Visual Search for Retail
Deploy visual search for retail stores and mobile apps. Shoppers snap photos of products in-store or from ads to find and purchase them instantly online.
Omnichannel retailers, retail apps, and brands with physical and digital presence looking to connect in-store browsing with online purchasing
Shoppers encounter products in physical stores, magazines, or on the street but have no efficient path to purchase. They forget product names, cannot find them online, or settle for substitutes. Retailers lose sales from these high-intent moments because the journey from physical discovery to digital purchase is broken.
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Why Mixpeek
Embeddings are trained on real-world product photos, not just studio shots, making matches robust to the imperfect conditions of in-store photography. Style-aware retrieval goes beyond exact matches to surface similar alternatives when the exact product is unavailable.
Overview
Visual search for retail connects the physical and digital shopping experience. When a shopper photographs a product in-store, from a catalog, or on someone else, the retailer's app instantly identifies the product and presents it for purchase with full availability and pricing information.
Challenges This Solves
Physical-to-Digital Gap
Shoppers discover products in physical environments but have no seamless path to find and purchase them through digital channels
Impact: Retailers lose an estimated 20-30% of high-intent purchase opportunities at the discovery-to-purchase transition
Real-World Photo Quality
Consumer photos taken in-store have poor lighting, oblique angles, background clutter, and partial product visibility
Impact: Standard visual search trained on studio product photography fails on 40-50% of real-world query images
Style Alternative Discovery
When the exact photographed product is not available, shoppers need visually and stylistically similar alternatives
Impact: Binary exact-match search returns nothing for out-of-stock or non-carried items, ending the shopping journey
Recipe Composition
This use case is composed of the following recipes, connected as a pipeline.
Feature Extractors Used
multimodal extractor
text extractor
Retriever Stages Used
feature-search
attribute-filter
rerank
Rerank documents using cross-encoder models for accurate relevance
Expected Outcomes
+35% higher than text search
In-app visual search conversion
90%+ on real-world photos
Product identification accuracy
Measurable physical-to-digital conversion path
Cross-channel revenue attribution
Launch Visual Search for Your Retail App
Clone the retail visual search pipeline and connect your product catalog and mobile app.
Frequently Asked Questions
Related Use Cases
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AI Catalog Search for Marketplaces
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Ready to Implement This Use Case?
Our team can help you get started with Visual Search for Retail in your organization.