Turn large product catalogues into guided discovery

Manual Shopify collection blocks show the same products to every shopper regardless of their intent. RecoMelon personalises discovery dynamically per session - adapting recommendations based on what each shopper is looking at, filtering for, and visually drawn to. Two shoppers on the same PDP see different recommendations based on their behaviour. This is what makes RecoMelon an active conversion tool rather than a static curated list.

Dealbreaker™ Filters are RecoMelon's intent-led refinement system. They let Shopify shoppers filter the recommendation panel by the attributes that actually drive their buying decisions - sleeve length, neckline, fabric, fit, or colour - rather than broad category filters. When a shopper signals they only want V-neck or relaxed fit, Dealbreaker™ eliminates everything that doesn't qualify before it's shown. This reduces bounce and increases purchase confidence.

RecoMelon helps turn search, collections and product pages into connected discovery journeys. When one product is almost right, shoppers can continue into visually aligned alternatives or complementary products rather than reaching a dead end. Results vary by catalogue, placement, traffic and measurement method. Published examples include Pretty Lavish achieving 18.1% higher AOV on recommendation-influenced orders, By Lotte shoppers exploring three times more recommendations per session, and DecoratorsBest increasing recommendation-driven orders by nearly four times.

RecoMelon starts with the product, not purchase history. Most recommendation engines use collaborative filtering - recommending what similar buyers bought. RecoMelon uses VisualDNA™ to read each product image and understand how it actually looks - cut, colour, silhouette, print, material - then recommends based on visual similarity. This means new products are recommended from day one with zero sales data, and a shopper viewing a floral midi dress sees more floral midis, not whatever happened to be in the same basket. The result is a product intelligence engine, not a personalisation engine built on historical behaviour.

RecoMelon is an AI product-discovery platform for Shopify. It connects natural-language search, visual recommendations, Smart Collections, PDP alternatives and complementary bundles using product imagery and catalogue context. VisualDNA™ helps understand characteristics such as colour, silhouette, pattern and style, while merchandising and behavioural signals add commercial and shopper context. The result is a connected discovery layer across search, collections, product pages and baskets.

No technical skills are needed. RecoMelon installs from the Shopify App Store in under 60 seconds - no developer time, no code changes, no downtime. ColdStart™ scans the product catalogue automatically on installation, and most merchants see their first recommendation-influenced orders within 60 minutes of going live. New merchants start on a $1/day, 30-day trial - full feature access from day one, with a money-back guarantee if it doesn't perform. For Shopify Plus merchants or custom theme stores, the RecoMelon team handles onboarding directly.

RecoMelon is designed to load asynchronously through Shopify App Embeds so it does not block the initial page render. Actual performance still depends on the theme, storefront architecture, installed applications and placement. Core Web Vitals and storefront behaviour should therefore be checked on a staging theme and monitored after launch.

RecoMelon's visual AI matching analyses fine-grained product attributes including fabric texture, colour harmony, silhouette structure, and print pattern. Rather than relying on broad category tags, the system identifies nuanced visual relationships between products - making recommendations feel precise and intuitive rather than generic. Accuracy improves over time as RecoMelon learns from how shoppers engage with the recommendation panel on your specific Shopify store.