Visual Search

Visual Search Solutions for Ecommerce

Help customers find products through images — upload, tap-to-search, find similar, and lifestyle-led discovery — on the same retrieval foundation as text search and recommendations.

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Visual discovery gap

When inspiration starts with an image, not a query

Shoppers upload photos, tap products in lifestyle shots, and expect find-similar to work — even when the catalog was built for keywords alone.

Catalog text cannot carry the product

Shoppers see the item in a photo, lifestyle scene, or variant image — but search still depends on titles and tags that never captured those attributes.

Visual search lives in a separate lane

Image retrieval, keyword search, and recommendations run on different stacks — so discovery feels inconsistent and expensive to change.

Similarity without merchandising control

Find-similar results look plausible but ignore stock, margin, campaigns, or category strategy — so visual discovery does not serve the business.

Visual search pays off when image retrieval, structured catalog data, and merchandising logic share one production retrieval layer — not when visual matching is grafted onto a stack that was never designed for it.

Capabilities

Image-led discovery that still behaves like commerce search

Strong visual search combines embeddings, structured attributes, behavioral signals, and business rules — the same multi-signal pattern modern digital commerce expects from search and browse.

Upload, tap, and find similar

Support upload search, tap-to-search on product and lifestyle imagery, and find-similar flows that keep shoppers moving when keywords fail.

Hybrid visual and text retrieval

Combine image embeddings with lexical search and structured attributes in one retrieval path — not a bolt-on vector service beside legacy search.

Structured catalog signals

Rank using color, material, size, category, and variant data alongside visual similarity so results stay commerce-relevant, not just visually close.

Catalog enrichment at serve time

Bring normalized product data, inventory state, and enriched attributes into the same index that powers visual matching.

Merchandising-aware ranking

Apply business rules, promotional boosts, and inventory-aware adjustments alongside visual relevance — curated control plus algorithmic discovery.

Production scale and freshness

Handle large, attribute-rich catalogs with real-time updates so visual discovery stays accurate as products, campaigns, and inventory change.

Business impact

What better visual discovery improves

The value shows up in engagement, conversion paths, and catalog coverage — not just novelty in a demo.

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Higher engagement through image-led discovery paths

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Fewer zero-result sessions when shoppers search by appearance

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Better visibility for long-tail and visually distinct SKUs

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Faster iteration on visual experiences without rebuilding the stack

Why Searchplex

Why retail teams bring Searchplex in

Usually when image-led discovery matters commercially, but the current platform makes custom capability, catalog alignment, or production rollout difficult.

Visual discovery delivery

From design through rollout — aligned to catalog structure, variant logic, mobile flows, and how your team will operate the system after launch.

Search and computer vision depth

Production experience in visual retrieval, catalog alignment, lifestyle parsing, and commerce-specific ranking — not demo-grade image matching.

Unified discovery foundation

Visual search as part of the same retrieval architecture as text search and recommendations — reducing seams, latency, and operational drag.

Team enablement after launch

Practical handoff so merchandising, search, and engineering teams can tune visual discovery without depending on a vendor black box.

Related paths

Explore the discovery stack

Visual search is strongest when it shares retrieval, ranking, and evaluation with text search and personalization.

AI Search & Personalization for E-Commerce

Hybrid lexical, semantic, behavioral, and visual signals on one discovery foundation.

Explore AI Search & Personalization →

Framework

The Retrieval Foundation

How content preparation, retrieval modeling, query understanding, ranking, and evaluation fit beneath multimodal discovery.

Explore the Retrieval Foundation →

Start here

Make your catalog visually searchable

Design image-led discovery that understands your catalog structure, variant logic, and merchandising goals.

Explore AI Search & Personalization