AI Catalog Search for Marketplaces
Deploy AI-powered catalog search for marketplaces. Semantic understanding matches buyer intent to seller listings across inconsistent multi-vendor catalogs.
Online marketplace operators, B2B procurement platforms, and multi-vendor catalog aggregators managing 1M+ listings from thousands of sellers with inconsistent product data
Marketplace catalogs suffer from wildly inconsistent product data across sellers. The same product is described differently by different vendors, and buyers cannot find what they need despite it being listed. Keyword search fails because sellers and buyers use different vocabulary, and product images are the most reliable signal but are not searchable.
Ready to implement?
Why Mixpeek
Multimodal search bridges the vocabulary gap between sellers and buyers by understanding product content rather than matching keywords. Taxonomy enrichment normalizes the long tail of seller categorizations. Handles the data quality challenges inherent in multi-vendor marketplaces.
Overview
AI catalog search for marketplaces solves the fundamental discovery problem of multi-vendor platforms: buyer intent rarely matches seller vocabulary. By encoding listings multimodally and searching semantically, buyers find what they need regardless of how sellers described it.
Challenges This Solves
Seller Data Inconsistency
Thousands of sellers describe similar products with different titles, attributes, and categorizations, creating a fragmented catalog experience
Impact: Buyers see incomplete or duplicate results, and equivalent products from different sellers appear under different search terms
Vocabulary Mismatch at Scale
Buyers search using consumer language while sellers list using industry terminology, brand-specific naming, and SKU codes
Impact: 25-35% of buyer searches return suboptimal results due to terminology mismatch
Category Normalization
Each seller maps products to categories differently, and many use free-text categories rather than the marketplace taxonomy
Impact: Category browse experiences show incomplete results and buyers lose trust in filtered navigation
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
taxonomy-enrich
Expected Outcomes
+40% over keyword baseline
Search relevance (NDCG@10)
75% reduction
Zero-result search rate
+20% from improved product discovery
Buyer conversion rate
Upgrade Your Marketplace Search
Clone the catalog search pipeline and connect your multi-vendor product database.
Frequently Asked Questions
Related Use Cases
Visual Product Search for E-commerce
Let shoppers find products by uploading a photo instead of typing keywords
Visual Search for Retail
Bridge the gap between in-store inspiration and online purchase
Fashion Visual Product Discovery
Search for fashion by style, not just by name or brand
Ready to Implement This Use Case?
Our team can help you get started with AI Catalog Search for Marketplaces in your organization.