Your stores already have cameras at the door, the cash wrap, and the stockroom. Very few of them warn anyone in time or turn what they record into a case police will take. Here's how one asset protection team does both. Shoplifting fell last year. Organized crews, repeat offenders, and fraud did not. 40% of retailers report organized retail crime, half see more repeat offenders, and most report fewer than half their thefts to police, often because one store's loss is too small to prosecute.
Meet Maya, director of asset protection for an apparel brand with 40 stores in six states. Her associates are trained never to chase a thief, so the cameras have to watch for them. One Saturday across every store, every store's cameras stream into one view, entrances, sales floors, the fitting room hallway, stockrooms, and parking lots. No new cameras. Any IP camera or the video system the stores already run connects. 10:12, in the lot at the Easton store, a plate is read and matched to a car linked to thefts at two other stores last month.
Maya's team gets a named alert before anyone walks in. At the door, the camera matches a person of interest on the company's watch list. The alert and a snapshot reach the regional lead and the store manager's phone. Nobody on the floor has to step in. 2:40 at Polaris, two people sweep a table of leggings into bags and run for the door. The robbery alert fires with the clip, and the manager calls police while associates stay back. The same cameras count every entrance, so district managers see where the afternoon rush is and staff up before the line grows.
11:50 that night, after close, the backdoor camera flags someone at the stockroom door, and the alert goes straight to the monitoring team. Monday morning, Maya builds the case. Every alert clip and plate read is kept in VIDIZMO Nexus, searchable by store, type, and time. Maya asks AI Intelligence Hub, "Where has this car or this group been seen in the last 90 days?" The answer lists 11 visits across five stores in three states, each linked to its clip, and points out the visits with the same move.
One person buys a small item while the others walk out. 11 small thefts become one case with a timeline, a total loss, and every clip in order. Together, they're large enough for a prosecutor to take. Before it goes to the detective, Redactor blurs the other shoppers, the associates, and the screen at the register. The original stays untouched. The detective gets an expiring link, and every view is logged. Then the footage teaches. A redacted clip becomes this week's store huddle, what a push-out looks like, and why associates step back, captioned with Spanish subtitles and every associate's completion tracked.
Setting it up takes two steps. Add a camera by its stream address and a name. Then build a rule visually, a detection, a zone drawn on the camera, and who gets the alert. It runs on the company's own servers, in its cloud, or at the edge. Maya's team sees crews coming, keeps associates out of harm's way, and turns small thefts into cases police act on. Start with a free AI readiness assessment. One store, two to four existing cameras. See it flag on your own footage.
