Retail · United States
Haversack Retail lifts conversion 19% with AI product recommendations
A generic best-sellers rail was underperforming on a catalog of 40,000+ SKUs across a fragmented multi-brand marketplace.
19%
19% lift in conversion on recommended items
26%
26% increase in average order value
✓
Live across 40,000+ SKUs
Before
Existing architecture
Static merchandising rules maintained manually per category.
After
Proposed architecture
A vector-similarity recommendation engine blended with real-time behavioral signals, deployed as a drop-in storefront component.
Machine LearningVector DatabaseNext.js
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