AIslens
Computer Vision Platform for In-Store Customer Analytics
Privacy-first computer vision platform analyzing in-store customer behavior — foot traffic patterns, dwell times, queue lengths, and conversion rates without storing personal data.
Increase in Conversion Rate
Stores Deployed
Labor Cost Optimization
Privacy Incidents
7 months
6 (2 CV Engineers + 2 Backend + 1 Frontend + 1 DevOps)
Retail
Web App
// About the Project
Project Overview
A retail chain with 150+ locations had no visibility into in-store customer behavior. They knew online conversion funnels intimately but the physical store was a black box. Decisions about store layout, staffing, and promotions were based on intuition rather than data.
UppLabs developed a computer vision analytics platform using existing security cameras (no new hardware) to track anonymized customer journeys through stores. Edge processing ensures no personally identifiable images leave the premises — only aggregated analytics data is transmitted to the cloud dashboard.
The platform generates heatmaps of store traffic, identifies bottlenecks, measures dwell time by department, monitors queue lengths in real-time, and correlates foot traffic with POS data to calculate true conversion rates. A/B testing of store layouts became possible for the first time.
// Product
What We Built
A retail chain with 150+ locations had no visibility into in-store customer behavior. They knew online conversion funnels intimately but the physical store was a black box. Decisions about store layout, staffing, and promotions were based on intuition rather than data.
UppLabs developed a computer vision analytics platform using existing security cameras (no new hardware) to track anonymized customer journeys through stores. Edge processing ensures no personally identifiable images leave the premises — only aggregated analytics data is transmitted to the cloud dashboard.
Services Provided
Technology Stack
// Challenges
Problems We Solved
Privacy by Design
GDPR and CCPA compliance required processing video entirely on-premise. No facial recognition, no personal data storage — only anonymous trajectory and count data.
Existing Camera Infrastructure
Had to work with 10+ different camera models, varying resolutions (720p-4K), angles, and lighting conditions across 150 stores without hardware upgrades.
Real-Time Processing at Edge
Each store processes 20+ camera feeds simultaneously on a single edge device. Required extreme model optimization for inference on limited compute.
Meaningful Business Metrics
Raw detection counts are useless. Converting anonymous blobs into actionable metrics (conversion rate, avg visit duration, department affinity) required sophisticated tracking logic.
// Solutions
How We Delivered
Edge-First Architecture
Custom YOLOv8 models quantized for NVIDIA Jetson edge devices. On-premise inference at 30 FPS across 20 cameras. Only anonymized metrics sent to cloud.
Multi-Camera Tracking
Re-identification using body shape and clothing features (not faces) to track anonymous journeys across camera zones. DeepSORT with custom appearance embeddings.
Automated Store Analytics
Real-time dashboard: heatmaps, queue alerts (notify staff when wait exceeds threshold), department conversion funnels, hourly traffic predictions for optimal staffing.
A/B Testing Framework
Compare store layouts, display placements, and signage effectiveness across locations. Statistical significance testing built in to prevent premature conclusions.
// Tech Stack
Technologies Used
// Services
What We Provided
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