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TITAN-REC 3.0
TITAN-HORIZON 2.5
TITAN-CORE 2.4
TITAN-TREND 1.7
TITAN-LINK 2.2
TITAN-FIRSTSOON
TITAN-ARCSOON
FR/EN
TITAN-REC 3.0
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The model

TITAN-REC 3.0

Personalized per-customer product recommendation model

TITAN-REC is a proprietary Deep Learning architecture that analyzes 47 signals per customer — purchase history, browsing behavior, complementary products, market trends, weather, seasonality — to generate individual recommendations across your entire catalog. Not generic best-sellers. The right products, for the right person, at the right time.

Flow architecture

Your tools. Our intelligence layer. Your actions.

TITAN-REC plugs directly into your existing stack. No migration, no rewrite. Signals come in. Recommendations come out.

E-commerce
PrestaShop
Shopify
WooCommerce
Magento
Emailing & CRM
Mailchimp
Klaviyo
Brevo
Acquisition & Analytics
Meta Ads
Google Ads
Google Analytics
Support
Zendesk
Gorgias
Processing layer
TITAN-REC 3.0
47
Signals / customer
+40
Suggested actions
75%
Next-buy accuracy
2ms
Latency
Email & Notifications
Personalized recommendation email
Post-purchase sequence
Targeted push & SMS
On-site & Cross-sell
Homepage / product-page / cart recommendations
Automated cross-sell & upsell
Catalog & Trends
Smart clearance
New product launch
Social-trend injection
Use case

Deployment surfaces

home
RECOMMENDED FOR YOU
Personalized homepage
"For you" block displayed on the home page, tailored to each visitor
newsletter
Your selection of the week
Personalized by TITAN-REC
0.93
0.93
0.93
Weekly email
5 products personalized per customer in the newsletter
meta ads
SPONSORED
Shop now
Meta Ads retargeting
Audiences built from per-customer recommendations
Under the hood

A recommendation engine, not one more plugin.

Multi-signal fusion

TITAN-REC integrates 47 signals per customer — purchase history, complementary products, market micro-trends, inter-product correlations, contextual signals (weather, seasonality, events). Every recommendation is the convergence of these 47 dimensions.

Multi-model consensus

Several specialized models must converge before a recommendation is issued. Not a single engine that decides: a confidence pipeline that eliminates noise and maximizes relevance.

Cold-start intelligence

Even for a visitor with no history, the model leverages real-time market micro-trends and contextual signals to suggest relevant products.

Continuous learning

The model improves every week from real interactions — clicks, purchases, returns, time spent. Not a static retrain: a learning loop that tracks your catalog as it moves, and that gets better with every retrain.

Model metrics
Next-purchase accuracy75%
Recommendation latency2ms
Signals per customer47
Business impact
Incremental sales+15%
Return rate-14%
Email conversion+25%
In-production deployment

TITAN-REC deployments

Titane deploys its personalized recommendation model across tens of thousands of customers
TITAN-REC· April 2026

Titane deploys its personalized recommendation model across tens of thousands of customers

Master Outillage

Integrate TITAN-REC

See what TITAN-REC recommends for your customers — or let us show you live, on your own data.