Aperçu illustratifA custom-built Magento 2 / Adobe Commerce extension that tracks behavioral signals and serves personalized
cross-sell, up-sell and "frequently bought together" blocks on homepage, PDP and cart — built, installed and supported by ECOSIRE.
Qu'est-ce que AI Product Recommendation Engine ?
A custom-built Magento 2 / Adobe Commerce extension that tracks behavioral signals and serves personalized cross-sell, up-sell and "frequently bought together" blocks on homepage, PDP and cart — built, installed and supported by ECOSIRE. Built to order by ECOSIRE for Magento 2 / Adobe Commerce (build-to-order) — indicative price from $999.00 USD; request a quote for a scoped proposal.
Fonctionnalités clés
Fait sur commande, clé en main
Aucune configuration à faire vous-même — une app fonctionnelle conçue, installée et prise en charge par ECOSIRE.
- 1
Vous commandez
Commencez par un prix de développement unique. Nous cadrons le projet avec vous au lancement.
- 2
Nous développons et installons
ECOSIRE la développe, la configure et l'installe sur votre Magento 2.
- 3
Mise en ligne + assistance
Vous êtes en ligne en une semaine ouvrée environ, avec deux semaines d’assistance après la mise en production. Les défauts du code que nous livrons sont corrigés gratuitement.
À propos de ce produit
The AI Product Recommendation Engine is a Magento 2 / Adobe Commerce extension that ECOSIRE builds to order for your catalog, then installs and supports on your store. It is not an instant Adobe Commerce Marketplace download — we engineer it around your taxonomy, themes and traffic, deliver it as a versioned module under app/code/Ecosire/ProductRecommendations, and hand over a tested, production-ready build.
Behaviorally, the module captures real signals — product views, dwell time, add-to-cart and purchase events — through frontend observers and a lightweight tracking endpoint, persisting them to dedicated tables so recommendations reflect what shoppers actually do rather than static "related products" lists. A scoring service (exposed via a service contract / DI interface) blends collaborative signals, frequently-bought-together affinity, and seasonal/trending models computed on a cron schedule.
Storefront output is delivered as themeable UI components (layout XML blocks + KnockoutJS for cart/checkout) that drop into homepage, PDP and cart, plus GraphQL and REST resolvers so PWA Studio / headless storefronts consume the same recommendations. An admin section under a dedicated ACL lets merchandisers configure placements, pin or exclude products, and run A/B tests of recommendation strategies with conversion and AOV reporting.
Everything respects multi-store-view scope, customer-group pricing and Magento's full-page cache (recommendations render via a private-content/ESI-friendly path so caching stays intact). On Adobe Commerce we can integrate with B2B and customer segments; on Open Source we deliver the equivalent natively. ECOSIRE handles installation, setup:upgrade, theme integration, and post-launch support so the engine ships clean and keeps performing.
Ce que vous obtenez
- Custom Magento 2 module (Ecosire_ProductRecommendations) under app/code, versioned and composer-installable, with registration.php, module.xml, di.xml and declarative schema (db_schema.xml)
- Installation and configuration on your environment — setup:upgrade, di:compile, theme block placement and full smoke test on staging then production
- Admin configuration guide plus a short merchandiser walkthrough of placements, pins/exclusions and A/B test setup
- GraphQL/REST API documentation for headless or PWA Studio consumption of the recommendation endpoints
- Source code handover with PHPCS/Magento coding-standard compliance and inline documentation
- Post-launch support window with bug fixes and Magento minor-version compatibility checks, plus a maintenance option thereafter
Pour qui c'est
Ecommerce Manager (mid-to-large catalog)
Owns AOV and conversion targets for a 5,000+ SKU store and wants personalization that goes beyond Magento's static related/up-sell lists without ripping out the existing theme.
Magento Technical Lead / Solution Architect
Needs a clean, service-contract-based module that respects FPC, store-view scope and customer groups, exposes GraphQL for the PWA front end, and won't become unmaintainable bespoke spaghetti.
Merchandiser / CRO Specialist
Wants admin control to pin hero products, exclude clearance items, and A/B test recommendation strategies with real conversion and AOV reporting rather than guessing.
Comment AI Product Recommendation Engine se compare
| Critère | ÉCOSIRE | Construction personnalisée | Concurrent | Natif Magento 2 |
|---|---|---|---|---|
| Personalized recommendations beyond static related/up-sell lists | Inclus | Prise en charge partielle | Inclus | Non inclus |
| Behavioral signal tracking (views, dwell, cart, purchase) | Inclus | Prise en charge partielle | Prise en charge partielle | Non inclus |
| A/B testing of placements with AOV/conversion reporting | Inclus | Non inclus | Prise en charge partielle | Non inclus |
| Built specifically around your catalog, theme and store-view scope | Inclus | Inclus | Non inclus | Prise en charge partielle |
| Full-page-cache safe, FPC-compatible rendering | Inclus | Prise en charge partielle | Prise en charge partielle | Inclus |
| GraphQL / REST for headless / PWA Studio | Inclus | Prise en charge partielle | Prise en charge partielle | Prise en charge partielle |
| Installed, tested and supported by the builder | Inclus | Prise en charge partielle | Non inclus | Non inclus |
| Source code handover with coding-standard compliance | Inclus | Inclus | Prise en charge partielle | Inclus |
Foire aux questions sur AI Product Recommendation Engine
Is this an instant download from the Adobe Commerce Marketplace?
No. This is a build-to-order extension. After purchase ECOSIRE engineers the module around your catalog, themes and traffic, then installs and configures it on your store. You receive a versioned, tested module under app/code — not a generic Marketplace package.
How long until it's live on my store?
Typical delivery is about one working week from kickoff, depending on catalog size, theme complexity, headless vs. Luma, and how many placements and A/B variants you need. We scope an exact lead time after a short discovery call and build/test on staging before any production deploy.
What ongoing support and updates do I get?
The build includes a post-launch support window covering bug fixes and Magento minor-version compatibility checks. After that you can take an optional maintenance plan for upgrade compatibility (new Magento / Adobe Commerce releases), model tuning and enhancements. We don't push silent auto-updates — changes are reviewed and deployed with you.
Will it slow down my store or break full-page cache?
No. Recommendations render through a private-content / customer-data path so the full-page cache stays intact, and heavy model computation runs on cron rather than per-request. We profile the integration on staging and keep storefront rendering lightweight.
Does it work with a headless / PWA Studio storefront?
Yes. The module exposes GraphQL resolvers and REST endpoints so PWA Studio or any composable front end consumes the same recommendation logic as the Luma storefront. We document the schema and endpoints as part of the handover.
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AI Product Recommendation Engine
A custom-built Magento 2 / Adobe Commerce extension that tracks behavioral signals and serves personalized cross-sell, up-sell and "frequently bought together" blocks on homepage, PDP and cart — built, installed and supported by ECOSIRE.
- Behavioral signal tracking — product views, dwell time, add-to-cart and purchase events captured via frontend observers and a dedicated REST tracking endpoint, stored in custom tables
- Personalized recommendation blocks for homepage, PDP and cart, delivered as themeable layout XML / block widgets placed via admin layout or templates
- Frequently-bought-together logic built from real order line-item co-occurrence, recomputed on a Magento cron schedule
- Seasonal and trending models that surface rising products by time window, category and store view