Case Study: Leading Ecommerce Retailer achieves smarter visual product recommendations with Quantiphi

Visual Search and Recommender Systems for E-commerce markets

Leading Ecommerce Retailer partnered with Quantiphi to improve product discovery and recommendations for fashion shoppers. The retailer needed a smarter way to suggest relevant products, support visual search from user-uploaded images, and deliver millisecond-level retrieval while handling noisy product labels, accurate apparel segmentation, and fast-changing fashion trends.

Quantiphi implemented a computer vision-based visual search and recommender system using deep learning, approximate nearest neighbor search, and models such as Faster R-CNN, ResNet, and VGG16. The solution enabled image-based search and retrieval of similar products from multiple retailer inventories, helping the client provide more natural product discovery and increase transaction conversion through smarter recommendations.