ViSenze

ViSenze powers visual search and product discovery for retailers, letting shoppers find products by image and AI-curated recommendation.

Overview

ViSenze brings visual AI to e-commerce discovery. Its visual search lets shoppers photograph or upload an image and find matching or similar products instantly, while its recommendation engine curates products based on visual attributes rather than just purchase history. Smart tagging automates product attribute extraction — color, pattern, style — at catalog scale, improving both search relevance and merchandising workflows. The platform integrates with major e-commerce stacks and is deployed by retailers and marketplaces across fashion, home, and beauty, where visual attributes drive purchase decisions. For merchants, ViSenze shortens the path from inspiration to purchase and turns image assets into structured data that improves every discovery surface. Its attribute extraction handles the long tail of catalog imagery quality variation, normalizing product data across suppliers so discovery experiences stay consistent even when inventory sources are heterogeneous. Its deployment model supports phased rollout, letting merchandisers validate conversion lift on priority categories before extending visual discovery across the full catalog and every customer touchpoint.

Key Features

• Image-based product search matching visual similarity
• AI-curated recommendations driven by visual attributes
• Automated product tagging and attribute extraction at catalog scale
• Integrations with major e-commerce platforms and marketplaces
• Deployments across fashion, home, and beauty retail
• Attribute normalization across heterogeneous supplier imagery

Pricing

ViSenze works through enterprise agreements priced by catalog size, traffic, and modules deployed. Evaluation periods and integration support are arranged with retail and marketplace technical teams.

Who Is It For

Retailers, marketplaces, and brands in visually driven categories that want discovery experiences matching how shoppers actually search — by image and style rather than text alone. It fits catalogs where text search systematically fails because shoppers cannot name the pattern, color, or silhouette they are looking for.