Store & Workforce Vision

Turn camera events into reviewable context.

A privacy-aware store operations prototype for occupancy, queues, and zone events, with configurable alerts and manager review.

Explore the build

Computer vision AI solutions Business systems Data & dashboards

Reviewed synthetic cafe occupancy scene with stylised blurred avatars, presented with illustrative event-review controls. — Project simulator scene with illustrative controls · Synthetic demo data.View larger

At a glance

Stage: Prototype
Industry: Retail and cafe operations
Context: Internal product
Capabilities: 5
Technologies: 10
Year: 2026

The challenge

A store can have several cameras and still lack a useful view of its operations.

Managers cannot watch every feed continuously, and hours of footage do not immediately explain a queue, an overcrowded area or a zone that needs attention.

The workflow also needs restraint. Automated activity alerts should help a manager investigate an event, with privacy controls and context before any conclusion about a person.

What we built

Store & Workforce Vision connects edge camera analysis to a bilingual operations dashboard.

Events instead of endless viewing

Configurable occupancy, queue, line and zone rules produce reviewable events. Phone-use logic includes device and exemption zones to account for approved work devices and break areas

Events instead of endless viewing

Configurable occupancy, queue, line and zone rules produce reviewable events. Phone-use logic includes device and exemption zones to account for approved work devices and break areas

An alert workflow

Managers can search events, inspect the associated evidence and manage alerts rather than relying on a continuous feed

Operational reporting

Aggregate analytics show movement, occupancy and queue patterns, with reports and optional summaries built from structured metrics

Camera and rule administration

Teams configure cameras, privacy masks, zones, schedules and thresholds from the web application

Local resilience

The edge component evaluates rules near the cameras and buffers delivery through a durable offline outbox

How it works

Privacy masks before analysis
Privacy masks are applied before analysis and storage. Stored event images and clips blur heads and faces; the system does not use face recognition. Aggregate workforce reporting is the default, with individual views restricted by policy and permission.
Gated summaries and blurred media
Optional language-model summaries receive structured events or aggregate metrics through a gate that rejects images and binary media. The intended cloud workflow receives events, metrics and blurred event media; live viewing is separately permissioned and audited.

Current stage

Prototype

Research
Prototype
Pilot
Production

This is a substantial internal engineering prototype, exercised with a synthetic cafe simulator and local substitutes. The gallery shows rendered simulator scenes with stylised avatars, not real CCTV incidents or real people.

Read the full status

Real cameras, production cloud services and pilot-trained models have not been validated in the documented runs. Alert latency, real-footage accuracy and edge GPU sizing require a controlled pilot before production commitments. Managers retain responsibility for reviewing alerts and interpreting the context.

Where this fits

Retail and hospitality operators can turn relevant camera events into a review workflow and aggregate operational insight. Explore our computer vision, analytics dashboards and retail solutions, or discuss your camera workflow.

discuss your camera workflow

Inside the project

Reviewed scenes from the project's synthetic cafe simulator.

Synthetic cafe simulator: phone-use rule example. This is rendered demo imagery, not real CCTV footage.View larger
Synthetic cafe simulator: a busy occupancy scenario. No real people or customer footage.View larger
Synthetic cafe simulator: work-zone rule example. Alerts remain subject to manager review.View larger
Synthetic cafe simulator: restricted-zone rule example, not a real incident.View larger