Smarter Shopping Assistants: Retrieval-Augmented AI and AR for Retail

Recent retail market studies report that companies using personalized user assistants generate up to 40% higher revenue. Meanwhile, according to a Forbes survey, 61% of users preferred shopping at retailers that provide AR experiences.

In practice, these two technologies are most effective when combined: RAG (retrieval-augmented generation) grounds chat responses in product facts (specs, reviews and policies); AR and visual search provide in-context previews (fit, scale, etc.), letting shoppers see items in real settings before they buy. 

Together they close two costly gaps in online shopping: doubt (will this fit or work for me?) and discovery (how do I find the right item fast?).

We’ve prepared a short guide that shows how we turn AI shopping assistant capabilities into measurable outcomes. Inside you’ll find:

• how RAG and AR work in practice;
• AI solutions for e-commerce & their features;
• pilot metrics and expected business lift;
• RAG assistant architecture and implementation.

Download the PDF whitepaper to see the pilot plan, product architecture and the exact metrics we saw in pilots.