Product Recommendations
AI-powered product recommendations that convert.
SabinoDB's recommendation engine combines machine learning rankings, real-time personalization, and configurable business rules to surface the most relevant products across every page of your storefront.
Trusted by leading e-commerce and retail brands
WHY SABINODB?
Built by retailers, for retailers.
Most recommendation engines were built by engineers alone. We built ours with engineers and people who've spent their careers inside apparel retail — and know exactly where the hard problems live.
01
Surface the right products, at the right time.
You've built an amazing assortment. We built a system that knows how to show it.
Proprietary AI that matches your product data to customer intent
Learns from every interaction, every day
Works across every page — home, PDP, cart, post-purchase
02
Optimize for profit, not just revenue.
Traffic isn't getting cheaper. Shopper attention isn't getting longer. Most engines optimize for clicks — which sounds right until you realize clicks don't pay the bills.
Every recommendation ranked by expected contribution margin
Real data scientists. State-of-the-art ML
Continuous learning — smarter with every interaction
03
Control when you need it. Out of the way when you don't.
Merchants know things algorithms don't — a brand push, a clearance priority, a product that needs a moment. We built for that.
Merchandising rules for brand, category, gender, material, and more
Pin priority products. Exclude what doesn't fit
Simple interface. No engineering degree required
04
The best partners in retail.
100+ years of combined apparel and fashion e-commerce experience. Enterprise-grade capability, built for growing brands.
Real operators who've lived the problems you're solving
Fast setup — live in weeks, not months
No chatbots. No ticket queues. Real people
RECOMMENDATION MODELS
Ready to use algorithms.
Choose from over 10 AI-powered algorithms to suit your business goals.
Similar items
Surface alternative options to the product a shopper is viewing, ranked by expected profit. Ideal for product detail pages where shoppers want to explore before committing.
Complementary items
Recommend products from categories that pair naturally with what's on screen — shoes with outerwear, a bag with a dress.
Frequently bought together
Surface items most often purchased in the same session as the current product, at the moment of highest intent. Works especially well on cart and add-to-cart pages to grow basket size.
Recommended for you
Deliver a personalized feed based on each shopper's browsing and purchase history. Recommendations update daily so returning visitors always see something relevant.
New arrivals
Highlight your newest products, ranked by popularity within each customer segment. Configure the lookback window to match your launch cadence.
More in this color
Show shoppers other products in the same color family as the item they're viewing — across categories — so customers can discover their favorite colors throughout your catalog.
Best sellers
Surface the top-performing products in general or within the collection a shopper is browsing, ranked by expected profit rather than raw volume.
Top rated
Promote your highest-reviewed products, with configurable minimum rating and review count thresholds so only genuinely well-loved items make the cut.
FILTERS & CONTROLS
Full control over every recommendation.
Easily set up rules to fine-tune what gets recommended, to whom, and where it appears.
Price
Set a price range for recommended products and choose whether to include or exclude items currently on sale.
Inventory level
Exclude products that fall below a minimum stock threshold so shoppers only see items available to purchase.
Reviews & ratings
Set a minimum rating or review count to ensure only well-reviewed products appear in recommendations.
New arrivals
Automatically surface new arrivals to align with product launches and campaigns.
Merchandising rules
Refine which products are eligible by brand, category, gender, material, keyword, or product ID — and pin your most important items to the top.
Audience targeting
Tailor product recommendations to specific audience segments and demographics.
Product discovery
Balance relevance with discovery by controlling how similar or varied your recommendations are, and how they refresh across page views.
Mix & match
Assign a different recommendation strategy — or a specific product — to each position in your widget, so every slot works harder for your business.
Frequently Asked Questions (FAQ)
Here is a breakdown of some common questions about Product Recommendations.
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Sabino works with all major e-commerce platforms including Shopify, BigCommerce, and others. If you're running a standard stack, we can work with it.
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We typically start with a free, fully-managed A/B test — no disruption to what you have today. We handle integration and setup, run Sabino alongside your existing recommendations, tune based on your merchandising inputs, and deliver a clear performance readout. You see the results before you make any commitment.
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Most tests run 30–60 days. We align on the timeline upfront based on your traffic volume and what's needed to reach statistical confidence.
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Simple: if the results justify it, you move forward. If not, no hard feelings. Pricing is shared from the start so there are no surprises — by the time the test is done, you'll know the lift and you'll know the cost. The ROI math is clear before you sign anything.
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Most engines rank products based on clicks and views. Ours uses a proprietary data pipeline that maps your product catalog to a structured taxonomy and attribution system, then applies state-of-the-art ML across product, site, order, and customer data. The result is recommendations ranked by expected profit — personalized for known customers and optimized for anonymous visitors alike.
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Pricing is based on your store size and is shared upfront. We'll update this section shortly — in the meantime, reach out and we'll walk you through it directly.
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Sabino is built for direct-to-consumer brands with established product catalogs, typically doing $5M or more in annual revenue. If you're early stage or running a small catalog, we're probably not the right fit yet — but we're happy to have an honest conversation about it.
Ready to personalize your storefront?
Book a demo today to see how SabinoDB's recommendation engine can increase discovery, basket size, and conversion across every page.