Complementary product recommendation model
TITAN-LINK is a proprietary Deep Learning architecture that recommends, for each product, the most relevant complements in actual usage — even those that don't surface from common purchases alone. The model can also steer these recommendations according to the store's priorities: margin, trends, seasonality, or categories to push.
Your tools. Our relational layer. Your sales surfaces.
TITAN-LINK plugs directly into your existing stack. No migration, no rewrite. The catalog comes in. The right recommendations come out — everywhere your customer buys.
The right complement, at the right moment.
On the product page, the cart, the post-purchase email — drag the slider to compare, surface by surface.
Every model ships with its reasoning — and with your priorities.
Open the calculation and follow the model’s full path, signal by signal, through to the recommendation or score it serves you: nothing is produced without your being able to redo the arithmetic. Then tune the model to your store’s priorities and strategy — margin, seasonality, categories to push, value horizon — you decide what it puts forward.
A relational graph, customizable.
Product relational graph
TITAN-LINK builds a unified graph of several million connections between your products, from their attributes, their co-purchases, and their browsing trajectories. Not a table of rules. A network of affinities.
Fusion of 17 independent signals
Co-purchases, similarity, complementarity, gross margin, bestsellers, trend, upselling — fused into a single score per product-to-product pair. Each signal is weighted according to your business intent.
Cross-category inference
The model detects unexpected complements across distinct categories. Where your native modules see a boundary, TITAN-LINK sees an affinity.
Steerable business weighting
Each signal can be reinforced according to your goals — conversion, margin, discovery. Not a frozen algorithm. A commercial strategy executed across your entire catalog.