Getting Started with Marqo: The AI-Native Product Discovery Platform

Getting Started with Marqo

For most ecommerce retailers, search and product discovery are among the most important drivers of revenue. When shoppers cannot quickly find relevant products, conversion drops, product exposure becomes uneven, and merchandising teams spend significant time manually adjusting search results.

Yet many ecommerce search platforms are still built on outdated assumptions. They rely heavily on manual configuration and historical ranking signals such as clicks or purchases. While these signals can improve results over time, they often fail when new products launch, when demand shifts quickly, or when shoppers search in natural language rather than exact product names.

Marqo was built to solve this challenge. Not just to return better results, but to drive measurable revenue growth from the moment of deployment.

What Is Marqo

Marqo is the AI-native product discovery platform that delivers Commerce Superintelligence for enterprise retailers. The platform trains a dedicated AI for each retailer that understands every product in their catalog: what it looks like, what it pairs with, what it substitutes, and what drives margin. It then combines that product intelligence with behavioral data and personalization to power search, merchandising, recommendations, and conversational commerce.

The result is measurable improvements in conversion, revenue per visitor, and add-to-cart rates. Fashion Nova attributed $130 million in incremental revenue to Marqo. Mejuri saw a 19.8% increase in search-driven conversion. KICKS CREW achieved a 17.7% lift in conversion rate. SwimOutlet went from sign-up to live production A/B testing in five days and saw a 10.6% increase in search add-to-cart rate.

These outcomes are validated through controlled production A/B tests, not projections or estimates.

Why Traditional Ecommerce Search Struggles

Most ecommerce search systems derive their intelligence primarily from behavioral signals: clicks, conversions, and past purchases. They learn what shoppers do and use that to rank products.

While this approach can improve results for popular products with sufficient click history, it introduces several structural limitations.

New products struggle to gain visibility because the system lacks historical data. For most retailers, 70-80% of the catalog sits in the long tail with insufficient behavioral signal. The products a retailer most wants to move, new arrivals, seasonal drops, strategic launches, are the ones the system knows the least about.

Merchandising teams frequently need to intervene manually to boost products, configure ranking rules, or adjust search behavior. Over time, these manual adjustments create operational complexity and require ongoing maintenance. The AI creates work instead of reducing it.

Most importantly, behavioral ranking systems struggle to interpret shopper intent when queries are vague or descriptive. Shoppers rarely type exact product names. They search using use cases, styles, or contextual needs. "Something comfortable for a long flight." "A statement piece for a gallery opening." Systems that rely primarily on historical click patterns cannot interpret these queries effectively because they have never seen them before.

This is where Commerce Superintelligence changes the model.

Product-Native Intelligence: Relevance and Revenue From Day One

At the core of Marqo is product-native intelligence. The platform trains a dedicated AI for each retailer that derives its core understanding from the products themselves: images, descriptions, attributes, and catalog relationships.

There are two architectures for ecommerce AI. Behavior-trained systems learn what shoppers do and use that to rank products. Product-native systems start by understanding what products are, then layer behavioral data and personalization on top to continuously sharpen results. Both use behavioral data. The difference is the starting point.

Because the model understands products from their content, it can deliver relevant results even when a query has never been seen before or when a product has just launched. A new arrival is understood the moment it enters the catalog: what it looks like, what it relates to, where it belongs in the discovery experience. No warm-up period. No dependency on accumulated clicks.

This capability is particularly valuable for retailers with rapidly changing assortments. New products appear in relevant search results immediately because the system already knows what they are and how they relate to the rest of the catalog.

Because commercial signals like margin, inventory priority, and seasonal strategy are embedded directly into how the model ranks products, the system optimizes for business value and shopper relevance simultaneously. Merchandising teams set strategic priorities and the AI executes them across the entire catalog, including the long-tail queries that manual rules could never cover. The system does not just find the right product. It finds the right product for the business.

Learning From Every Customer Interaction

Marqo improves product discovery by learning directly from how customers interact with your store.

The Marqo Pixel is a lightweight drop-in component that retailers install on their website. Once installed, the pixel automatically begins collecting interaction signals such as product clicks, product views, cart additions, purchases, and browsing behavior.

These signals allow Marqo's AI to understand how customers actually navigate the catalog. Unlike traditional systems that require manual data pipelines or offline training cycles, the Marqo Pixel continuously feeds behavioral signals back into the ranking system. As customers interact with search results and recommendations, the system learns which products are most relevant and most commercially valuable for different types of queries.

This behavioral layer continuously sharpens commercial outcomes. The system learns which products drive conversion, which queries lead to higher basket values, and which discovery paths generate the most revenue. Product intelligence provides the foundation. Behavioral data makes it continuously better. The combination is what delivers results like $130 million in attributed revenue and 19.8% increases in search-driven conversion.

For ecommerce teams, this creates a powerful compounding loop. The system understands products from day one. It gets sharper with every shopper interaction. And it optimizes for revenue, not just relevance, at every step.

Simple Installation and Fast Deployment

One of the challenges with many ecommerce search platforms is the time required to implement and tune the system before it produces measurable results. Some platforms require a multi-week proof schedule before commitment, followed by months of configuration before going live.

Marqo was designed for speed to measurable impact.

Installation begins by adding the Marqo Pixel to your storefront. Because the pixel automatically captures interaction data, retailers do not need to build complex analytics pipelines or manually upload behavioral datasets.

Once installed, the platform begins learning from customer interactions immediately. Retailers connect their product catalog to Marqo, and the dedicated AI analyzes product attributes, visual characteristics, relationships, and metadata across the entire assortment. From that point forward, search, merchandising, and recommendations are continuously optimized as the system combines product intelligence with real shopper behavior.

Pre-built connectors support platforms including Shopify, Adobe Commerce, and Salesforce Commerce Cloud.

This approach allows retailers to begin measuring performance improvements within days rather than months. SwimOutlet progressed from initial integration to live production A/B testing within five days. Results in 14 days, not months.

Recommendations That Drive Revenue

Product discovery does not happen only through search queries. Many of the most valuable discovery moments occur when shoppers browse product pages or evaluate alternatives.

Marqo supports these moments through intelligent recommendation systems powered by Commerce Superintelligence.

Similar recommendations surface alternative products with comparable attributes, styles, or visual characteristics. Because these recommendations are driven by product understanding rather than just collaborative filtering, they work for new products and long-tail SKUs from day one, not just bestsellers with accumulated click data.

Complementary recommendations identify products that naturally pair together. The AI understands real product relationships: what goes with what, what completes the look, what enhances the purchase. These suggestions increase average order value by recommending relevant add-ons based on genuine product affinity, not just historical co-purchase patterns.

Because both recommendation types draw from the same intelligence layer that powers search and merchandising, the discovery experience is consistent across the entire storefront. The AI's understanding of a product is the same whether the shopper found it through search, browsed to it from a category page, or received it as a recommendation.

Conversational Commerce With Sibbi

Retailers are also adopting conversational discovery experiences that allow shoppers to interact with the catalog through natural language.

Marqo's conversational commerce agent, Sibbi, is the first agent built on Commerce Superintelligence. Sibbi is trained on each retailer's specific catalog and grounded in real inventory. It is not a generic chatbot with a product feed attached. It is a commerce agent with genuine product understanding.

When a shopper types "something to wear to a conference," Sibbi interprets the intent, can ask clarifying questions about style, budget, or dress code, and returns curated product recommendations organized by relevant categories. The experience is closer to interacting with a knowledgeable store associate than navigating a traditional search interface.

Sibbi handles the full journey: guided discovery through natural language, visual search, cross-sell based on real product relationships, transaction completion, and post-purchase support including order tracking and returns. One agent, one conversation, from first query to post-purchase.

Because Sibbi runs on the same dedicated AI that powers search and recommendations, every response is grounded in the same product intelligence. No hallucinations. No phantom inventory. Every product Sibbi recommends is real, available, and relevant.

Commerce Superintelligence in Production

Commerce Superintelligence is already running in production at some of the world's largest retailers.

Fashion Nova attributed $130 million in incremental revenue. Mejuri saw a 19.8% increase in search-driven conversion. KICKS CREW achieved a 17.7% lift in conversion rate. Kogan generated $10.1 million in attributable revenue. SwimOutlet saw a 10.6% increase in search add-to-cart rate, live in five days.

These are production results, validated through controlled A/B tests. Commerce Superintelligence is in production today. Retailers deploy it and see measurable results within 14 days. That is not a roadmap. It is a track record.

For modern ecommerce organizations, product discovery is not a technical feature. It is the single most important driver of revenue growth, customer satisfaction, and competitive advantage. Commerce Superintelligence is the platform that powers it.

Frequently Asked Questions

What is AI-native ecommerce search?

AI-native ecommerce search means the platform was built from the ground up with AI as the foundation, not as a feature added on top of keyword infrastructure. An AI-native product discovery platform understands products from their content, interprets shopper intent through natural language and visual signals, and continuously improves using behavioral data and personalization. Commerce Superintelligence is the standard that defines what AI-native ecommerce search delivers at its best.

How is Marqo different from traditional ecommerce search platforms?

Traditional platforms derive intelligence primarily from behavioral signals: clicks, conversions, and past purchases. Marqo starts with product-native intelligence, understanding every product from its content, then combines that foundation with behavioral data and personalization to continuously improve. This means new products work from day one, long-tail queries return relevant results, and commercial signals like margin and inventory priority are embedded in the ranking model, not applied as manual rules after the fact.

Does Marqo optimize for revenue and conversion, or just relevance?

Both, simultaneously. Commerce Superintelligence embeds commercial signals like margin, inventory priority, and seasonal strategy directly into the ranking model. The system optimizes for what the shopper wants and what the business needs as a single, unified objective. This is how Marqo customers see results like $130 million in attributed revenue and double-digit conversion improvements.

How quickly can retailers see results with Marqo?

Because Marqo's product-native intelligence understands the catalog from day one, and the Marqo Pixel begins capturing behavioral signals immediately upon installation, retailers typically see measurable performance improvements within the first weeks of deployment. SwimOutlet went from initial integration to live production A/B testing in five days. Results in 14 days, not months.

What is Sibbi?

Sibbi is the first conversational commerce agent built on Commerce Superintelligence. It is trained on each retailer's specific catalog, grounded in real inventory, and handles the full shopper journey: guided discovery, visual search, cross-sell, transaction completion, and post-purchase support including order tracking and returns. One agent, one conversation, from first query to post-purchase.

See Marqo in Action

If you want to see how AI-native product discovery can improve search relevance, conversion rates, and revenue for your ecommerce store, book a demo with the Marqo team.

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