Multimodal Product Understanding
Multimodal Product Understanding is the use of unified representations of products, queries, and users—across text, images, and structured attributes—to power core ecommerce functions like search, ads targeting, recommendations, and catalog management. Instead of treating titles, images, and attributes as separate signals, these systems learn a single semantic representation that captures product meaning and user intent, even when data is noisy, incomplete, or inconsistent. This application area matters because ecommerce performance is tightly coupled to how well a platform understands both products and user intent. Better representations lead directly to more relevant search results, higher-quality recommendations, more accurate product matching and de-duplication, and more precise ad targeting. The result is higher click-through and conversion rates, improved catalog health, and increased monetization from search and display inventory, all while reducing the manual effort required to clean and standardize product data.
The Problem
“Your catalog is noisy—so search, ads, and recs can’t understand products or intent”
Organizations face these key challenges:
Search relevance relies on brittle keyword matching; synonyms and long-tail queries underperform (e.g., “running trainers” vs “athletic sneakers”).
Duplicate and near-duplicate SKUs proliferate (same product, different titles/images), inflating catalog size and fragmenting reviews, inventory, and ranking signals.