Marqo vs Coveo: AI-Native Ecommerce Search vs Enterprise Search Platform

Overview

Coveo and Marqo both serve enterprise ecommerce teams evaluating product discovery platforms. They share surface-level capabilities: AI-powered search, merchandising controls, recommendations, and personalization. But the platforms come from fundamentally different origins, and those origins shape everything about how they work.

Coveo was founded in 2005 as an enterprise search company, originally a spinoff from Copernic desktop search technology. Its core is a keyword-based inverted index with machine learning models layered on top for re-ranking and personalization. Over the past two decades, Coveo has expanded across four verticals: Commerce, Service, Website, and Workplace. Ecommerce is one of four business lines, not its sole focus.

Marqo was founded by former Amazon engineers Jesse Clark and Tom Hamer who recognized that ecommerce product discovery required an entirely new architecture. Keyword-based platforms could not understand shopper intent. Behavior-dependent platforms could not rank new products without accumulated click history. Marqo pioneered product-native intelligence: proprietary ecommerce models that process text, images, and product attributes within a single unified representation of shopper intent. Backed by Lightspeed Venture Partners ($17.8M in funding), Marqo has since produced the largest publicly disclosed revenue uplift for a single retailer in the ecommerce search category and powers discovery for enterprise retailers including Fashion Nova, Mejuri, KICKS CREW, Kogan, and SwimOutlet.

This comparison examines how the platforms differ across architecture, search quality, multimodal capabilities, merchandising, implementation, customer results, and who each platform serves best.

Architecture: Retrofitted AI vs. AI-Native

This is the most important difference between the two platforms, and it affects everything downstream.

Coveo's architecture starts with a traditional keyword search engine built in 2005. Over time, Coveo added Automatic Relevance Tuning (ART), a machine learning model that re-ranks search results based on user click behavior. More recently, Coveo has added semantic search capabilities, passage retrieval, and generative AI answering. Each of these capabilities was added as a layer on top of the existing keyword foundation.

This is a common pattern in enterprise search: start with keyword matching, add ML re-ranking, then add semantic search as a separate layer. The result is a hybrid system where keyword retrieval generates the initial candidate set and machine learning adjusts the ranking afterward.

The limitation is structural. If the keyword layer fails to retrieve a relevant product in the first place, no amount of re-ranking can surface it. For queries where shoppers use natural language, describe a style or aesthetic, or search by intent rather than product name, keyword retrieval systematically misses relevant results. Re-ranking an incomplete candidate set cannot solve a retrieval problem.

Marqo has developed the leading proprietary AI models for ecommerce, trained exclusively on retail product understanding. The retrieval layer itself is powered by these models, which understand product semantics, visual attributes, and shopper intent natively. The AI is the retrieval system itself. Text queries, image inputs, and product attributes are processed within a single unified model, which means the system retrieves based on what the shopper means and what the product actually is, not on keyword overlap between a query and a product title.

This matters most for the queries that drive the most revenue: vague, intent-driven, long-tail queries where shoppers are discovering rather than navigating. Queries like "minimalist walnut desk for a small apartment" or "quiet luxury wedding guest outfit" require the retrieval system to understand what the shopper means, not just match keywords against product titles.

Search Quality

Coveo's Automatic Relevance Tuning learns from behavioral signals: clicks, add-to-carts, and purchases. Over time, this improves ranking for queries that receive significant traffic. The system also offers query suggestions, dynamic facets, and search-as-you-type.

Platforms that rely primarily on behavioral signals to rank products face a structural challenge: new arrivals, seasonal drops, and long-tail items with limited interaction history receive less intelligent ranking until sufficient shopper data accumulates. For retailers with large, frequently changing catalogs, this limitation directly affects which products shoppers see.

Marqo's search quality is built on three foundations. First, product-native intelligence: the AI has seen every product in the catalog. It understands visual attributes, text descriptions, and product relationships simultaneously inside a single unified model, independent of how accurately a merchandiser tagged them. This means every product is understood from the moment it enters the catalog, before a single shopper interacts with it.

Second, retailer-specific model training through Marqtune. Rather than applying a general commerce model, Marqo trains a dedicated AI on each retailer's specific catalog. These models learn the vocabulary, product relationships, and visual characteristics unique to that business. In published benchmarks across a dataset of over 4 million ecommerce products, Marqo's models outperformed Amazon Titan by 38.9% on standard relevance measures (MRR), with retailer-specific fine-tuning producing 73% to 78% additional relevance improvement over generic baselines.

Third, continuous behavioral learning. Marqo ingests clickstream and purchase data to continuously refine rankings over time, building on top of the product-native foundation rather than depending on it as the primary ranking signal.

The practical result: Marqo delivers strong relevance from day one and gets stronger as behavioral data flows in. Fashion Nova reported $130M in incremental revenue after deploying Marqo. Kogan reported $10.1M in incremental revenue and a 20.4% increase in purchase conversion rate. SwimOutlet achieved a 10.6% increase in search add-to-cart rate within five days of going live.

See the full results on our Customer Stories page.

Visual and Multimodal Search

For categories where appearance drives purchase decisions, visual search is not a feature checkbox. It is core infrastructure.

Coveo lists visual search as a capability on its commerce solutions page. However, Coveo's visual search exists as a separate feature within a platform whose core architecture was built for text-based keyword retrieval. Image understanding and text understanding operate as distinct systems rather than as part of a unified model.

Marqo treats multimodal understanding as foundational to the architecture. Text queries, image inputs, and product catalog attributes are processed within the same model. A shopper can search by uploading a photo, typing a description, or combining both, and the system interprets all of these inputs within a single unified representation of intent. Image-to-product matching, visual similarity, and cross-modal search operate without requiring separate modules or integrations.

For retailers in fashion, beauty, home goods, footwear, and luxury, where shoppers frequently search by style, aesthetic, or visual inspiration, the depth of multimodal understanding directly impacts conversion. A system that natively understands visual context produces fundamentally different results than one that offers image search as an add-on to keyword infrastructure.

Merchandising Controls

Coveo provides a Merchandising Hub with tools for boosting, burying, pinning, and creating promotional rules. These controls allow merchandising teams to manually adjust ranking for specific queries or product groups. Coveo's Qubit acquisition in 2021 brought additional personalization and experimentation capabilities into the platform.

Marqo provides comprehensive enterprise merchandising controls through Merchandising Studio, integrated with its AI-native ranking engine. Teams can boost or bury products, pin items to fixed positions, create time-bound campaign rules, segment ranking strategies by geography or customer cohort, and inject sponsored placements within search and browse results.

The difference is how these controls interact with the underlying ranking system. Marqo's merchandising operates alongside a continuous optimization engine that learns toward defined business objectives. Teams can configure multi-objective optimization strategies that balance conversion rate, revenue, margin, inventory priorities, or promotional focus within the ranking logic itself. Explainability and auditing tools allow teams to understand why products rank where they do and measure the impact of merchandising rules versus algorithmic ranking through controlled A/B testing.

The result is scalable control: merchandising teams retain full authority while the AI reduces repetitive manual tuning and handles the long tail of queries and products that no team can manually manage.

Ecommerce Focus vs. Platform Breadth

This is a strategic consideration that goes beyond feature comparison.

Coveo serves four verticals: Commerce, Service, Website, and Workplace. The platform powers customer service portals, internal knowledge bases, corporate websites, and ecommerce storefronts. This breadth means engineering, product, and R&D resources are spread across fundamentally different use cases. The AI models, ranking logic, and feature roadmap serve multiple masters.

Coveo's ecommerce capabilities were partly built organically and partly acquired. The 2019 acquisition of Tooso brought AI commerce engine technology. The 2021 acquisition of Qubit brought personalization and experimentation capabilities. Integrating acquired technology while maintaining a coherent platform across four verticals is an ongoing challenge for any company in this position.

Marqo is built exclusively for ecommerce product discovery. Every model, every feature, and every infrastructure decision is optimized for the specific problem of helping shoppers find products they will buy. The proprietary ecommerce AI models, the fine-tuning infrastructure, Merchandising Studio, the conversational commerce agent Sibbi, and the analytics are all designed around commerce outcomes.

For retailers evaluating search platforms, the question is whether ecommerce product discovery is best served by a focused platform built for that specific problem or by a horizontal platform where commerce is one of several verticals. The answer depends on how central discovery quality is to your business.

Customer Results

Coveo does not publish specific revenue uplift metrics from ecommerce customers. The company references case studies across its four verticals, but publicly available commerce-specific results with named retailers and dollar-denominated outcomes are limited.

Marqo has produced the largest publicly disclosed revenue uplift for a single retailer in the ecommerce search category. Fashion Nova reported a $130M revenue increase. Kogan reported $10.1M in incremental revenue. Redbubble reported $11M in incremental revenue and a 21% increase in add-to-cart rate. Mejuri reported a 19.84% increase in search revenue per user. KICKS CREW reported a 17.7% uplift in conversion rate. SwimOutlet achieved a 10.6% increase in search add-to-cart rate, progressing from initial integration to live production A/B testing within five days.

These are named retailers with dollar-denominated results validated through controlled A/B testing on live traffic. The most reliable way to evaluate the impact for your catalog is to run the same kind of test.

See the full results on our Customer Stories page.

Conversational Commerce

Coveo has introduced generative AI-powered conversational product discovery as a newer capability, using RAG (Retrieval-Augmented Generation) to provide AI-generated answers grounded in product catalog data.

Marqo's conversational commerce agent, Sibbi, is built directly on Marqo's proprietary product-native intelligence. Sibbi is trained on each retailer's catalog and understands every product visually, semantically, and commercially. It asks clarifying questions, interprets images and natural language, cross-sells based on real product relationships, and completes transactions, all grounded in real inventory. Sibbi also extends beyond discovery into post-purchase, handling order tracking, returns, and common support questions without requiring a separate system.

The difference is what powers the conversation. A generative layer over keyword search retrieves what the keyword system can find. A conversational agent built on product-native intelligence retrieves based on genuine product understanding.

Implementation and Time to Value

Coveo is an enterprise platform with enterprise implementation requirements. Deployments typically involve professional services engagements, custom integration work, and multi-month timelines. Pricing is custom-quoted and not publicly available, consistent with Coveo's enterprise-sales positioning.

Marqo is designed to reduce friction between integration and measurable results. Deployment begins with a lightweight tracking pixel that captures behavioral signals without deep engineering changes. Pre-built connectors support platforms including Shopify, Adobe Commerce, and Salesforce Commerce Cloud. In published case studies, retailers have progressed from initial integration to live production A/B testing within days. SwimOutlet went from sign-up to live results in five days.

For teams where speed to measurable impact matters, the difference between days and months of implementation directly affects time to ROI.

Pricing

Coveo does not publish pricing. The company uses a sales-driven, custom-quote model. Industry reputation positions Coveo as one of the more expensive options in the enterprise search category. Coveo is publicly traded (TSX: CVO) with approximately $148M in annual revenue, providing some transparency into the scale of its operations.

Marqo offers transparent pricing aligned with usage and catalog size. For retailers evaluating total cost of ownership, transparent pricing reduces procurement friction and allows clearer ROI modeling before commitment.

Who Each Platform Is For

Coveo is well-suited to large enterprises that need a single platform spanning customer service, knowledge management, corporate search, and ecommerce, particularly organizations already using Coveo in other verticals that want to extend to commerce within the same ecosystem.

Marqo is the enterprise product discovery platform for retailers where search directly drives revenue. It powers catalogs with over 15 million SKUs across fashion, beauty, home goods, footwear, luxury, electronics, and marketplaces. Retailers choose Marqo for its product-native intelligence, multimodal understanding, retailer-specific model training, and the largest publicly disclosed revenue results in the category. KICKS CREW, Mejuri, Kogan, Redbubble, and SwimOutlet all went from integration to measurable revenue impact within weeks.

If you are evaluating search platforms, test Marqo on your own catalog and discover why leading enterprise retailers are making the switch. Book a Demo

This comparison was developed using publicly available information including company websites, published case studies, press releases, SEC filings, and analyst reports as of May 2026. Platform capabilities and positioning evolve over time. We encourage readers to verify current offerings directly with each vendor. All Marqo customer results are based on controlled A/B testing on live traffic. Individual outcomes vary based on catalog structure, traffic volume, implementation approach, and business context. We recommend running a controlled test on your own catalog to evaluate expected impact.