PRODUCT RECOMMENDER
Sequence-based Co-purchase pattern Pre-basket (browsing) Post-basket (cart-aware) Dynamic reweighting Non-generic
Start a shopping list and let the model suggest related products in real time.
Add a few items to your basket to see how the suggestions evolve. Use the search field to add any of the 3,600 catalog items, or simply tap a recommendation to add it directly to your basket.
Questions about how this could work with your own catalog? Email sales@paniax.com.
This page is a product recommender demo built on a catalogue of about 3,600 SKUs.
This example showcases several capabilities found in modern, production-grade commerce intelligence engines:
✅ Sequence-based learning – understands the customer journey across browsing and purchasing steps.
✅ Co-purchase pattern detection – identifies which products naturally belong together in real baskets.
✅ Context-aware recommendations – suggestions adapt to the user's real-time behavior and session context.
✅ Next Best Product logic – prioritizes the product most likely to increase relevance, engagement, or cart value.
✅ Pre-basket & post-basket intelligence – works both during product browsing and after items are added to the cart.
✅ Affinity-based ranking – uses SKU-level relationships instead of static category rules or manual merchandising.
✅ Dynamic reweighting – real-time recalculation as the basket evolves, improving prediction relevance on every interaction.
✅ Non-generic, data-driven behavior – learns directly from behavioral data, not preprogrammed if-X-then-Y logic.
A real customer deployment would train on your own behavioral data and be retrained periodically as products, seasons and buying patterns change.
Results are for demonstration purposes only.
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