مثالی پیش منظرA 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.
AI Product Recommendation Engine کیا ہے؟
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.
اہم خصوصیات
آرڈر پر تیار، مکمل طور پر آپ کے لیے
خود سیٹ اپ کی ضرورت نہیں — ایک کام کرنے والی ایپ جو ECOSIRE بناتا، انسٹال اور سپورٹ کرتا ہے۔
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آپ آرڈر دیتے ہیں
ایک بار کی تعمیر کی قیمت سے آغاز کریں۔ آغاز پر ہم آپ کے ساتھ مل کر دائرہ کار طے کرتے ہیں۔
- 2
ہم بناتے اور انسٹال کرتے ہیں
ECOSIRE اسے آپ کے Magento 2 پر بناتا، ترتیب دیتا اور انسٹال کرتا ہے۔
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لائیو + معاونت
آپ تقریباً ایک کاروباری ہفتے میں لائیو ہو جاتے ہیں، گو لائیو کے بعد دو ہفتوں کی معاونت کے ساتھ۔ ہمارے فراہم کردہ کوڈ کی خرابیاں مفت درست کی جاتی ہیں۔
اس مصنوع کے بارے میں
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.
What you get
- 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
Who this is for
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.
How AI Product Recommendation Engine Compares
| Criterion | ECOSIRE | Custom Build | Competitor | Magento 2 نیٹو |
|---|---|---|---|---|
| Personalized recommendations beyond static related/up-sell lists | Included | Partial support | Included | Not included |
| Behavioral signal tracking (views, dwell, cart, purchase) | Included | Partial support | Partial support | Not included |
| A/B testing of placements with AOV/conversion reporting | Included | Not included | Partial support | Not included |
| Built specifically around your catalog, theme and store-view scope | Included | Included | Not included | Partial support |
| Full-page-cache safe, FPC-compatible rendering | Included | Partial support | Partial support | Included |
| GraphQL / REST for headless / PWA Studio | Included | Partial support | Partial support | Partial support |
| Installed, tested and supported by the builder | Included | Partial support | Not included | Not included |
| Source code handover with coding-standard compliance | Included | Included | Partial support | Included |
Frequently Asked Questions about 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