Blur abstract background of people shopping in supermarket, products on shelves

META-aiviCase Study

Retail Stock Verification Using AR + AI


The customer is a supermarket chain with more than 1000 stores nationwide.


Ensuring Accuracy in Supermarket Shelf Display Organization

In retail, the meticulous organization of shelves plays a crucial role in enhancing the overall customer experience and streamlining operational processes. Recognizing the significance of precise shelf placement, our customer sought a solution that not only verified the correct location of each product but also ensured price accuracy on display tags.

The complexity of this task was heightened by the presence of various brands and multiple products from the same brand on the shelves. Additionally, the solution needed the capability to recognize any empty spaces that required restocking.


Misplaced Items, Stock Shortages, and New Staff Mistakes

The initial pricing inspection process required manual execution, limiting the ability to scan more than one item simultaneously. This, coupled with the possibility of items being incorrectly positioned, created challenges for new employees unfamiliar with product placement and pricing. Additionally, the supermarket chain aimed to consistently maintain fully stocked shelves to meet customer demands.


Smartphones and IP Cams for Real-time Stock Monitoring

Using META-aivi, an AI model was trained to swiftly recognize various brands and distinguish products within the same brand. This enabled the verification of accurate placement of products on shelves and correct prices, ensuring timely restocking and assisting new employees in dealing with customer inquiries.

In this specific instance, a smartphone was deployed for the inspection, yet META-aivi is versatile enough to integrate with IP cameras for continuous 24/7 product monitoring. This integration facilitates real-time notifications to prevent stockouts, ensuring shelves are consistently well-stocked.


5x faster than previous stock verification method
100% product-on-shelf detection accuracy
Reduced shelf stacking mistakes

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