Magic Quadrant for Search and Product Discovery
24 June 2025 - ID G00823079 - 43 min read
By Mike Lowndes, Noam Dorros, and 2 more
Innovations in search and product discovery can be adopted without rebuilding entire digital commerce platforms. Digital commerce leaders can use this research to understand the commerce search market and support related product discovery initiatives.
Strategic Planning Assumption
During 2026, at least one new generative AI (GenAI)-based conversational UI pattern in search and product discovery (S&PD) will disrupt traditional search and browse UIs, penetrating at least 5% of the market.
Market Definition/Description
Gartner defines search and product discovery as applications that augment digital commerce solutions to facilitate navigation, filtering, comparisons and, ultimately, selection of products. They provide search (keyword, semantic and visual), merchandising (automation, configuration and curation of business rules) and product recommendations. These applications also provide catalog navigation (including SEO keyword automation and guided selling assistants). Personalization, optimization and analytics capabilities should also be available. Platforms are deployed as SaaS. They provide administrative tooling to enable digital commerce roles (merchandisers, content managers and search specialists) to support customer experiences via no-code. With the emergence of generative AI, conversational search and guided selling assistants are now appearing.
Search and product discovery applications can provide the digital customer journey from landing on a website or app to finding the correct product and adding to basket. Search results can be highly visual, using engaging layouts and multimedia. Content other than product information, such as educational information, compliance materials, customer reviews and related news may also be included in search results to engage customers and further support buying decisions.
Mandatory Features
The mandatory features for this market include:
- Product search (keyword)
- Semantic search support (natural language processing [NLP] and/or vector)
- Type-ahead/rich autocomplete
- Search analytics
- Results personalization
- Rule-based, curated and algorithmic merchandising
- Product recommendations (contextual, product substitution, complementary, etc.)
- Catalog navigation (browse) replacing a static taxonomy up to (but not including) product detail pages (PDPs)
Common Features
The common features for this market include:
- Advanced semantic search via natural language technologies, vector embeddings and/or graphs to understand query intent and map products to that intent
- Automated SEO content creation for product lists for category landing pages and subcategory product listing pages (PLPs)
- Productized integrations to digital commerce platforms
- Content search and other content types integrated with product search
- Optimization: A/B and multivariate testing (MVT) of ranking rules and merchandising
- Visual search
- Guided selling assistants
- On-site retail media support
- Catalog enrichment/product data normalization
Magic Quadrant
Vendor Strengths and Cautions
Algolia
Algolia is a Leader in this Magic Quadrant. Its multitenant SaaS product runs on AWS and Microsoft Azure, plus GCP for AI modeling and analytics. It is API-first and modules include AI Search, AI Browse, AI Recommendations, Merchandising Studio and Analytics. It also offers general site content and enterprise search. Making the most of the platform requires front-end and API development skills, and it has an extensive developer hub.
Pricing is API-usage-based (request and API consumption), with volume discounts available. API use is free for developers with 1 million SKUs. Pricing is partially publicly available.
Headquartered in Palo Alto, California, Algolia has a strong presence in Europe and North America, and a growing presence in Asia/Pacific and Latin America. It serves a broad range of industries, targeting midtier to large enterprises.
- Next best actions: Action-oriented analytics, derived from clickstream data and AI engines, provide suggested opportunities for optimization, such as dynamic reranking, synonyms or rules.
- Vendor growth: Despite a flat market, Algolia grew the most among vendors in this Magic Quadrant in 2024, indicating ongoing customer trust in this vendor’s products.
- Language: Its core engine supports over 60 languages out of the box. No additional customization is required for Algolia to work with multiple languages as part of global rollouts.
- B2B support: The product includes parts search, replacement and substitutions in e-commerce, but some B2B-specific capabilities, such as account-based search and search personalization, are limited compared to those of some competitors.
- GenAI integration: While some competitors are progressing toward a single search journey supported by GenAI, Algolia currently lacks this integration, and conversational and search remained separate entities at the close of this research.
- Automated merchandising: AI-based automation is not incorporated into merchandising, putting Algolia behind the curve compared to some competitors.
Algonomy
Algonomy is a Niche Player in this Magic Quadrant. Its multitenant SaaS solution runs on AWS. Modules include Find (search), Discover, Recommend, Engage (guided selling) and Automated Sales Assistant (conversational), which can also power guided-selling questionnaires. Its S&PD offering works both as a stand-alone and as part of a wider personalization suite.
Pricing depends on the modules used as well as API calls, the number of SKUs and page views.
Headquartered in San Francisco, California, and Bangalore, India, it has a global presence (excluding China). It serves a range of industries, primarily targeting midsize and large retail organizations.
- Personalization: Extensive search personalization capabilities (e.g., recommended ensembles [outfit styling/bundle curation]) that can also be used with general content and preview ability make this solution a good fit for organizations requiring broad personalization beyond search.
- Search management: The platform provides detailed configuration options and uses “wisdom of the crowd” (i.e., aggregated customer data) for product relevance and ranking.
- UI: The business tooling is intuitive and easy to use — especially the visual merchandising features and the analytics dashboard.
- Growth: In 2024, overall growth, in terms of both customers and revenue, was low compared to other vendors. Prospects should consider the vendor’s viability and ability to innovate in a tough market.
- B2B functionality: Algonomy lacks some key capabilities for supporting enterprise B2B clients, such as sophisticated catalog and pricing segmentation, and unit-of-measure conversion. B2B customers should carefully evaluate its available capabilities and customer references.
- AI deployment: Overall use of AI within the product is weak outside the Recommendations module — such as in merchandising and analytics, where configurations are primarily rule-based.
Athos Commerce
Athos Commerce is a Niche Player in this Magic Quadrant. It is the result of a merger between Searchspring and Klevu in 1Q25.
Its multitenant SaaS solution, (currently branded Searchspring), runs on AWS. Modules include Site Search, Merchandising, Personalization, and Reporting and Insights, with optional B2B support, navigation, recommendations and optimization. A Product Data Feed Management platform and Intelligent Reach are add-ons.
Pricing is tiered, combining a fixed fee and usage; details are publicly available.
Headquartered in San Antonio, Texas, it has a strong presence in North America and Asia/Pacific (mostly Australia). It targets SMB and midtier segments, primarily in retail, but it also serves automotive, healthcare and high tech.
- Time to market: The solution is packaged for broad and retail-centric customers, and it has a quick implementation timeline, which will suit organizations looking for quick ROI for straightforward use cases.
- Pricing model: All capabilities are offered in three tiers, with publicly available pricing and packaging information — which is ideal for customers that don’t have a large SKU count or complex site operational requirements.
- Ease of use: Its business tooling is comprehensive and rule-based; however, its simple UI and ease of use ensures new users can onboard quickly to use the platform.
- Regional focus: Geographic coverage remains low outside North America and parts of Asia/Pacific. It has only a small presence in Europe. Prospects outside the core regions must consider the availability of implementation partners and product support.
- Industry presence: The customer base primarily comprises high-growth SMBs and midtier brands in the retail vertical. The company has limited presence in other industry verticals and is best suited to B2C brands and retailers.
- Advanced search and AI: The platform offers few advanced search capabilities, such as semantic search and merchandising. It also lags competitors in its incorporation of GenAI.
Bloomreach
Bloomreach is a Leader in this Magic Quadrant. Its multitenant SaaS product, Discovery, is available on AWS and GCP, with Autonomous Search (includes merchandising), Conversational Shopping (known as Clarity), Recommendations, and SEO. It uses Google Vertex AI Search and its own vector database as well as Apache Solr-based keyword search blended into hybrid search via Bloomreach’s Loomi Search+.
Pricing is a flat fee plus SKU count, expected API calls and visual search usage. Add-ons include general site content search, support, additional environments and other Bloomreach commerce solutions.
Headquartered in Mountain View, California, it is strong in North America and solid in EMEA but has little presence in other regions. It mostly serves midsize to large B2C retail organizations but also has customers in B2B manufacturing and distribution.
- Broad go to market: Bloomreach’s primary GTM vehicle is a commerce experience platform, providing the presentation layer, product discovery and customer data platform for headless digital commerce platforms. This broad approach differentiates it for those looking for a wider capability set.
- KPI focus: For KPIs and GTM, it recently switched its focus from relevance to ROI, which aligns with market expectations in S&PD that success is measured via business outcomes rather than pure technical capability.
- Hybrid search: With vector search now commonly available, Bloomreach has moved its focus in Loomi Search+ to how traditional keyword search and NLP are blended with vector search results.
- Ecosystem: A downside of the breadth of Bloomreach’s offering is a tendency to look inward, and its ISV ecosystem is smaller than other leading vendors.
- Expansion: Bloomreach’s industry breadth remains low. The product now performs well for B2B, but the perception of Bloomreach as primarily for retail may limit awareness among potential B2B prospects.
- GenAI integration: Although Bloomreach Conversational Shopping is well-established and utilizes Google Gemini to power the digital shopping assistant (Clarity), the integration of GenAI features into the core search workflow lags other vendors.
Constructor
Constructor is a Leader in this Magic Quadrant. Its multitenant SaaS platform runs on AWS or GCP but is cloud-agnostic, so it could be run on other public and private clouds. Modules include Search & Autosuggest, Browse, Collections, Recommendations, Attribute Enrichment, Quizzes, AI Shopping Assistant and Sponsored Listings. It received a $24 million Series B investment in 2024.
Pricing is a flat subscription fee and invoicing is driven by forecast API calls, which is determined through a multiweek POC to estimate usage and immediate ROI. Additional modules require reassessment.
Headquartered in San Francisco, California, it is strong in North America and growing in Europe. It mostly serves large global retail organizations but also has automotive, wholesale and distribution customers, including midsize enterprises.
- Vision: Constructor focuses on commerce and is adding innovative capabilities, including LLM filtering, agentic AI, retail media integration and connecting product discovery.
- Growth: It is among the fastest-growing vendors in this Magic Quadrant despite a flat market. The new funding has further fueled its product development and market expansion.
- AI at the core: The AI merchandising capability is designed to require manual curation by exception only. The platform offers commerce-focused, vertical-specific LLMs, a personalization neural network for better context and intent relevance, as well as AI ranking and scoring transparency.
- Sales and customer experience strategy: Constructor’s rapid growth is from a small regional base, and It is less established compared to major competitors, especially outside North America. It has a limited customer acquisition strategy and its own customer event and developer community.
- Market presence: Its customers are mostly in North America and retail. It has a growing presence in EMEA, and small but growing presences in APAC and LATAM. Presence outside the retail vertical is relatively small.
- B2B functionality: Its B2B capabilities are standard and lack sophistication in managing complex pricing and product grouping.
Coveo
Coveo is a Leader in this Magic Quadrant. It offers a multitenant SaaS platform running on AWS across North America, EMEA and Asia/Pacific (Australia), and modules include Search and Autocomplete, Product Listings, Recommendations, Badging and Coveo ML Relevance Generative Answering.
Pricing is driven by the number of queries, products, recommendations, documents, page views and environments. Relevance Generative Answering is priced separately based on the number of queries.
Coveo is headquartered in Quebec City, Canada. It also has a strong presence in North America and to a lesser extent, in EMEA and Asia/Pacific. It serves a range of B2C and B2B industries, and targets larger enterprises and midsize organizations.
- AI vision: Machine learning and LLMs available across the platform improve relevancy, search personalization and generative answers, and Coveo continues to invest in algorithmic capabilities.
- Enterprise fit: Coveo’s ability to support complex business requirements suits larger organizations with complex needs. Many customers have an annual GMV of over $1 billion, and Coveo often partners with application vendors that also target large enterprises, such as SAP, Adobe and Salesforce.
- Catalog support: A unified index combines products, services and content. It supports large numbers of product fields and filtering for price, inventory, buyer group, past purchases, warehouses and more. This is a good fit for organizations with large catalogs and complex products.
- Conversational search: Conversational product discovery experiences are lacking, and the generative Q&A doesn’t provide an inviting experience to encourage deeper engagement for search, browse or dialogue.
- Pricing model: Coveo’s pricing model is consumption-based across six or more capabilities and modules with different consumption parameters and allocations. Prospects may find this complexity difficult to negotiate.
- Required skills: While the platform uses machine learning to automate rules, its parameters are highly granular, and some configurations (such as the semantic model and passage retrieval API) require technical skills.
FactFinder
FactFinder is a Niche Player in this Magic Quadrant. Its primary S&PD product, FactFinder Next Generation, is a hybrid single and multitenant SaaS platform. Although the product discovery core includes navigation, merchandising, personalization, recommendations, Geo (geolocation search), Customer-Specific Info and Predictive Basket, customers have the option to pick their modules, which are priced accordingly. The AI Guided Selling feature uses GenAI.
Pricing is driven by unique initial searches per month, the number of channels and the modules opted in.
Headquartered in Pforzheim, Germany, it has a strong presence in Europe and small customer bases in North America and Asia/Pacific. It serves a range of industries, primarily B2B construction, home goods, fashion and sporting goods. It targets midsize to enterprise businesses.
- B2B focus: The solution is a good fit for B2B organizations with complex catalogs and account-based pricing needs, and the solution offers units-of-measurement conversion support.
- Industry span: FactFinder recently deployed its search and product discovery solution for clients across multiple industries, including travel, retail, construction and pharmacy.
- GenAI capabilities: FactFinder offers GenAI features such as GPT synonyms and AI-guided selling, and an AI assistant is used to generate an advisor experience for creating guided selling campaigns.
- Geographical presence: Outside its core region of Germany, Austria and Switzerland, FactFinder is not well-known and rarely appears in vendor shortlists. Experienced associates and implementation partners may not be available in all geographies.
- Growth: In 2024, its overall growth in terms of customers and revenue was low compared to other vendors in this research. Prospects should consider the vendor’s ongoing commercial performance in a tough market.
- Product innovation: Most core merchandising capabilities lack AI support. Analytics and composability also lag competitors in this space.
Google is a Leader in this Magic Quadrant. Its Vertex AI Search for commerce comprises multitenant SaaS APIs running on Google Cloud as part of a larger AI portfolio. Search, browse and recommendations share one API, and visual search is on a separate API. It leverages other technology services of Google, including LLMs developed from Google Shopping and product-centric knowledge graphs.
Pricing is retrospective, billed monthly based on actual query usage (API calls), with volume discounts. Smaller customers can self-service onboard via Google Cloud standard tooling.
Google is headquartered in Mountain View, California, and maintains a global presence. It has a wide range of B2C customers and is growing in B2B. Uniquely among vendors researched, it also sells components of its search solution to other vendors.
- Conversational search: An out-of-the-box GenAI selling assistant is blended into the core search experience and can enable real-time conversations for finding products. It offers integration with the rest of the product, including personalization.
- Pricing model: Google offers simple pricing. Users pay only for what they consume and only for two APIs, with an opportunity to qualify for volume discounts.
- Blending keyword and semantic search: It maintains 13 relevance models running in parallel, eight of which are semantic and five keyword- or term-based, as well as a separate AI model.
- Search personalization: The platform offers 1:1 search results personalization, which relies on retailer on-site user event feeds, and currently lacks integrations of external signals, such as CDP/CRM integrations and geography-based segments.
- Technical focus: The solution remains focused on organizations with development teams or implementation partners in support. Less advanced customers can leverage some no- and low-code connectors and integration capabilities, but implementation requires a technical team.
- Merchandising AI: Google leans heavily on an AI-first approach to merchandising. This focus on AI with basic control — and only basic manual business curation — can be a challenge for some prospects.
HawkSearch
HawkSearch (owned by Bridgeline Digital), is a Challenger in this Magic Quadrant. It provides multitenant SaaS and dedicated managed hosting solutions on AWS, and uses OpenSearch. Modules include Smart Search (i.e., semantic and visual search), Merchandising, Product Recommendations and various industry accelerators.
Pricing is available in three tiers (Core, Premium and Enterprise) based on API calls and SKU count. Implementation cost is included, with add-on features available at the Premium and Enterprise levels.
Headquartered in Des Plaines, Illinois, it maintains a strong presence in North America, a solid base in EMEA and smaller bases in Latin America and Asia/Pacific (excluding China). It serves a range of industries, primarily targeting B2B digital commerce, manufacturers and distributors.
- Search functionality: HawkSearch leans on machine learning, sales and trends, and search relevancy to power product suggestions. It integrates content search capabilities to fully support content-driven experiences.
- Growth: In a generally flat market, HawkSearch continues to grow. Its concentration on small to medium enterprise B2B businesses has earned sound customer and revenue growth in 2024, indicating ongoing customer trust in this vendor’s product.
- B2B proficiency: It provides comprehensive B2B capabilities for unit-of-measurement conversion, SKU search, product grouping with multilayer drill-downs, bundling and child variant management. Entitlement policies can be managed based on accounts, location and inventory availability.
- Merchandising AI: Merchandising is traditionally rule-based and lacks significant use of AI with the exception of the AI Multiplier feature. The lack of AI puts HawkSearch behind leading competition.
- Brand awareness: Its focus on the small to lower end of the enterprise B2B market results in overall weak market awareness compared to that of competitors, which could limit its future growth potential.
- AWS dependence: HawkSearch has a completely AWS-driven architecture, leaving its core search technology and architecture dependent on AWS architecture and releases, which may limit innovation, and it does not enable customers to choose another infrastructure.
Lucidworks
Lucidworks is a Challenger in this Magic Quadrant. Its multitenant or single-tenant SaaS or PaaS solution, Fusion, has “Studios” for AI Apps, Commerce, Knowledge Management and Analytics. It can be deployed on AWS, GCP or Microsoft Azure. Fusion blends Solr keyword search and vector search.
The core license includes all modules, with eight pricing tiers based on records, queries and throughput. LLM hosting, additional environments and premium support cost extra.
Headquartered in San Francisco, California, it is strong in North America, with small bases in Europe and Asia/Pacific. It serves several industries but mostly retail, financial services, manufacturing and high tech. It primarily targets complex global organizations but has some midsize enterprise customers.
- Industry focus: Lucidworks has a broad presence across industry verticals such as retail, financial services, manufacturing, high tech and professional services.
- Enterprise focus: It has large or very large enterprise customers with large catalogs and complex merchandising requirements. Its product has the customization capabilities, sophistication and scalability required for complex solutions.
- Business UI: A new and easy-to-use UI is available for merchandising and analytics dashboards to enable low- and no-code configurations.
- Market awareness: Lucidworks is rarely seen on Gartner client shortlists and general awareness of the brand is lower than leading vendors. Below-average sales and marketing strategies may underpin this challenge.
- Geographic presence: Its customers are mostly in North America, with very few in other regions. Prospects in other regions must consider the availability of product support and implementation skills.
- AI capabilities: While it uses AI for some capabilities, it requires manual tuning between vector and lexical (keyword) search. GenAI capabilities are currently more optimized for content search than product discovery.
Netcore Unbxd
Netcore Unbxd is a Leader in this Magic Quadrant. Its multitenant SaaS solution can run on AWS, GCP or Microsoft Azure. Modules include Search and Browse (with merchandising, analytics and optimization, and conversational AI) and Recommendations. It provides over 20 AI models and industry vertical models and is a hybrid keyword/vector search solution.
Pricing is based on modules purchased plus the number of API calls and SKUs; add-ons for visual search, its product enrichment conversational assistant, front-end solutions, professional services and premium availability cost extra.
Headquartered in San Mateo, California, its headcount is mostly in India. It has a strong presence in North America and small bases in Europe and Asia/Pacific (excluding China and Japan). Most customers are in retail, but it also serves wholesale, automotive and publishing. It primarily targets large global enterprises but also serves midsize businesses.
- Focus: The vendor is focused on digital commerce S&PD and continues to expand into close adjacencies, such as product enrichment and the automation of recommendations for multichannel marketing messaging.
- Availability: Netcore Unbxd is available globally across multiple public cloud infrastructures, enabling truly global rollouts and thus, suits prospects with global operations.
- AI strategy: AI is embedded throughout the product, and customers can bring their own AI models to the product. The breadth of AI capabilities remains a differentiator and is not limited to GenAI; traditional NLP/NER remains a strong feature.
- B2B support: The platform has historically had a strong focus on retail. Although coverage for B2B has improved, it lags other Leaders evaluated in this research for this use case.
- Brand awareness: Overall market awareness remains weak compared to that of competitors, especially in North America, and global marketing efforts lag those of competitors. Prospects should consider the availability of implementation partners and product skills.
- Content search: The search solution remains product-centric. Though general content can be indexed and searched, this feature remains separate, and it is not as well-integrated as by other leading vendors.
Nosto
Nosto is a Niche Player in this Magic Quadrant. Its multitenant SaaS solution, Product Experience Cloud, runs on AWS alongside Content Experience Cloud as part of the Nosto Commerce Experience Platform. Modules include Personalized Search, Category Merchandising, Product Recommendations and Dynamic Bundles. Search is powered by Elasticsearch with vector embedding and basic GenAI features from the underlying Nosto Experience AI platform.
Pricing of Product Experience Cloud is based on GMV or visits and AI training data, plus the modules used. An ROI guarantee within the first three to six months is provided, depending on usage volumes. Implementation support services are additional.
Headquartered in Helsinki, Finland, it has a strong presence in Europe and North America and a growing base in Asia/Pacific. It serves a range of industries, mostly brands and retail, and targets midsize and lower enterprise B2C organizations.
- AI: Nosto has continued to invest in AI, including GenAI, for a broad feature set within the platform and added vector search to power its LLM use (e.g., training industry vertical models) in 2024.
- Language support: Supporting 28 languages out of the box and more than 100 in total, Nosto supports multicountry and multilingual rollouts.
- Search results and recommendations personalization: Nosto focuses heavily on behavioral and transactional product-affinity-based personalization, now further enhanced with AI tools.
- B2B support: Nosto’s B2B penetration remains low, and its feature set is basic at best. B2B prospects must check that the sophistication of expected features is mature enough for their purpose.
- Potential technology overlap: The core platform, Experience AI, is a personalization engine with some CDP features, and Product Experience Cloud is layered over this. Prospects that already have a personalization engine risk supporting duplicate functionality.
- Content and navigation: Content search is available but remains basic, and catalog navigation is not fully supported. Prospects looking to replace digital commerce catalog navigation may need to look elsewhere.
Rezolve Ai
Rezolve Ai is a Challenger in this Magic Quadrant. Its S&PD product, GroupBy (acquired in 1Q25), runs on GCP as multitenant or single-tenant SaaS. It comprises Search AI and Browse, Recommendations AI, and Enrich AI (catalog enrichment). Core search capabilities are powered by Google’s Vertex AI Search for Retail.
Pricing is based on the number of API queries, with Enrich AI costs based on SKUs processed.
Headquartered in London and New York City, it has a strong presence in North America, with smaller bases in Europe, Asia/Pacific and Latin America. It serves multiple industries, but mostly retail, wholesale, distribution and marketplaces. It primarily targets a range of medium and larger enterprise, regional and multicountry organizations.
- New vision: GroupBy’s recent acquisition by Rezolve Ai makes it part of a wider agentic/GenAI platform oriented to the emergence of conversational UIs, including conversational search.
- Pricing strategy: Rezolve Ai uses a simple usage-based model, charging per 1,000 Search and Browse API calls. There are no additional fees for autocomplete, ingestion, catalog size or PDP clicks. Enrich AI is priced per SKU.
- Catalog enrichment: Especially useful in B2B, the Enrich AI module enables the optimization of sparse or poor catalogs for search retrieval, for example, by turning unstructured and/or supplier information and product image features into attributes that can be searched.
- Market reach: Rezolve Ai is rarely seen on shortlists and has lower brand recognition outside North America. Prospects must consider the availability of implementation expertise and product support in their region.
- Google dependency: It has not filled some of the gaps in the Google APIs on which it relies, meaning it shares some limitations, such as lack of user control over the algorithm and segment-based personalization.
- Search personalization: It relies on Google’s clickstream-based personalization, plus some native product recommendation models, with little in the way of testing or optimization capability.
Yext
Yext is a Niche Player in this Magic Quadrant. It offers a multitenant SaaS solution that can be deployed in AWS and GCP. Yext Search comes with Yext Content, a CMS/graph database bundled with all Yext products. It offers basic and advanced search packages, with the latter providing full access to all search capabilities.
Pricing is based simply on the number of queries and document entities.
Headquartered in New York City, Yext has a strong presence in North America and Europe, and a small base in Asia/Pacific. It mainly serves the financial services, healthcare and food services verticals, and it targets organizations of all sizes.
- Use cases: Yext Search is focused on specific use cases such as SEO, page publishing, content listings, reviews, hyperlocalized advertising and chatbots. This can be a good fit for organizations looking for a cost-effective all-in-one content, marketing and search solution.
- Growth: Yext showed good revenue growth in 2024, in the midst of a difficult market climate, indicating ongoing interest in this vendor’s product.
- Content types: It supports various content types, such as video, blog, help, FAQ and locations, which can be combined in the same UI with product results or rendered separately. This is a good fit for organizations with many product-related content types and complex catalogs.
- Technology gaps: Very little AI is used within its product search, merchandising and personalization. Semantic search mostly relies on rules and NER. Semantic search is not available for product searches, only for content search.
- Search investment: Yext’s support of broad, content-centric use cases dilutes its focus on S&PD capabilities, and gaps, such as merchandising, remain in its offering compared to competitors.
- CMS dependence: It relies on Yext Content as its knowledge graph, where it stores search data and manages taxonomy, navigation and catalog segmentation. As a result, product catalogs must sync with the CMS before they are available for search indexing, adding an extra step for users managing their own product catalog.
Zoovu
Zoovu is a Niche Player in this Magic Quadrant. Its multitenant SaaS S&PD product includes Search Studio and Conversation Studio (which has a guided selling assistant), and it is built on the Zoovu Data Platform. It sells additional tools, such as Configuration Studio (for visual configuration) and Advisor Studio (for PDP optimization, in-page question answering and guided selling).
Pricing is tiered, modular and driven by usage, the number of SKUs, engagement and searches, and the modules the customer is buying.
Headquartered in Boston, Massachusetts, Zoovu has offices in Europe and a presence in Latin America. It targets enterprise brands, retailers and B2B distributors, and best matches large enterprises’ complex needs.
- Product enhancements: Zoovu has enhanced its overall product capabilities. It added Zoe, a guided selling assistant, dynamic bundling and catalog management capabilities, and can support PDPs, thus replacing a digital commerce platform catalog.
- Business tooling: It offers an easy-to-use business UI with drag-and-drop options for visual merchandising and GenAI classifiers for generating guided-selling questionnaires.
- B2B focus: Zoovu’s part-finder capabilities and its support for variant groupings offer search capabilities for B2B use cases. Zoovu’s B2B customers are primarily multiregional and in various industry verticals across manufacturing and medical devices.
- Search personalization: Zoovu offers limited search results personalization and product recommendations within its search offerings. Context and user groups are supported but not native segmentation or customer identification. Customers must rely on third-party integrations with personalization engines to meet those requirements.
- Product finder focus: Its go to market has been primarily built around its product finder functionality, and most Zoovu customers use its solution for that. Many fewer use the full suite, which lacks some of the more advanced features for search merchandising and personalization found elsewhere.
- Brand awareness: Due to its focus on product finders, Zoovu is rarely seen on shortlists for clients that are looking for an end-to-end S&PD platform. Prospects should consider their specific needs for the platform.
Vendors Added and Dropped
We review and adjust our inclusion criteria for Magic Quadrants as markets change. As a result of these adjustments, the mix of vendors in any Magic Quadrant may change over time. A vendor's appearance in a Magic Quadrant one year and not the next does not necessarily indicate that we have changed our opinion of that vendor. It may be a reflection of a change in the market and, therefore, changed evaluation criteria, or of a change of focus by that vendor.
Added
No vendors were added to this Magic Quadrant.
Dropped
- Crownpeak.
- Klevu was acquired by Searchspring (now Athos Commerce).
Inclusion and Exclusion Criteria
To be included, each vendor had to satisfy the following, as of 31 March 2025:
- The vendor must actively offer for sale a minimum of one SaaS digital commerce search and discovery product that meets the Market Definition and stated functionality.
- The digital commerce search and discovery product must support over 50 current production customers.
- Vendors must have onboarded more than five customers to the search and discovery product in 2024.
- The search and discovery product must serve customers in more than one unique industry vertical within the digital commerce space. To qualify as serving customers in an industry, the product must have a minimum of 5% of production customers in that industry.
- The digital commerce search and discovery product must be used by paying customers in more than one geographic region.
- Vendors must have ARR software revenue from their digital commerce search and discovery product of more than $9 million.
- ARR software revenue and customer numbers declared for this research must relate to use primarily for digital commerce search and discovery as defined by the Market Definition. Revenue and customers must not be included for deployments unrelated to commerce search and product discovery deployments, such as general site search only or personalized recommendations only.
Honorable Mentions
Crownpeak
Crownpeak sells the Fredhopper discovery platform. Fredhopper is well-established in the S&PD market, especially in EMEA. Fredhopper and Experience Orchestrator (XO) provide a combined search, merchandising and recommendations platform that together offers a multilingual/multisite platform with robust visual merchandising and new conversational AI features. The solution is primarily targeted at larger retailers.
Fractal
Fractal’s product, Flyfish.ai, began as a mobile-first conversational search platform, and now offers an AI-driven multimodal search solution that supports search via text, image and voice and a guided selling product with conversational capabilities. It uses LLMs to enrich product data and supports hybrid search with keyword, NLP/NER and vector search. It has vertical-specific models and mostly targets fashion/beauty retailers and financial services. Its guided-selling product leverages LLMs to handle vague or complex requests, and blends multiple experiences, including product recommendation, content and calls to action. The conversational experience can be extended to social media and messaging platforms. Flyfish.ai doesn’t provide many merchandising capabilities and thus, fails to meet the inclusion criteria.
Luigi’s Box
Luigi’s Box is an EMEA-focused commerce search vendor with integrated AI capabilities providing multilingual product and content search, merchandising, recommendations, browse (listings), shopping assistant and analytics, with productized integration into popular midsize-focused digital commerce platforms. Luigi’s Box provides robust NLP/NER combined with vector search.
Syte
Syte first appeared as a pure-play visual search vendor with differentiating visual accuracy for “shop the look.” Visual search remains its core capability, but the platform has expanded to a more rounded S&PD product. Syte passes many of the inclusion criteria for this research but its narrow focus on only apparel (inclusive of jewelry) and home decor precludes it. If your use case is heavily visually oriented, this may be a vendor to shortlist.
Evaluation Criteria
Ability to Execute
Gartner analysts evaluate providers on the quality and efficacy of the processes, systems, methods or procedures that enable IT provider performance to be competitive, efficient and effective, and to positively impact revenue, retention and reputation within Gartner’s view of the market.
Product or service: The core goods and services that compete in and/or serve the defined market. This includes current product and service capabilities, quality, feature sets and skills. This can be offered natively or through OEM agreements and partnerships as defined in the Market Definition and detailed in the subcriteria. Representative analysis components include:
- Demonstration of product capabilities
- RFI responses on the product or service
- API scope and usage by customers
- Peer Insights data
Overall viability (of the business unit, finances, strategy and organization) and financials: Viability includes an assessment of the organization’s overall financial health as well as the financial and practical success of the business unit. It weighs the likelihood of the organization to continue to offer and invest in the product as well as the product’s position in the vendor’s current portfolio. Representative analysis components include:
- Corporate growth trends in revenue and profitability
- Digital commerce search and discovery product growth trends in revenue and profitability
- Customer growth trends
- Global customer presence
Sales execution and pricing: The organization’s capabilities in all presales activities and the structure that supports them. This includes deal management, pricing and negotiation, presales support and the overall effectiveness of the sales channel. Representative analysis components include:
- Gross merchandise value segmentation
- Average deal size
- Pricing approach, flexibility and simplicity
Market responsiveness and track record: The ability to respond, change direction, be flexible and achieve competitive success as opportunities develop, competitors act, customer needs evolve and market dynamics change. This criterion also considers the provider’s history of responsiveness to changing market demands. Representative analysis components include:
- Product enhancements delivered due to customer request
- Frequency of enhancements
Marketing execution: The clarity, quality, creativity and efficacy of programs designed to deliver the organization’s message in order to influence the market, promote the brand, increase awareness of products and establish a positive identification in customers’ minds. This “mind share” can be driven by a combination of publicity, promotional activity, thought leadership, social media, referrals and sales activities. Representative analysis components include:
- Vendor and product awareness and mind-share levels
- Perceived differentiators in-market
- Specific marketing success in research year
Customer experience: The products and services and/or programs that enable customers to achieve the anticipated results with the products evaluated. Specifically, this includes quality supplier-buyer interactions, technical support and account support. This may also include ancillary tools, customer support programs, availability of user groups and service-level agreements. Representative analysis components include:
- Peer Insights data
- The availability and quality of support and online training
- The scope of customer success programs
Ability to Execute Evaluation Criteria
| Evaluation Criteria | Weighting |
|---|---|
Product or Service | High |
Overall Viability | Medium |
Sales Execution/Pricing | High |
Market Responsiveness/Record | High |
Marketing Execution | Low |
Customer Experience | High |
Operations | NotRated |
Source: Gartner (June 2025)
Completeness of Vision
Gartner analysts evaluate providers on their ability to convincingly articulate logical statements. This includes current and future market direction, innovation, customer needs, competitive forces and how well they map to Gartner’s view of the market.
Market understanding: The ability to understand customer needs and translate them into products and services. Vendors show a clear vision of their market. They listen, understand customer demands, and can shape or enhance market changes with their added vision. Representative analysis components include:
- The scope of the digital commerce search and discovery offering
- Track record of meaningful enhancements
Marketing strategy: Clear, differentiated messaging consistently communicated internally and externalized through social media, advertising, customer programs and positioning statements. Representative analysis components include:
- Social media, influence and advertising
- Customer programs and positioning statements
- Thought leadership publications
Sales strategy: A sound strategy for selling that uses the appropriate networks, including direct and indirect sales, marketing, service and communication. This includes partners that extend the scope and depth of the vendor’s market reach, expertise, technologies, services and customer base. Representative analysis components include:
- Partner strategy
- Locations and span of sales team
Offering (product) strategy: An approach to product development and delivery that emphasizes market differentiation, functionality, methodology and features as they map to current and future requirements. Representative analysis components include:
- The product roadmap vision
- Product roadmap details
- Differentiation of the search and product discovery vision
Vertical or industry strategy: The strategy to direct resources (sales, product, development), skills and products to meet the specific needs of individual market segments, including verticals. Representative analysis components include:
- The scope of current industry-specific functionality
- The scope of planned industry-specific functionality
- The span of customers across industry verticals
Innovation: Direct, related, complementary and synergistic layouts of resources, expertise or capital for investment, consolidation, defensive or preemptive purposes. Representative analysis components include:
- Planned capital investment in digital commerce search and discovery technology
- The ability to leverage cloud-native capabilities
- The use of emerging technology
- Adherence to modern architectural paradigms
Geographic strategies: The provider’s strategy to direct resources, skills and offerings to meet the specific needs of geographies outside the “home” or native geography, either directly or through partners, channels and subsidiaries, as appropriate for that geography and market. Representative analysis components include:
- The span of geographic locations
- The span of partner network
- Revenue and customers across geographies
Completeness of Vision Evaluation Criteria
| Evaluation Criteria | Weighting |
|---|---|
Market Understanding | High |
Marketing Strategy | Low |
Sales Strategy | Medium |
Offering (Product) Strategy | High |
Business Model | NotRated |
Vertical/Industry Strategy | Medium |
Innovation | High |
Geographic Strategy | Medium |
Source: Gartner (June 2025)
Quadrant Descriptions
Leaders
Leaders demonstrate the ability to provide a depth and breadth of functionality. They deliver capabilities across multiple industries and business models that can scale up to support large search volumes. They provide sales and support services both directly and through an ecosystem of application, service and integration partners. They innovate, typically by means of technology updates, new products and product functionality, investments inside and outside core S&PD, and programs that improve customers’ ability to succeed. Leaders tend to be mature in a market, and also have financial, technical and organizational viability, and consistently feature in Gartner clients’ evaluations of vendors. They often set the competitive benchmark against which other vendors measure themselves.
Challengers
Challengers provide functionality that may have a narrower scope than Leaders in relation to serving the total addressable market. Challengers may focus on fewer industries, geographies or business models. These vendors are often highly respected, and have a solid customer base, but they may not be the most innovative, or only invest in innovation that is key to their target markets. They use their research and development resources, access to investment, profits and market reputation to attract customers and execute well. Challengers often focus on a perceived high-growth sector of the market. They often invest heavily in technology to meet the needs of their target customers, and they have robust feature sets for the customers they serve.
Visionaries
Visionaries demonstrate the ability to disrupt through innovation. They may incorporate new technologies or architectural approaches into their products, use creative pricing strategies or focus on a narrow market segment. They often win new customers quickly because they have identified an underserved niche in the market — one not addressed by Leaders or Challengers. Visionaries often have modern offerings but have yet to win large numbers of customers, and they often lack resources compared with larger companies. They also often have smaller partner networks and act as fast movers. Visionaries are often funded by venture capital or private equity companies, which provide the capital that enables them to invest in technology, sales and marketing resources for continued progress. There are no Visionaries in this Magic Quadrant.
Niche Players
Niche Players address a narrow band of the market, defined by industry, digital gross merchandise value (GMV), company size, region, technology capability or a combination of these characteristics. They frequently provide cost-effective solutions. They often target smaller or emerging-market opportunities, or smaller end-user companies. Niche Players often lack geographical or transactional scale; attract a significantly smaller range of technology, implementation or service partners; and offer more narrowly focused products (e.g., focusing either on B2C or B2B, but not both equally). They lack the financial viability of Leaders and Challengers, although they still meet the inclusion criteria. Like Visionaries, Niche Players are often funded by venture capital or private equity companies, which provide the capital that enables them to invest in technology, sales and marketing resources for continued progress.
Context
Buyers of S&PD products are looking to deliver and support a unique, compelling and consistent CX across many channels. Buyers are seeking more flexible and nimble implementations and postimplementation extensions that enable accelerated time to market, reduce the total cost of ownership and deliver desirable digital business outcomes.
“Headless” options — that is, decoupled front ends or architectural approaches that put them on a path to composable commerce are often preferred. The shift to composable commerce is based on the business agility of the overall architecture, despite its complexity.
Our inclusion criteria for vendors in this Magic Quadrant emphasize annual revenue and customer growth as indicators of the vendor’s strength and stability. Vendors’ financial performance remains important, but we also considered the size and distribution of the customer base to help buyers interpret the likely extent of a vendor’s support offerings.
The evaluation criteria emphasize the requirements for future success, architectural vision, innovation and breadth of capabilities — all of which provide essential context for buyers determining the best-fit vendor for their needs.
Ultimately, however, each buyer’s requirements are different. Organizations should match their own requirements for functionality, industry expertise, technology and cost to the vendors’ offerings. They should use the companion Critical Capabilities for Search and Product Discovery to rank vendors’ products for each use case by particular functional and nonfunctional criteria.
Market Overview
Demand for differentiating S&PD continues to grow. It is often seen as a relatively cost-effective way to provide optimal experiences in the path to purchase — without fully replacing an incumbent digital commerce or digital experience platform. S&PD helps organizations meet the demands of today’s B2B and B2C buyers.
Multiple Waves of Technical Innovation
For many years, the key to a site or app search was relevance. However, good relevance has become highly commoditized and is not enough on its own. The minimum expectation today is personalized relevance, using customer behavioral data in aggregate (sometimes called “wisdom of the crowd”) and/or one-to-one (i.e., using clickstream in real time).
Keyword search (keyword matching) was the dominant search technology for many years. This was enhanced by NLU/NLP-based semantic search — capturing the meaning of multiword and natural language queries linguistically; returning accurate, contextual responses; and reducing the chance of receiving zero results. Traditional NLP, such as named entity recognition (NER), is still widely used, especially backed on to a knowledge graph of the search domain (e.g., a product catalog), which provides inference.
Since the last round of research, vector search (mathematically derived closeness of products to query intent) has become firmly established and a basic expectation in S&PD products, providing further improvements to semantic search. Vector databases also power RAG for many LLM-based GenAI use cases. Most vendors now provide some form of hybrid search, including both keyword and vector embedding results, with various techniques in play to determine which results to provide the user.
Other key market shifts contributing to the evolution of S&PD include the following.
GenAI
Many vendors have experimented with the use of new GenAI-based UIs for discovery, but established, accepted use cases remain elusive. The whole industry seems to be on the verge of an inflection point to a new discovery UI. But new UIs are hard to establish, and certain large vendors are likely to lead the way to general acceptance of hybrid visual and conversational discovery UIs.
For this year, Gartner has consolidated a fragmented set of use cases into one we call guided selling assistants (see Hype Cycle for Digital Commerce). These may be separate customer journeys from a core search experience (typically at the category or even product level), but we increasingly expect them to be integrated directly into the core search and navigational experiences.
Algorithmic Merchandising
Merchandising is being automated via AI, especially using customer behavior data. However, many organizations still seek to retain curated/visual merchandising as an important tool, as AI has yet to access business strategy or tactics (or the contents of warehouses).
Retail Media Networks
The growth of retail media networks impacts S&PD by requiring the facilitation of sponsored product placement. While curated merchandising can cover some of this manually, organizations are beginning to want product-sponsoring automation, integration with third-party-network APIs or the auctioning of placements.
Catalog Enrichment
Especially in B2B, poor catalog data remains a problem for S&PD clients. While Gartner’s best practice puts the management and enrichment of product data firmly in the PIM function, we recognize this is not always possible and does not improve search retrieval as an outcome.
Acronym Key and Glossary Terms
| API | Application programming interface |
| ARR | Annual recurring revenue |
| AWS | Amazon Web Services |
| B2B | Business to business |
| B2C | Business to consumer |
| CDP | Customer data platform |
| CMS | Content management system |
| CX | Customer experience |
| DACH | Germany, Austria and Switzerland |
| DXP | Digital experience platform |
| GMV | Gross merchandise value |
| GCP | Google Cloud Platform |
| GTM | Go to market |
| ISV | Independent software vendor |
| KPI | Key performance indicator |
| LLM | Large language model |
| MACH | “Microservices, API-first, cloud-native SaaS, headless” — the tagline of the MACH Alliance, an industry body dedicated to promoting this approach |
| NER | Named entity recognition |
| NLP | Natural language processing |
| PIM | Product information management |
| POC | Proof of concept |
| S&PD | Search and product discovery |
| SaaS | Software as a service |
| SKU | Stock keeping unit |
| SMB | Small and midsize business |
| UI | User interface |
This Magic Quadrant is based on primary and secondary research by Gartner. This research drew on, but was not limited to:
- Gartner Peer Insights reviews for “S&PD” posted during 2024 and the first three months of 2025
Other sources include:
- Recorded briefings and demonstrations in which the vendors provided Gartner with insights into their products’ capabilities
- Feedback about vendors and their products captured during thousands of conversations, and other interactions with users of Gartner’s client inquiry service in 2024 and the first three months of 2025
- Generally available sources of information
Ability to Execute
Product/Service: Core goods and services offered by the vendor for the defined market. This includes current product/service capabilities, quality, feature sets, skills and so on, whether offered natively or through OEM agreements/partnerships as defined in the market definition and detailed in the subcriteria.
Overall Viability: Viability includes an assessment of the overall organization's financial health, the financial and practical success of the business unit, and the likelihood that the individual business unit will continue investing in the product, will continue offering the product and will advance the state of the art within the organization's portfolio of products.
Sales Execution/Pricing: The vendor's capabilities in all presales activities and the structure that supports them. This includes deal management, pricing and negotiation, presales support, and the overall effectiveness of the sales channel.
Market Responsiveness/Record: Ability to respond, change direction, be flexible and achieve competitive success as opportunities develop, competitors act, customer needs evolve and market dynamics change. This criterion also considers the vendor's history of responsiveness.
Marketing Execution: The clarity, quality, creativity and efficacy of programs designed to deliver the organization's message to influence the market, promote the brand and business, increase awareness of the products, and establish a positive identification with the product/brand and organization in the minds of buyers. This "mind share" can be driven by a combination of publicity, promotional initiatives, thought leadership, word of mouth and sales activities.
Customer Experience: Relationships, products and services/programs that enable clients to be successful with the products evaluated. Specifically, this includes the ways customers receive technical support or account support. This can also include ancillary tools, customer support programs (and the quality thereof), availability of user groups, service-level agreements and so on.
Operations: The ability of the organization to meet its goals and commitments. Factors include the quality of the organizational structure, including skills, experiences, programs, systems and other vehicles that enable the organization to operate effectively and efficiently on an ongoing basis.
Completeness of Vision
Market Understanding: Ability of the vendor to understand buyers' wants and needs and to translate those into products and services. Vendors that show the highest degree of vision listen to and understand buyers' wants and needs, and can shape or enhance those with their added vision.
Marketing Strategy: A clear, differentiated set of messages consistently communicated throughout the organization and externalized through the website, advertising, customer programs and positioning statements.
Sales Strategy: The strategy for selling products that uses the appropriate network of direct and indirect sales, marketing, service, and communication affiliates that extend the scope and depth of market reach, skills, expertise, technologies, services and the customer base.
Offering (Product) Strategy: The vendor's approach to product development and delivery that emphasizes differentiation, functionality, methodology and feature sets as they map to current and future requirements.
Business Model: The soundness and logic of the vendor's underlying business proposition.
Vertical/Industry Strategy: The vendor's strategy to direct resources, skills and offerings to meet the specific needs of individual market segments, including vertical markets.
Innovation: Direct, related, complementary and synergistic layouts of resources, expertise or capital for investment, consolidation, defensive or pre-emptive purposes.
Geographic Strategy: The vendor's strategy to direct resources, skills and offerings to meet the specific needs of geographies outside the "home" or native geography, either directly or through partners, channels and subsidiaries as appropriate for that geography and market.