Report:

Magic Quadrant for Search and Product Discovery

Top Vendor Products for Search and Product Discovery in 2025

• Algolia
• Bloomreach
• Constructor
• Coveo
• Google
• Netcore Unbxd

Search and Product Discovery Vendor Product Comparisons 2025

• Algolia - Leader
  

  Pros:

  • 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.

  Cons:

  • 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 - Niche Player
  

  Pros:

  • 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.

  Cons:

  • 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 - Niche Player
  

  Pros:

  • 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.

  Cons:

  • 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 - Leader
  

  Pros:

  • 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.

  Cons:

  • 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 - Leader
  

  Pros:

  • 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.

  Cons:

  • 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 - Leader
  

  Pros:

  • 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.

  Cons:

  • 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 - Niche Player
  

  Pros:

  • 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.

  Cons:

  • 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 - Leader
  

  Pros:

  • 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.

  Cons:

  • 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 - Challenger
  

  Pros:

  • 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.

  Cons:

  • 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 - Challenger
  

  Pros:

  • 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.

  Cons:

  • 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 - Leader
  

  Pros:

  • 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.

  Cons:

  • 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 - Niche Player
  

  Pros:

  • 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.

  Cons:

  • 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 - Challenger
  

  Pros:

  • 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.

  Cons:

  • 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 - Niche Player
  

  Pros:

  • 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.

  Cons:

  • 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 - Niche Player
  

  Pros:

  • 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.

  Cons:

  • 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.

Reference

• Gartner, Magic Quadrant for Search and Product Discovery, 24-Jun-2025, ID G00823079

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