Real-time product recommendations that improve with every click.
An e-commerce store with 5,000+ SKUs shows the same "bestsellers" to every visitor. Static merchandising ignores browsing signals: categories viewed, time on product pages, cart adds/removes, price sensitivity, brand preferences. Conversion sits at 2-3%.
Algolia Recommendations operates with sub-10ms response times. Nosto delivers individualized experiences. Dynamic Yield offers ML-powered strategies with A/B testing. These prove personalization lifts conversion and AOV.
Mid-market merchants face meaningful monthly costs and integration complexity. Many default to Shopify's built-in "you may also like" using simple co-purchase data.
The agent builds real-time behavioral profiles from clickstream data: pages viewed, products examined, time spent, cart activity, price sensitivity, and category affinity. For returning customers, purchase history and reviews layer on top.
Hybrid recommendations: collaborative filtering (similar customers bought these), content-based (similar along style, price, brand dimensions), and contextual signals (time, device, referral). Different weights for different placements: discovery on homepage, complementary on PDP, completion at checkout.
Recommendations update within the session. A visitor shifting from running shoes to hiking boots sees recommendations adapt immediately. Email uses the most recent profile, not stale purchase data.
A senior engineer integrates directly with your storefront (Shopify, WooCommerce, or custom) via a lightweight JS snippet that streams clickstream events into a FastAPI service backed by Redis for session-level profile state and PostgreSQL for long-term interaction history. Recommendations run through a hybrid model combining collaborative filtering and content-based similarity, with placement-specific feature weights trained in TensorFlow on your historical order and browse data. Initial model training and storefront integration takes 2–3 weeks, with A/B test infrastructure included from day one.
The JS snippet streams click, add-to-cart, and dwell events in real time. Redis holds a live per-visitor profile that shifts recommendation weights as behavior evolves within the same session — no login required.
Collaborative filtering identifies what similar buyers purchased next. Content-based similarity fills cold-start gaps for new SKUs using catalog attributes. Each placement — homepage, PDP, checkout — uses separately tuned feature weights rather than one global model.
Homepage widgets optimize for discovery (broad affinity signals). PDP widgets optimize for complementary and substitute items. Checkout widgets target bundle completion. Email triggers use purchase-gap and win-back signals from the PostgreSQL interaction log.
Experiment infrastructure is part of the base build, not a bolt-on. Compare recommendation strategies head-to-head with statistical significance tracking across conversion rate, AOV, and revenue per visitor — no third-party tool required.
Anonymized work with hard metrics — NDA-bound, no client names.
2.4x
Search Click-Through
180ms
P95 Search Latency
31%
Conversion Lift
“The search click-through rate change was visible in analytics within the first week. The semantic understanding is actually working — customers are finding products they would not have found with keyword matching.”
— Head of Product, E-Commerce Platform
<2.5s
LCP (Core Web Vitals)
50+
Products at Launch
6
Filter Dimensions
“The routine builder was the feature we couldn't get from Shopify. It became the primary driver of repeat visits — customers come back to update their routine as they try new products, not just to browse.”
— Founder, Skincare Retail Brand
We custom-build each agent to fit your data, your rules, and your existing systems.
Free 30-min scoping call
Fordel Studios