AI shopping assistant for eCommerce procurement

This case study demonstrates how an AI-powered shopping assistant transformed traditional catalogue search into a conversational procurement experience. By interpreting natural language queries and mapping them to a 100,000+ SKU inventory, the solution reduced search friction, improved discovery accuracy, and accelerated inquiry workflows within a closed eCommerce platform.

The assistant combines AI-driven intent recognition with structured catalogue retrieval, delivering relevant recommendations while preserving strict access control across public and authorized environments. This is particularly important in educational procurement, where users search by intent rather than SKU and require controlled pricing visibility.

As a result of the implementation:

• The new AI-driven experience was well received by school staff, who actively explored the assistant to speed up and make product discovery more intuitive


• Inquiry creation became more streamlined, reducing friction between product selection and submission


• Search abandonment was reduced by guiding users through structured conversational recommendations


• Guided recommendations supported natural upsell opportunities by suggesting related and complementary items during the selection process


• User engagement session time increased, indicating stronger interaction with the platform


• The AI assistant was fully integrated into the live platform within 8 weeks