Our neural networks learn your catalog, your customers, and your sales cycles on their own. They aggregate and pass that information to Titane, which turns those insights into real actions.
Explore the architecture→Watch signals propagate in real time through our deep learning architectures. Each node is a compute unit processing customer behavioral data, with connections showing the gradient flow during training.
Five proprietary deep learning models. Trillions of GPU computations.
Personalized per-customer recommendations
TITAN-REC analyzes 47 signals per customer — purchase history, browsing behavior, complementary products, market trends, weather, seasonality — to generate individual recommendations across your entire catalog. The right products, for the right person, at the right time.
View the model →Complementary product recommendations
TITAN-LINK recommends, for each product, the most relevant complements in actual use — even those that don’t surface from co-purchases alone. The model steers these recommendations toward your priorities: margin, trends, seasonality, or categories to push.
View the model →Churn detection and personalized actions
TITAN-CORE detects, for each customer, whether they are Active, Spacing out, Slipping away, or Inactive. The model analyzes more than 20 signals to tailor actions to their real situation: build loyalty, re-engage, retain, or win back.
View the model →Sales prediction and inventory management
TITAN-HORIZON combines 28 signals per product — sales history, stock levels, supplier lead times, weather, public holidays, advertising budget, e-commerce events, market trends — to generate a complete replenishment calendar, week by week, SKU by SKU.
View the model →Trending product detection
TITAN-TREND continuously monitors market signals — TikTok, Instagram, Pinterest, Reddit, Google, competitor launches — and fuses them to detect the products gaining momentum. The exact SKUs, the precise colorways, two to six weeks before it shows up in your sales.
View the model →New-signup scoring and activation
TITAN-FIRST scores every new signup from day zero, using browsing, acquisition, and context signals, to trigger differentiated welcome journeys that accelerate the first purchase.
Customer trajectory and need anticipation
TITAN-ARC learns each customer’s own rhythm — the sequence of their purchases, their browsing, and their needs over time — to anticipate their next need: a consumable to reorder, a project step to complete. This long-range reading feeds every TITAN model.