About Trinocular AI | Trinocular AI

The problem is attention, not footage

Almost every site that calls us already has cameras, and most already record. What no site has is enough people to watch every feed at the moment something matters. The gap between an incident starting and a person knowing about it is where the damage happens, and closing that gap is the entire point of the product.

Built around existing estates

Trinocular AI adds an understanding layer on top of compatible existing CCTV rather than requiring its own hardware. That constraint shapes everything: the models have to work with the angles and resolutions real sites have, not the ones a demo would choose.

Deployed per site, not per product

Every detection is a configured model and rule set. Thresholds, zones, schedules and alert recipients are set for the site they run on and tuned against its own footage before going live, because an alert volume a team cannot action is the fastest way to have a system switched off.

What we hold to

Work with what is already installed

Replacing a camera estate is the most expensive way to add intelligence to it, and usually the least necessary. Trinocular AI is built to run on compatible existing CCTV because that is what sites actually have.

Say what a camera cannot do

A model that reports confidently and wrongly is worse than no model. Where an angle, a resolution or a lighting condition cannot support a detection, we say so before deployment rather than after the first missed event.

An alert that reaches nobody is not an alert

Detection is the easy half. The work that matters is routing the right event to the right person fast enough to act on, with the evidence attached.

Capability is not permission

Some of what vision models can do should not be switched on casually. Identity matching in particular requires lawful basis, notice and oversight, and we treat those as deployment prerequisites rather than paperwork.

Cameras Record. We Make Them Understand.

How we work