Visual AI vs traditional keyword and category-based recommendations. Which approach wins for your store?
| Aspect | sImage Visual AI | Related Products | Winner |
|---|---|---|---|
| Recommendation Method | AI analyzes actual product images (CLIP embeddings) | Based on keywords, categories, or sales data | sImage |
| Accuracy for Visual Products | ✓ 768-dimensional vectors capture colors, patterns, styles | ✗ Limited to exact text/category matches | sImage |
| Best For | Fashion, decor, art, jewelry (visual-first products) | General retail with defined categories | Tie |
| Setup Time | 5 minutes with theme editor block | 10-15 minutes configuration | sImage |
| Cross-Category Discovery | ✓ Finds products anywhere in catalog | ✗ Limited to same category | sImage |
| Style & Color Matching | ✓ Understands aesthetics and palettes | ✗ Requires manual styling rules | sImage |
| Starting Price | $0/mo (100 products) | Varies ($10-30/mo typical) | sImage |
| Requires Product Images | Yes - uses CLIP model | No - uses metadata only | Tie |
| Learning Curve | Automatic - AI improves with catalog | Manual - requires configuration | sImage |
| Manual Overrides | ✓ Per-product control | Varies by app | Tie |