E-Commerce Visual Search
Increasing product discovery by 34% with a highly contextual, attribute-rich visual search dataset.
The Challenge
A major e-commerce platform wanted to revolutionize their shopping experience by allowing users to search for products using their smartphone cameras. However, user-uploaded photos are notoriously blurry, poorly lit, and cluttered, making it difficult for standard models to accurately retrieve the correct product.
Our Solution
Dserve AI created a highly specialized dataset of 300,000 product images. We didn't just use clean studio shots; we actively sourced lifestyle images showing products in natural, cluttered contexts. Every item across 80+ categories was annotated with fine-grained attributes: color, texture, material, shape, and style tags.
The Impact
"The platform launched their visual search feature with an astonishing 99% retrieval accuracy. By accurately bridging the gap between messy user photos and clean catalog items, they increased user product discovery and engagement by 34% in the very first quarter."
Taxonomy & Ontology
Bridging the Reality Gap
01. Catalog Ingestion
Processing clean, high-resolution studio shots of the product inventory.
02. Wild Sourcing
Collecting user-generated, smartphone-quality lifestyle images of identical products.
03. Vector Alignment
Mapping the 'wild' images to the clean catalog representations using contrastive learning tags.
04. Attribute Extraction
Isolating specific features (e.g., 'v-neck', 'floral') to improve granular search.
"We were pleasantly surprised with Dserve AI's robust workflow management, quick turnaround time, their experience in AI data pipelines, and their network of expert annotators. Moreover, the quality that they offer is second to none."