Ward connects to the cameras you already have. It shows you where time and output really go, and flags machine trouble before it becomes downtime. No new hardware. No facial recognition.
The cameras are already there. The footage just sits, unwatched, until someone goes looking for a reason things went wrong.
Video only gets reviewed once something has already gone wrong. By then the shift it could have saved is over.
A failing motor or a jammed line shows up in the output numbers hours after the machine first started showing it.
Without real throughput and idle data by station, every process change is an educated guess.
Active and idle time by station, units processed, and task-level breakdowns. Ward points at the specific bottleneck, not just the number next to it.
Ward sees unusual motion and visual patterns on equipment, early wear signals, and downtime risk. One feed, no added sensors. The camera already pointed at the line is enough.
Point Ward at any RTSP or ONVIF camera, existing or new. No proprietary hardware to install.
Edge inference identifies zones, poses, actions, and equipment states in real time. Raw video can stay on-site.
Patterns across shifts surface bottlenecks, idle time, and early machine anomalies.
Supervisors get specific, ranked flags. Nobody has to sit and review footage after the fact.
The worker app is how Ward earns trust. Each person sees their own shift, their own numbers, and exactly what the system sees about them. It is fair-workload visibility, not a leaderboard to punish.
Ward is built to be trusted by the people it sees. If a worker read this page, nothing here should surprise them.
Ward runs on managed, elastic infrastructure, so it scales from one camera to a whole plant without re-architecture.
We come to your plant, connect to a camera you already have, and show you what Ward sees on your actual floor. Not a slide deck.
A floor walk takes about an hour. No rip-and-replace. Bring one camera feed and we'll do the rest.