Plans, resources, company information, and legal documents.
A system that sees every event can surface the few that actually matter. We built that carefully, because the line between 'helpful' and 'surveillance' is real.
A warehouse produces a constant stream of events — scans, counts, adjustments, movements. A person can't watch all of it, and watching all of it wouldn't help anyway; almost everything is normal. The useful thing is to surface the handful of events that are genuinely unusual, so a manager can take a look.
What it does
Anomaly detection watches the stream and flags patterns that fall outside what's normal for your operation: an adjustment far larger than usual, activity at an odd hour, a receipt that doesn't match its purchase order, a location behaving unlike itself. It doesn't decide what the pattern means — it raises it for a human to interpret. The judgment stays with your team.
Tuned to be trusted, not ignored
An alert system is only useful if alerts are taken seriously, and they're only taken seriously if there aren't too many. We default to a conservative threshold that surfaces the genuinely notable and stays quiet otherwise. Every alert has a simple useful / not-useful control, and that feedback makes the system better at showing you the things your team actually acts on.
A deliberate stance on surveillance
We're wary of the framing "AI watches your warehouse." It isn't wrong, but it can curdle into something we don't want to build. So a few choices are deliberate: alerts go to operations leads, not to individual operators' records. The system never disciplines anyone — it surfaces a pattern, and a person decides what, if anything, to do. The defaults are conservative on purpose.
Used well, it catches the quiet problems — a mislabeled vendor shipment, a reporting bug inflating numbers, a process drifting out of spec — before they compound. That's the value: not catching people, but catching the unusual stuff before it costs you.