Bridging the gap between words and visuals in product discovery — for search that understands what users see, say, and mean.
In e-commerce, customers rely on both product descriptions and images to make purchasing decisions. Traditional search systems often focus solely on text, missing the rich information conveyed by visuals. VisualText Search bridges this gap by integrating textual and visual data, enhancing the relevance and accuracy of search results.
• More accurate ranking: Combines the visual semantics of product appearance with descriptive language for better matches.
• Improves discovery: Helps users find relevant products even when they use limited or ambiguous text.
• Supports zero-shot search: Enables matching to unseen or rare product types by leveraging visual similarity.
• No extra training cost: Efficient retrieval thanks to precomputed multimodal embeddings and shared vector space.
VisualText Search has been benchmarked across large-scale product catalogs and public datasets. Results show:
↑ 9.1% improvement in purchase recall vs. text-only baselines
↑ 8.7% improvement in relevance accuracy (human-rated)
↑ +5% exclusive matches — items found only through multimodal search
These gains translate to better engagement, reduced bounce rates, and more satisfied users — especially in fashion, furniture, beauty, and other visual-heavy categories.
How VisualText Search creates real value
• Fashion: understands style, silhouette, and color beyond keywords
• Home Decor: matches based on texture, form, and visual theme
• Beauty: ranks products by packaging, branding, and finish
• Second-hand marketplaces: improves matching for listings with poor text quality
Ready to elevate your on-site search and navigation? Contact us today to learn more about our AI Search Audit Agent module and how it can benefit your business.
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