This is like upgrading your online store’s search bar so it understands shoppers the way a good salesperson does—by looking at both the words and the product pictures, not just matching text literally.
Traditional eCommerce search often shows irrelevant results because it relies on simple keyword matching and ignores product imagery and semantic meaning. This approach combines text and image understanding so customers can find what they actually meant, even with vague or imperfect queries.
Quality and coverage of the product catalog data (images + descriptions), plus tuned ranking logic and analytics around how your specific customers search and click—these become hard-to-copy assets over time.
Early Majority
Uses multimodal CLIP embeddings to align product images and text in the same space, then blends this with classical BM25 keyword search in Python, giving a practical, engineer-friendly recipe that outperforms pure keyword search without requiring full custom ML model training.
This is like giving your online store a smart digital stylist, photographer, and sales assistant that can instantly create product images, descriptions, and personalized messages for each shopper.
This is like giving your online merchandising team a super-smart assistant that constantly watches sales, inventory, and trends, then tells you what to stock, when to reorder, and how to price and present products for maximum profit across the whole product lifecycle.
Think of an AI shopping assistant as a smart, always-on store associate that lives inside your website or app. It chats with customers, understands what they want (even if they’re vague), recommends the right products, and can walk them all the way through to checkout.