AI Image / Visual Search

核心功能

Image upload search returning catalog products ranked by visual similarity score

AI visual embeddings (vector representations) generated per product image for fast nearest-neighbor matching

Pattern and shape recognition that matches texture, silhouette, and color regardless of keyword text

Auto product tagging from visual cues to enrich layered navigation and faceting

Camera and mobile capture support so shoppers can snap a photo and search on the spot

Configurable similarity threshold in admin to tune result strictness (precision vs. recall)

Cron-driven background indexing job that embeds new and changed images without blocking catalog saves

Observer on catalog_product_save_after keeps the visual index in sync as products change

Service contract (ImageSearchInterface) exposing visual search over both REST and GraphQL endpoints

Plugin/interceptor injection into storefront search and PDP with no Magento core file edits

Admin ACL resource and system configuration section under Stores > Configuration for role-gated control

Works on Magento Open Source and Adobe Commerce, with optional Live Search alignment on Commerce

按需定制，全程代劳

无需自行搭建——由 ECOSIRE 构建、安装并提供支持的可用应用。

您下单

以一次性构建价格开始。我们在启动时与您共同确定范围。

我们构建与安装

ECOSIRE 在您的 Magento 2 上构建、配置并安装。

上线 + 支持

约 2–4 周内上线，并提供上线后的支持期。

关于此产品

AI Image / Visual Search turns a photo into a query. Instead of guessing keywords, your shoppers upload an image — or snap one on mobile — and the extension returns the closest matching products from your catalog, ranked by visual similarity. It is purpose-built for fashion, furniture, and lifestyle merchants where look, shape, color, and pattern drive discovery far more than SKU text.

This is not an instant Adobe Commerce Marketplace download. It is a build-to-order extension: ECOSIRE builds the module against your Magento version, theme, and catalog structure, then installs and configures it on your environment. Technically it ships as a proper module under app/code/Ecosire/VisualSearch, wired with di.xml dependency injection, a custom service contract (ImageSearchInterface) so your storefront, REST, and GraphQL layers all call one stable API, and an admin ACL resource so only authorized roles touch configuration.

Under the hood, product images are converted to AI visual embeddings (vectors) during a backgrounded cron-driven indexing job, with an observer on catalog_product_save_after keeping the index fresh as the catalog changes. Pattern recognition and auto product tagging from visual cues enrich faceting. At query time, an uploaded image is embedded and matched against the index; a configurable similarity threshold controls how strict results are. A plugin/interceptor injects the visual-search entry point into your existing search and PDP without core edits.

Works on both Magento Open Source and Adobe Commerce (we adapt to Commerce-only features like Live Search where it makes sense). You get a clean upgrade path, documented config, and ECOSIRE support after go-live.

你得到什么

Custom Magento 2 module delivered under app/code/Ecosire/VisualSearch, version-matched to your install

Installation and configuration on your Magento environment (staging first, then production)

Admin configuration section with similarity threshold, indexing controls, and ACL setup

REST and GraphQL endpoints documented for headless/PWA storefront integration

Initial catalog image embedding/index build plus the cron job that maintains it

Storefront integration into your theme (upload widget, mobile camera capture, results rendering)

Technical handover doc covering architecture, config, reindex commands, and upgrade notes

Post-launch support window for bug fixes and Magento minor-version compatibility

这是给谁的

Fashion & apparel merchant

Sells visually-driven products where shoppers struggle to name a print, cut, or color. Wants 'find me something that looks like this' so customers discover lookalikes and reduce zero-result searches.

Furniture & home decor retailer

Has large catalogs where style and form matter more than SKU text. Needs shoppers to upload an inspiration photo and surface matching sofas, lamps, or rugs by shape and pattern.

Lifestyle / multi-brand store owner

Runs a broad catalog where keyword search underperforms on aesthetic intent. Wants AI visual search plus auto-tagging to improve discovery and on-site conversion without rebuilding their search stack.

AI Image / Visual Search 如何比较

标准	伊科西尔	定制建造	竞争对手	Magento 2 原生
Photo-upload visual search with similarity ranking	包含	部分支持	包含	不包括在内
AI visual embeddings + pattern/shape recognition	包含	部分支持	部分支持	不包括在内
Auto product tagging from visual cues	包含	部分支持	部分支持	不包括在内
Mobile camera capture support	包含	部分支持	包含	不包括在内
Built, installed & supported on your exact Magento/theme	包含	包含	不包括在内	不包括在内
REST + GraphQL service contract for headless/PWA	包含	部分支持	部分支持	部分支持
Configurable similarity threshold tuned to your catalog	包含	部分支持	部分支持	不包括在内
Instant self-serve download, no build wait	不包括在内	不包括在内	包含	包含

关于AI Image / Visual Search的常见问题

How long until the extension is live on my store?

Because this is build-to-order, ECOSIRE builds the module against your exact Magento version, theme, and catalog before installing. Typical delivery is roughly 2 to 4 weeks depending on catalog size, embedding model choice, and integration depth (standard Luma/theme vs. headless PWA via GraphQL). We confirm a firm timeline after a short scoping call, install on staging first, then promote to production.

What ongoing support and updates do I get?

Every build includes a post-launch support window for bug fixes and compatibility with Magento minor releases. Because the module is isolated under app/code/Ecosire/VisualSearch with no core edits, Magento patches and most upgrades apply cleanly. ECOSIRE offers continued support and feature work after the initial window; we keep the service contract and admin config stable so updates don't break your storefront.

Does it work on both Magento Open Source and Adobe Commerce?

Yes. The module is built on standard Magento 2 architecture (service contracts, di.xml, plugins, observers, ACL) so it runs on both Magento Open Source and Adobe Commerce. On Adobe Commerce we can align the visual results with Live Search/Catalog Service where it adds value, but the core visual matching does not depend on Commerce-only features.

Where do the AI embeddings run, and does my catalog data leave my server?

We scope this with you. The embedding step can call an external AI provider (vectors only, not raw customer data) or run against a self-hosted model, depending on your data-residency and cost requirements. Product images are embedded during a cron-driven background job; queries embed the uploaded image and match it against your local vector index. We document exactly what is sent where before build.

Will it slow down catalog saves or storefront performance?

No. Image embedding runs in a cron-driven background indexing job, not inline with catalog saves. An observer on catalog_product_save_after only flags changed products for re-embedding. At query time the storefront calls the service contract, which performs a fast nearest-neighbor lookup against the prebuilt index, so visual search adds minimal latency to the page.