Why we built QORA
Most physical–security operations in 2026 are still humans watching a wall of nine camera feeds, trying to notice the one tile where something is wrong. QORA puts AI in front of that monitor — so the operator only sees what matters, and walks away from every shift with an audit–grade incident log they didn’t have to type.
The dispatch latency problem
A construction site we worked with had four cameras, a 24/7 monitoring contract, and a perfectly normal 2026 setup — meaning: a human in another time zone, with thirty other sites also on their screen, watching feeds that almost never change.
When something did change — an unbadged person crossing the loading dock at 2am — the average time from camera capture to a phone ringing on the site lead’s nightstand was just under 14 minutes. Most of that was the operator scrolling, double-checking, escalating, and typing the address into the dispatch form.
The cameras did their job. The NVR did its job. The monitoring contract did its job. The system worked. It was just very, very slow at the only thing that mattered.
What we changed
QORA doesn’t replace the cameras. It doesn’t replace the NVR. It plugs in on top.
Edge inference runs against the existing ONVIF/RTSP streams (and Agent DVR, and iSpy — we’ll meet you where you are). What the model produces isn’t a Big Red Alert. It produces a short, structured incident record: where, when, who-or-what, confidence, and a one–sentence summary in plain English. That record is what the operator sees on their console — not raw video.
The operator’s job changes. They’re not watching feeds. They’re reviewing summaries. The grid of nine cameras becomes a queue of three or four incidents, in the order the model thought mattered. Most of them get dismissed in a second. A few get dispatched.
On the same construction site, after QORA: capture-to-dispatch dropped from 14 minutes to under 90 seconds. Same humans. Same cameras. Same NVR. Different layer of software on top.
Things we deliberately didn’t do
- We didn’t build cameras. The market has fifty companies making good cameras. Pick one. We’ll work with it.
- We didn’t auto-dispatch. The model proposes. The human approves. Auto-dispatching is how you get police rolling on a deer.
- We didn’t replace VMS. If the customer already runs Milestone or Genetec, QORA sits beside it, not in front of it. The VMS keeps doing recording, retention, and replay. QORA does triage.
- We didn’t pretend to be real-time. Edge inference + cloud queue means there’s a small lag between event and incident card. That’s OK. Real-time fantasy is how you get false confidence. Three–to–five seconds is fine for everything that isn’t an active shooter, and we’re honest about that.
What an incident card looks like
The operator console is intentionally boring. Black background, one queue, one incident card at a time. Each card has:
- A severity chip (Low / Medium / High), driven by location + time-of-day + class of detection.
- A thumbnail of the moment, not the whole clip.
- One sentence: “One person entered Dock B at 14:32 wearing a high–vis vest. No matching badge swipe in the last 60 minutes.”
- A recommended next step: “Verify with site lead before dispatch.”
- Three buttons: Dispatch, Dismiss, Open clip.
That’s the whole UI. Operators don’t want a dashboard. They want a queue.
Where QORA goes next
Today: construction, schools, warehouses, mobile deployments, multi-site SMBs. Tomorrow: tying into intercoms and access–control so the model can recommend a badge-out instead of just flagging it, with the human still in the chair. We’re also working on the compliance reporting surface — the “here’s what happened in October” PDF that today takes a security manager half a day to assemble.
QORA is in private beta. If you’ve got cameras, an operator who’s tired, and an incident–to–dispatch problem, we’d like to talk.