Grocery Store Digital Twin for Wayfinding and Guest Services

The operational problem

Grocery is one of the hardest retail environments to model. Aisles are reorganized, end caps rotate, seasonal displays appear overnight, and out-of-stocks move products to different shelves. A wayfinding experience built on a static planogram is wrong almost immediately, and guest-service staff absorb the gap. Answering "where is the tahini" correctly requires a spatial model of the store that updates itself, and that model has to be built and queried inside the store — shoppers are on camera, and that footage cannot be shipped to a cloud analytics service.

What Enfuse built

• Camera-based perception in-aisle for shelf, fixture, and display recognition
• A spatial store model — aisles, bays, shelves, end caps — kept current from live observation
• Product-to-location mapping reconciled against the retailer's item master and planogram feed
• A guest-service assistant that answers location, availability, and substitution questions against the twin
• Kiosk and associate-handheld interfaces with turn-by-turn in-store directions
• Queue, dwell, and congestion signals surfaced to store leadership in real time
• Anonymization at capture — no faces, no identity, no biometric matching

Architecture

• See — in-aisle cameras stream to NVIDIA Jetson devices for local inference
• Understand — shelf, fixture, and display recognition at the edge on Jetson
• Locate — observations resolved into the store's spatial coordinate model
• Reason — the Dell GB10 on site hosts the twin and the guest-service assistant
• Act — wayfinding at kiosks and handhelds; congestion and gap alerts to staff

Hardware and software

• NVIDIA Jetson Orin (in-aisle edge inference)
• Dell GB10 on-site workstation (digital twin + assistant inference)
• Dell networking and store infrastructure
• TensorRT-optimized vision models, multi-object tracking, spatial indexing
• Integration with item master, planogram, and inventory systems

Deployment environment

On-premises in-store. Camera inference on Jetson, twin and assistant on the Dell GB10 in the back office. No video egress.

Results

• Wayfinding answers grounded in the store's current layout rather than a static directory
• Guest-service load shifted from associates to self-serve kiosk and handheld interactions
• Store leadership gets live congestion and shelf-gap signals from the same perception layer

Client engagement. Store-level figures are withheld at the customer's request; the video shows the deployed digital twin and wayfinding experience.