Image-to-Image Similarity Search
Upload or capture a product photo and return the closest visual matches from your catalog, ranked by similarity in color, shape, pattern, and style.
Visual Search API
Computer vision that lets shoppers search using a photo instead of keywords, matching it against your product catalog. Built for ecommerce, digital asset management, and stock media platforms.
- 0+Years Engineering Excellence
- 0+Projects Delivered
- 0%Successful Go-Live
- 0 HrsResponse Time
velvet armchair, curved arms≤ $400
embedding · 512-d 3 attributes found
Closest matches ranked by similarity
- 98%
- 96%
- 93%
- 91%
- 88%
- 85%
Capabilities
Four building blocks that turn a shopper's photo into the right products.
Text-to-Image and Cross-Modal Search
Combine an image with text filters, like category, price range, or size, so results narrow by both what the image shows and what the shopper actually wants.
Multi-Item and Attribute Detection
Detect multiple products within a single image and extract attributes like material, print, silhouette, and design details for finer-grained matching.
Embedding-Based Architecture
Product images are converted into vector embeddings and indexed for fast retrieval, the same approach used for image and text similarity search in modern multimodal systems.
Use Cases
Aarvexa's custom visual search solutions are tailored for platforms requiring precise image-to-image matching capabilities.
Ecommerce and retail
Shoppers upload a photo, a screenshot, or a picture from a magazine and get matched to visually similar products in your catalog instead of guessing at search terms.
Digital asset management
Search large image or media libraries by visual reference instead of relying entirely on filenames, folders, or manually entered tags.
Fashion and furniture
Match clothing, footwear, accessories, and home décor by print, silhouette, material, and style from a single reference photo.
Stock and creative libraries
Locate visually similar assets across large creative libraries without depending on how thoroughly each image was tagged.
Custom Visual SearchOwnedmodels + data + workflows
Custom-built vs. off-the-shelf visual search platforms
Criteria Custom-Built (Aarvexa AI)Off-the-Shelf Visual Search
SetupBuilt to your existing catalog and architecturePrebuilt plugin for major platforms
Catalog fitTrained on your categories, attributes, and namingGeneric product taxonomy
Accuracy on your dataBenchmarked against your real catalog imagesUnverified against your specific images
PlatformDeploys to any storefront, app, or internal systemTied to the vendor's supported platforms
Data ownershipYour data and resulting model stay yoursData handling and model reuse depend on the vendor's published terms
PricingScoped to your integration and volumePer-query or per-seat SaaS tiers
After Launch
Three numbers determine whether visual search is actually working, not just installed.
Key Metrics for Visual Search Success
Ensure your visual search solution drives real business value with these measurable outcomes:
Conversion Rate
The share of shoppers who use visual search and go on to purchase, compared against text-search and browse-only conversion.
Revenue per session
Total revenue from sessions that included at least one visual search query, divided by the number of those sessions.
Adoption Rate
The share of users who try visual search at all. Low adoption usually points to a discoverability problem, like a buried search icon.
More Solutions
Extend visual intelligence across object recognition, object tracking, safety monitoring, apparel analysis, food recognition, face recognition, demographic analysis, and AI image analysis.
FAQ
Straight answers on visual search matching, catalog integration, metrics and pricing.
A visual search API lets shoppers search with a photo instead of keywords. It converts the image into a vector embedding and returns the closest visual matches from your own product catalog.
A plugin uses a generic product taxonomy and is tied to specific platforms. A custom build is trained on your categories, attributes and naming, benchmarked on your real images, and deploys to any storefront, app or internal system.
Ecommerce and retail teams, digital asset management, fashion and furniture brands, and stock or creative media libraries.
Share your catalog size, image quality and the journeys you want to improve. We scope a pilot on a representative slice of your catalog and agree success metrics first.
It depends on catalog size, image quality and integrations. A focused pilot is usually the first milestone, and we agree timelines during discovery.