For cities and municipal governments

Real-time aerial vision for a smart city

Your city already watches its streets from the ground. Hover adds the feeds it is missing, starting with the air, then adding sensors, and turns them into actionable intelligence with AI: a city that sees, understands, and responds in real time.

Signal flow
01 · Remote sources
Drone
Aerostat
Camera
Sensor
02

We bring the feeds from the field

Live · over cellular
03

We turn them into intelligence with AI

Sees · understands · responds
04 · Outcomes
Faster response
Informed decisions

In short: your city already watches its streets from the ground. Hover adds the feeds it is missing, starting with the air, then adding sensors, and turns them into actionable intelligence with AI: a city that sees, understands, and responds in real time.

But what about the incidents where there are no cameras?

A crash on a remote arterial
Construction in progress
Congestion just forming
An emergency far from any pole
01

Live aerial video, from scenes other cameras barely cover

Many cities have already gathered thousands of fixed cameras into one room. But what about the incidents where there are no cameras? A crash on a remote arterial, construction in progress, congestion just forming, an emergency far from any pole.

With Hover, drones and tethered aerostats stream live video from the field, over cellular networks and with no installation, even from places with no camera and poor signal. And it does not stop at the air: any remote source integrates alongside the cameras the city already operates, straight into the center it already has. The live video is watchable wherever it is needed: a phone, a tablet, or the operations room, and every agency in the sector can share one live source.

Not only video: low-cost sensor nodes, battery or solar powered with no civil works, install in minutes and report for years into the same live view.

02

Turning every source into actionable intelligence with AI

A smart city is not the one with the most cameras; it is the one that understands what is happening and responds in real time. The AI runs on every source and understands what it sees: it detects crashes, obstructions, and anomalies as they happen, counts and classifies vehicles, and picks up patterns the eye misses.

Video stops being screens to stare at and becomes actionable intelligence: alerts, priorities, and responses, in the moment they matter. Operators stop watching dozens of screens; the AI points them at what matters. Real-time urban analytics.

In practice

Scenario 01 A crash on an arterial.

A drone arrives before any unit and streams live to the center. Operators see the whole scene, size the response, and clear the road faster.

Scenario 02 Construction in progress.

Scheduled overflights document progress and measure the effect on traffic, with live video and a searchable record: public-investment oversight without deploying crews.

Scenario 03 A mass event or an emergency.

Drones and aerostats hold a continuous aerial view where there are no cameras, and the AI flags the moment something changes.

Scenario 04 The network's blind spots.

Where a camera is not justified, a low-cost node installed in minutes counts bikes, measures corridor flow, reads air quality, or spots dead streetlights: dozens of strategic points for a fraction of the cost of traditional infrastructure.

Results the city can show

Faster incident response.

The AI identifies crashes, obstructions, and anomalies as they occur, so the city acts sooner.

Management by data, not intuition.

A searchable event record over time: the basis for modern, measurable, defensible decisions.

A better-informed public.

The same intelligence feeds the channels and dashboards the city already uses, in real time.

FAQ

Does it replace the command center we already have?

No. Hover adds the feeds your operation is missing, starting with the air, and delivers them straight into the center you already run. It integrates; it does not replace.

What sources does it support?

Any remote source, on the ground or in the air: drones, tethered aerostats, fixed cameras, and sensor nodes, with no hardware vendor lock-in. For cameras, RTSP is enough.

Does the video hold up over cellular?

Yes. Streams ride LTE, Starlink, or mobile networks, where ordinary streaming drops, with no installation. On signal loss it retransmits and the feed recovers.

What does the AI detect?

What your team describes in plain language: it detects crashes, obstructions, and anomalies as they happen, counts and classifies vehicles, and picks up patterns the eye misses. Real-time urban analytics.

What about spots where a camera is not justified?

Low-cost sensor nodes, battery or solar powered with no civil works, install in minutes and report for years into the same live view: bike and flow counts, air quality, or dead streetlights.

Where does the video live?

Video streams live from the field straight to our platform on AWS in the United States. Data is stored and AI runs in US regions; feeds are private by default, under the city's control.

How do we start?

With a conversation and a bounded pilot on one corridor or one concrete case, alongside the systems the city already runs. Write to us and we will set it up.

Shall we add the feeds your city is missing?

Let's scope a bounded pilot, alongside the systems the city already runs.