Ecommerce search is undergoing its biggest shift since the keyword era. Shoppers no longer come with perfect queries - they come with intent, expressed through text, images, voice, or a mix of everything.

As AI reshapes how shoppers express what they want, retailers must move from “literal keyword matching” to “understanding what the shopper is trying to find.”

Visual search solves this gap by letting shoppers search the way they think, not the way they type.

Why ecommerce is moving beyond keywords

Keyword search still works. But today’s shoppers expect discovery that feels:

• Instant and frictionless - fewer clicks, fewer refinements

• Visually intuitive - especially for fashion, beauty, home décor, lifestyle

• Context-aware - recognizing style, attributes, categories, use-case

• Personal - tuned to their preferences and behavior

Multimodal search unlocks these experiences by letting shoppers express intent the way humans naturally do: through language + visuals + context.

Why visual search is the first practical step toward multimodal search

Multimodal search combines image + text + behavioral + contextual signals, but most retailers begin the transformation with visual search, because:

• It’s easy for shoppers to use

• It produces immediate conversion lift

• Image understanding boosts accuracy across your entire search pipeline

• It prepares product data for future agentic workflows

• It makes the catalog “AI-readable” by enriching attributes

Visual search is the gateway to improved product discovery—and the foundation for more advanced AI-led capabilities.

UX improvements that visual search unlocks

Visual search improves the shopper journey across the funnel:

• Removes the need for perfect keywords — a single upload gives instant context.

• Reduces zero-result searches through visual intent recognition.

• Shortens refinement cycles — shoppers skip filters and jump straight to relevant items.

• Delivers “complete the look” recommendations using visual style matching.

• Improves mobile search where typing long queries is inconvenient.

• Boosts engagement by turning social inspiration into purchasable moments.

Setup complexity: what teams really need to know

Visual search may seem complex, but modern AI platforms simplify the heavy lifting.

What’s easy:

• Plug-and-play APIs for uploading product images

• Pre-trained vision models

• Auto-extraction of visual attributes

• Real-time ingestion and indexing

Where retailers must plan:

• Maintaining consistent product image quality

• Cleaning catalog metadata to align text + visual signals

• Ensuring categories are correctly mapped

• Identifying attributes that visual AI can enhance

This naturally brings us to a critical dependency: Attribute Enrichment.

Data requirements for accurate visual + multimodal search

Visual search accuracy depends on the quality of:

1. Product images

• Clear, front-facing, uncluttered

• Multiple angles

• Standardized lighting

2. Catalog attributes

Visual models perform best when paired with strong metadata like:

• Color

• Material

• Pattern

• Fit

• Silhouette

• Occasion

• Style

Retailers often have gaps here, which is why AI-led attribute enrichment becomes essential.

3. Behavioral signals

• Clicks

• Product views

• Add-to-cart events

• Category refinements

This behavioral data helps the model fine-tune rankings over time.

AI accuracy drivers: what determines great results

Visual search accuracy depends on multiple system-level components:

• Image quality — the clearer the product, the better the model identifies it.

• Attribute density — enriched catalogs > raw catalogs.

• Label consistency — “navy blue” vs “blue/navy” slows down matching.

• Category hygiene — miscategorized products degrade accuracy significantly.

• Hybrid relevance models — vision AI + text retrieval + vector embeddings + user intent.

• Feedback loops — more interactions → better ranking over time.

Netcore Unbxd’s hybrid model combines vision understanding, entity recognition, vector search, and behavioral ranking to ensure accuracy scales with catalog size.

Why Netcore Unbxd Visual Search stands out

Netcore Unbxd delivers enterprise-grade visual discovery built specifically for ecommerce:

• Detects objects, patterns, and colors within an image

• Handles images of varying sizes, formats, and origins

• Identifies multiple objects with bounding boxes

• Returns visually similar results in under ~3 seconds

• Flexible input formats through the Visual Search API

• Integrates seamlessly with semantic, vector, and keyword engines

• No-code setup and management via the Netcore Unbxd Console

• In-depth analytics to measure revenue impact, user engagement, and historical trends

This creates a scalable, reliable, high-accuracy visual search experience tailored for product-rich ecommerce brands.

How Visual Search bridges into attribute enrichment and agentic search

Visual search generates a rich layer of visual attributes (color, pattern, material cues, structures) that enhance search ranking and retrieval. Combined with AI-led Attribute Enrichment, retailers can:

• Fix incomplete or inconsistent product metadata

• Improve product classification and category mapping

• Strengthen multimodal search precision

• Provide richer signals for vector and semantic rankers

Once your catalog is fully enriched and AI-ready, you unlock the next stage: agentic search. Agentic search uses multimodal understanding to:

• Reformulate vague queries

• Identify shopper intent automatically

• Suggest categories or styles proactively

• Guide shoppers through contextual, multi-step journeys

Visual search → attribute enrichment → agentic search becomes the natural AI growth path for modern retailers.

Book a demo today to explore Netcore Unbxd visual search.