AI-Driven In-Store Operations Intelligence

/ Retail / Singapore / 2022-2023 /

Detection Accuracy: 97-99%
Faster Incident Response: 30-40%
Compliance Improvement: 20-30%

Client: WiredAve
Industry: Retail
Region: Singapore
Period: 2022-2023
Engagement: Dedicated Team
Team size: 80 MM

Services used

AI & Automation

Summary

The client needed greater visibility into in-store staff activities to ensure consistent service quality and effective operations. Limited insight into customer interactions and daily tasks made it difficult to measure engagement, optimize workflows, and maintain standards across stores.

The Challenge

No systematic way to monitor staff activities and customer interactions

Standards varied across stores without objective measurement

Managers relied on periodic walkthroughs rather than continuous data

Solution needed to work with existing CCTV infrastructure

The Solution

01 Staff activity classification

Face detection, pose estimation and activity classification run in real time, turning in-store behaviour into something that can be measured instead of observed on a walkthrough.

02 Edge AI video processing

Video streams are processed on-device at the edge, which is what keeps it fast and keeps the footage from having to leave the store.

03 Works with existing CCTV

People detection and tracking run against the CCTV systems already installed, so the client gets the analytics without a camera replacement programme.

04 Dashboard-ready metrics

Traffic metrics arrive ready for dashboards, which is what turns continuous observation into the objective measurement of standards the client wanted across stores.