would apply, not where it is deployed.
| Setting | What the fusion layer would address |
|---|---|
| Defence and national security | Layered early warning across sites and borders, where wide-area coverage from dedicated hardware alone is hard to afford |
| Airports and airfields | Separating registered traffic on ADS-B and Remote ID from an unregistered contact near an approach path |
| Energy, water and industrial sites | Long perimeters and dispersed assets, where one radar per site does not scale |
| Ports and offshore energy platforms | Large open areas with constant legitimate movement, so the false-alarm rate decides whether an alert is trusted at all. Maritime sensing is on the roadmap, not built |
| Stadiums and public events | Temporary coverage at short notice, assembled from sensors already on site |
| Prisons and secure facilities | Small, low, short-duration flights that any single sensor class routinely misses |
Privacy, stated up front. Civil sites carry data-protection obligations a military perimeter does not, because RF and acoustic sensing happens around the public. Two things are true of the demonstrator today. It works on processed detection events, not recordings or raw captures, and the only positional data it handles is public ADS-B aircraft telemetry and the public network Remote ID positions of drones that identify themselves. And it has not been assessed against GDPR for any real deployment, because there has not been one. What a real installation must do depends on the sensors a partner brings, and that assessment belongs in the pilot, before anything is switched on.
Export control. DroneWatch AI is controlled as military software, category ML21 of the EU Common Military List, in every configuration, including civil settings. It is not supplied, licensed, transferred or made accessible outside Devaland without the authorisations that regime requires, obtained first.
For partners
We bring the software intelligence layer, the AI correlation, the operator console and the integration engineering. A partner brings real sensors, an accreditation path and a customer channel. EU Defence Innovation (EUDIS) and European Defence Fund calls fund exactly this kind of defensive software layer.
First step: a pilot at one real site with one or two real sensor types plus the live feeds, to measure the real-world false-alarm rate. Built by a former Army Engineer (Military Technical Academy, Bucharest) who deploys and operates production AI systems on his own infrastructure.
Project stages
Where the project actually is, and what each step requires. Phases 1 and 1.5 are built and running. Everything beyond them needs a partner, real sensors, and a site.
25 September: network Remote ID added as a second live public feed, context only.
| Stage | Status | What it covers |
|---|---|---|
| Phase 1 Prototype | BUILT | Prototype: simulated signals plus two live public feeds (ADS-B and network Remote ID, context only). |
| Phase 1.5 Review tooling | BUILT | Review tooling. No false-alarm rate is claimed. |
| Phase 2 Real sensors | Needs a partner | Real sensors: needs a partner and a site. |
| Phase 3 to 5 | Planned | Planned, long term, dependent on a partner. |
| Extension Maritime | Concept only | Maritime: concept only, needs a partner, nothing built. |
DroneWatch AI was entered in the EUDIS Defence Hackathon Spring 2026 in Romania (see the entry), and has since been built into this working demonstrator.
