We Built a Custom AI Surveillance System for a Waterfront Community — From the Network Up
At a gated lakefront community in Central Florida, we didn’t “install cameras.” We engineered a custom, high-end AI surveillance system — the analytics, the recording, and the entire network it rides on.
AI at every layer
Not a box of cameras and a DVR — a purpose-built AI stack we specified and integrated:
- AI license-plate recognition at the gate — reads and logs every plate in and out, with allow/block lists: residents vs. visitors, and a flagged vehicle triggers an instant alert.
- AI cameras across the grounds — on-board human/vehicle detection, so alerts fire for people and cars, not raccoons and rain; perimeter rules (tripwire, after-hours intrusion) that only trip on a person or vehicle.
- AI-driven recording — face detection and recognition against a known-faces database, plus AI forensic search: pull every person or vehicle at the gate in a given hour in seconds, instead of scrubbing 30 days of footage.
High-end analytics are only as good as the infrastructure beneath them. That’s the part we build.
The hard part: reaching the far edges
The buildings were easy. The reach was not — a marina the better part of a mile from central recording, with no cable path and no clean line of sight. A direct wireless shot was impossible: a tree line dead in the path (you’d need a 40–60 ft tower to clear it), a two-story house in the beam, and a wetland you can’t trench through.
So we designed a two-hop, point-to-point wireless relay off an existing 50-foot pole:
- Hop 1: ~1,028 m (0.64 mi)
- Hop 2: ~427 m, with paired radios at the pole
Real link-budget work — terrain profiles to prove Fresnel-zone clearance the whole way (the beam clears a mid-path rise by ~5 m), antenna bearings computed to the degree and corrected for magnetic declination, every mount GPS-surveyed before anyone climbed.
When the RF won’t hold, change the game
We engineered it right — and on install, the far path still wouldn’t stay clean enough to trust a community’s footage to, 24/7. The amateur move is to keep spending: taller tower, bigger dish. The professional move is to recognize a ~mile RF shot across variable terrain is a fragile foundation for continuous recording — and re-architect. So we made the marina an island:
- A cellular uplink — and carrier provisioning was its own gauntlet: device-compatibility rejections, hotspot throttling, and an undocumented setup before we had a stable link.
- A compact on-site recorder capturing every marina camera locally to a surveillance-grade drive — roughly two weeks of continuous full-resolution footage, never touching the cellular link.
- The elegant part: cellular hands you no public IP (carrier-grade NAT), so nothing can connect in. We built a secure tunnel back to the community’s central recorder — so the marina cameras appear in the same app as every other camera on the property. No public IP, no port forwarding, no open ports.
And the cameras that kept dropping
On the property’s other wireless links, cameras kept falling off the recorder. The easy assumption was power. We measured instead — a latency sweep put one link at 35% packet loss and over a second of latency, while wired cameras sat at 0.2 ms. Root cause: two co-located wireless bridges had auto-selected the same channel and were jamming each other. A channel plan, not new hardware. You only find that in the data.
How we pull it off
AI analytics, RF link budgets, cellular fallbacks, carrier-NAT tunnels — specified, built, and driven remotely from our HQ 40 miles away, over a zero-trust mesh, orchestrated through BRAVO, our own AI operations platform, directed by our engineers. Most managed-IT shops sub this out. We build it.
