RETAIL
Store Safety & Validation
Store Safety & Validation. What it takes to run it.
Detect spills, blocked exits, and unauthorized access.
// 01 · The question
“Is there a spill, a blocked exit or something in the wrong place?”
// 02 · Why it is hard
Spills are transparent and take the colour of the floor beneath them. This is the class that is genuinely hard to see, and it is the one with the direct liability attached.
// 03 · What the labels have to be
Bounding box
Decided before the first frame
These are presence-in-a-region questions against zones defined once. A blocked exit is an object inside a marked area, and the alert does not become more actionable if the object has an outline.
This is the decision that is expensive to reverse. The geometry has to match what the answer contains, and finding out it does not means labelling the set a second time.
// 04 · How we run it
Three parts of one loop, on this job.
LexAnnotate
Shopper Tracking
Annotate in-store video for customer detection, employee identification, door events, and product placement. Distinguish between customers, staff (by uniform), and children within each camera's region of interest.
LexInsight
Conversion Analytics
Turn raw foot traffic data into conversion intelligence. Link visual detections to store zones, time slots, and operational events. Surface patterns that static dashboards miss.
LexAlert
Store Alerts
Monitor detection accuracy across store locations. Detect when camera repositioning, seasonal displays, or lighting changes degrade model performance. Route the failures back for retraining.
// 05 · What breaks it after launch
Shop floors are cluttered by design. Pallets, displays and shoppers block the view of the exact floor area being monitored, so the model reports clear on a zone it could not actually see.
It is not the only one that can get this use case, it is the one that usually gets it first. All ten conditions.
// 06 · In retail
Store Intelligence
The Challenge
The models lacked high-quality labeled data, activity alerts came back false, and unmonitored registers left customers waiting at checkout.
Our Execution
Labeled customer and staff positions. Validated alerts and traffic counts for accurate footfall tracking.
The Result
Faster AI training with accurate labeling. Smarter alerts for open doors and empty registers.
4M+
Annotations & validations