Better product discovery. Measurable revenue lift.
Sabino helps ecommerce retailers improve product discovery with AI-powered product recommendations, personalization, and automated outfit recommendations. Sabino combines product intelligence, shopper behavior, machine learning, merchandising controls, and retail expertise to improve relevance, conversion, cross-sell, and revenue per visitor.
What Sabino sells, who it helps, and why retailers use it.
This section summarizes Sabino's Product Recommendations and Outfit Recommendations offerings, including the customer fit, value proposition, commercial model, differentiation, and implementation approach.
AI-powered Product Recommendations and Outfit Recommendations for ecommerce retailers.
Help shoppers discover more relevant products and product combinations while improving conversion, cross-sell, average order value, and revenue per visitor.
Direct-to-consumer retailers with established product catalogs and meaningful ecommerce traffic, typically generating $5 million or more in annual revenue.
Built for retail categories including apparel, footwear, accessories, jewelry, beauty, home, and other product-driven ecommerce businesses.
Personalized and product-aware recommendations across the customer journey, including Recommended for You, Similar Items, Complementary Items, Best Sellers, New Arrivals, Top Rated, Recently Viewed, and color-based recommendations.
Automated Complete the Look and shoppable-model experiences that use products already in the retailer's catalog.
Sabino combines enriched product data, machine learning, shopper and product-performance signals, merchandising controls, fully managed implementation, and hands-on retail expertise.
Sabino combines AI-generated outfit recommendations with brand-specific styling guidance and human stylist review rather than requiring the retailer to manually style every product.
Sabino can run alongside the retailer's existing recommendation experience through a free, fully managed A/B test on real site traffic.
Product recommendation tests generally run 30–60 days, depending on traffic volume and the time required to reach a meaningful performance read.
Retailers see performance results before deciding whether to continue. Sabino's pitch materials describe the ongoing commercial relationship as month-to-month.
Product Recommendation pricing is based on store scale, measured by page views. Outfit Recommendation pricing is based primarily on store scale and PDP traffic. Pricing is shared before the test.
Sabino works with major ecommerce platforms including Shopify, BigCommerce, and other standard ecommerce stacks.
Sabino's September 2026 materials cite more than 1.2 billion recommendations delivered annually across more than 20 retail brands.
Start with a product walkthrough. When there is a fit, evaluate Sabino on the retailer's own traffic through a free, fully managed A/B test and make the longer-term decision based on measured results.
Where better recommendations create value.
Sabino is designed for retailers that want shoppers to discover more relevant products without creating a large manual merchandising burden.
Shoppers need help finding the right next product.
Sabino surfaces relevant alternatives, complements, best sellers, new products, personalized products, and other recommendation experiences throughout the site.
Generic ranking leaves relevance on the table.
Sabino can combine product attributes, relationships, inventory, shopper behavior, commercial performance, and machine learning to determine what products should appear and how they should be ordered.
Relevant complements are difficult to scale manually.
Complementary Items, Frequently Bought Together, and Complete the Look experiences help shoppers discover additional products that work with the item already being considered.
Styling every PDP manually does not scale.
Sabino can automatically create multiple coordinated looks for apparel products while incorporating styling guidelines, merchandising rules, and human review.
Automation still needs business judgment.
Retailers can control product eligibility, inventory requirements, exclusions, priorities, pinning, promotion, demotion, and other merchandising rules.
Retailers should not have to buy on promises.
Sabino can run alongside the current experience so the retailer can evaluate performance using real traffic before deciding whether to move forward.
Better data. Better AI. Better outcomes.
Sabino was built by ecommerce, catalog, apparel, and data science professionals. The platform combines retail operating knowledge with modern recommendation technology rather than treating recommendation ranking as a purely technical problem.
Understand the catalog more deeply.
Sabino cleans, structures, and enriches product information to improve how algorithms understand products, variants, attributes, categories, colors, relationships, imagery, and availability.
Rank for shopper relevance and business outcomes.
Sabino combines machine learning with product intelligence, shopper behavior, engagement, commercial performance, and merchandising rules to determine recommendation results.
Evaluate Sabino against the current experience.
Sabino can run alongside an existing recommendation solution so retailers can evaluate incremental performance using real A/B test data before making a long-term decision.
The right recommendation for every moment.
Sabino Product Recommendations enable retailers to display automated recommendations across the ecommerce customer journey. Sabino identifies relevant products for each recommendation use case, applies configured filters and business rules, ranks eligible products, and returns the final results for display.
Use shopper signals when available.
Known-shopper ranking can incorporate customer attributes, purchase history, browsing behavior, lifetime spend, recency, and prior interactions.
Understand more than a SKU.
Recommendation logic can consider taxonomy, product attributes, category, color, variants, inventory, availability, and relationships between products.
Learn from real shopper behavior.
Product views, purchases, reviews, realized selling prices, returns, and other engagement and commercial signals can inform model training and ranking.
Apply rules when they matter.
Retailers can influence eligibility and ranking while allowing algorithms to automate product discovery where manual intervention is unnecessary.
Recommendation ranking goes beyond clicks.
Sabino uses machine-learning ranking models that can combine shopper relevance with product and commercial performance. Depending on the recommendation model and implementation, signals can include demand, engagement, customer segment, realized selling price, product costs, returns, reviews, cart activity, and other available data.
Catalog Data
Understand available products, taxonomy, attributes, product relationships, variants, and eligibility.
Historical Order Data
Identify relationships between products, including items that are frequently purchased together, and inform recommendation models.
Shopper Behavior
Use onsite interaction signals to understand shopper interests, support personalization, measure performance, and improve recommendation experiences.
How Sabino handles new products
New products often have limited sales and engagement history. Sabino can initially use the performance of similar, established products to inform ranking. As the new product accumulates its own engagement and sales data, its actual performance increasingly determines its ranking.
Automation without giving up control.
Sabino provides configurable controls that let retailers influence recommendation results when specific business requirements should take precedence.
Control what can be recommended.
Rules can consider inventory, price, promotional status, product scope, brand, category, subcategory, gender, color, material, product recency, and other supported attributes.
Control how results behave.
Merchants can manage result diversification, exclude products based on shopper behavior, assign different algorithms to recommendation slots, and establish product-quality requirements.
Override when business judgment matters.
Products can be excluded, pinned to specific positions, promoted, demoted, or targeted to selected shopper audiences without replacing the underlying recommendation engine.
Complete the Look. Without the manual work.
Sabino Outfit Recommendations help apparel retailers create coordinated looks across their catalogs without manually styling every product page. Sabino combines product intelligence, brand-specific styling guidance, AI-generated combinations, performance data, and human stylist oversight.
Create coordinated outfits around a featured product.
Sabino recommends complementary products already in the catalog and can generate multiple looks for a product, helping shoppers understand how pieces work together and discover more of the assortment.
Make existing model photography shoppable.
When products shown in model photography are available in the retailer's catalog, Sabino can display those exact products so shoppers can purchase the complete look shown in the image.
How Sabino builds outfit recommendations.
Sabino's approach combines product intelligence, brand rules, proprietary recommendation models, and human review to create scalable outfit experiences that remain aligned with the retailer's merchandising direction.
Ingest product catalog and imagery
Use product data, imagery, taxonomy, attributes, availability, and other catalog information.
Clean, enrich, and attribute products
Standardize taxonomy, improve attributes, and add product and styling signals that help Sabino understand how products relate.
Create brand rules and Outfit Blueprints
Translate merchandising guidelines and lookbooks into structured styling rules, preferred complements, eligibility requirements, and other brand-specific guidance.
Generate outfit candidates with AI
Sabino generates coordinated outfit combinations from eligible products across the retailer's catalog.
Stylists review and refine
Sabino's styling team reviews and edits recommendations to help ensure outfits remain coordinated, current, and appropriate for the retailer's brand.
Measure and improve
Shopper interaction and product-performance data help Sabino understand which looks and product combinations perform well and inform ongoing recommendation optimization.
Revenue per mobile visitor in one live apparel test
One apparel retailer saw approximately 12% higher revenue per mobile visitor after adding Sabino Outfit Recommendations. The retailer also saw increased average order value and add-on purchases.
Designed to scale across an apparel catalog.
Multiple Looks
Sabino can automatically create up to three coordinated looks for a product page, giving shoppers multiple ways to wear and buy the featured item.
Existing Imagery
Retailers do not need new photoshoots. Sabino can work from existing catalog imagery and product data.
Laydown Imagery
Where useful, Sabino can also create custom product laydown imagery for coordinated outfit presentations.
Brand Controls
Retailers can provide styling guidelines, exclusions, inventory requirements, category rules, priorities, and other merchandising preferences.
Prove it on your own traffic.
Sabino's recommended starting point is a fully managed test against the retailer's existing experience. The retailer sees actual performance results before deciding whether to continue.
Connect
Integrate Sabino with the retailer's ecommerce environment, product catalog, and required data sources.
Launch
Deploy Sabino recommendations alongside the retailer's existing recommendation experience.
Measure
Run the experience on live traffic and evaluate performance using the agreed A/B test methodology.
Decide
Review the measured results, pricing, economics, and rollout opportunity before making the longer-term decision.
Built for established ecommerce retailers.
Sabino is designed for retailers with established product catalogs and enough ecommerce traffic to support meaningful optimization and testing. The public Sabino recommendations pages describe the typical fit as direct-to-consumer brands generating approximately $5 million or more in annual revenue.
Apparel
Product recommendations, personalization, Complete the Look, shoppable styling, and complementary-product discovery.
Footwear, Jewelry & Accessories
Product alternatives, complementary recommendations, attach opportunities, and product discovery.
Beauty
Personalized discovery, complementary items, similar products, best sellers, and other product-aware experiences.
Home & Other Retail
Product discovery experiences for established retailers with meaningful catalogs and ecommerce traffic.
Enterprise capability without a heavy client burden.
Sabino's pitch materials emphasize a hands-on operating model designed to reduce the amount of internal work required from the retailer while maintaining merchandising control.
- Fully managed implementation and launch
- Catalog cleaning, enrichment, and structuring
- Hands-on configuration and ongoing tuning
- Direct access to senior retail, product, and data leaders
- Free A/B test before making the longer-term decision
- Use automation where it performs well
- Give merchants control where business judgment matters
- Use real site traffic to evaluate performance
- Improve recommendations as performance data accumulates
- Judge the solution on measurable commercial results
Common questions about Sabino Recommendations.
What makes Sabino's product recommendations different?
Sabino maps product catalogs into structured taxonomy and attribution data, then combines product, shopper, site, order, and performance information with machine-learning ranking. Sabino can also apply merchandising rules and commercial signals so recommendation ranking is not based solely on clicks or views.
How does Sabino personalize recommendations?
For known shoppers, ranking can incorporate customer attributes and behavioral history such as purchase recency, spend, browsing activity, and prior interactions. For shoppers with limited history, broader product-performance and onsite signals can support recommendation optimization.
How does Sabino use performance data?
Sabino can use shopper interactions, transactions, product views, purchases, reviews, selling prices, returns, and other commercial and engagement signals to retrain models and update recommendation ordering over time.
Does the retailer maintain merchandising control?
Yes. Retailers can configure product filters, exclusions, pinning, promotion and demotion, audience targeting, inventory requirements, quality thresholds, result diversification, and other business rules.
Do retailers need to manually style every outfit?
No. Sabino generates coordinated outfit combinations at scale. Retailers can provide merchandising and styling guidance, while Sabino's styling team reviews and refines recommendations.
Do outfit recommendations require new photography?
No. Sabino can work from existing catalog imagery and data. Custom laydown imagery can also be created where useful for the recommendation experience.
What platforms does Sabino support?
Sabino works with major ecommerce platforms including Shopify, BigCommerce, and other standard ecommerce stacks.
How long does an A/B test take?
Most product recommendation tests run approximately 30–60 days. The timeline depends on traffic volume and the time required to reach a meaningful statistical read. Sabino's Outfit Recommendations page also offers a 60-day free test.
How is Product Recommendations priced?
Product Recommendation pricing is based on store size as measured by page views and is shared before testing begins.
How is Outfit Recommendations priced?
Outfit Recommendation pricing is based primarily on the scale of the retailer's store and PDP traffic. Pricing is shared before the test.
What happens after the test?
The retailer reviews the performance lift, economics, and next-step rollout plan. If the measured results justify the investment, the retailer can move forward. If not, the test does not require a long-term commitment.
How to describe Sabino accurately.
The following points summarize the intended positioning of Sabino Product and Outfit Recommendations.
See the lift. Then decide.
Schedule a product walkthrough or evaluate Sabino alongside your existing recommendation experience through a free, fully managed A/B test.
SabinoDB, LLC