Semantic product search

For product discovery, I’d focus on search platforms that combine semantic search, synonym recognition, and traditional keyword matching. This combination helps shoppers find the right products even when they use different terminology from your catalog—for example, searching for “sofa” when your products are labeled “couch,” or “waterproof jacket for hiking” instead of a specific product name.

The best options depend on whether you want a ready-to-use e-commerce search solution or a more customizable search engine that your developers can build on. These are the platforms I’d put on your shortlist for 2026.bcloud.ai+2

1. Platforms worth evaluating

1. Algolia

(Best starting point)

A strong option if you want a managed search API, fast integration, typo tolerance, configurable synonyms, and semantic relevance through NeuralSearch. Good for teams that want to improve search without building the full retrieval system themselves.

Evaluate: how well it blends exact product-name matches with meaning-based results, and how usage-based pricing scales.

Website:Algolia

2. Constructor

(Best for conversion optimization)

Designed for e-commerce product discovery, with AI-driven relevance and ranking informed by shopper behavior. Worth evaluating if your primary goal is to get shoppers to relevant products and improve conversion, rather than simply return text matches.

Evaluate: behavioral data requirements, ranking transparency, merchandising controls, and enterprise pricing.

Website:Constructor

3. Bloomreach Discovery

A particularly relevant choice if synonym recognition is a major requirement. Bloomreach supports synonyms learned from shopper behavior as well as configurable query expansion, helping bridge differences between shopper vocabulary and catalog terminology.documentation.bloomreach.com+4

Evaluate: how much synonym learning is automatic, how you control it, and whether the wider commerce suite fits your needs.

Website:Bloomreach

4. Coveo

A good enterprise candidate when you need both semantic product discovery and explicit synonym management. Its commerce catalog semantic encoder is designed to connect shopper queries with product descriptions that use different language.docs.coveo.com+4

Evaluate: implementation effort, integration with your commerce stack, and the availability of specific AI features under your plan.

Website:Coveo

5. Elasticsearch

Best for engineering teams that want deep control over relevance. It supports semantic retrieval and hybrid search, combining vector-based similarity with lexical matching. You can customize analyzers, synonyms, scoring, and filtering to suit your catalog.www.elastic.co+1

Evaluate: development and maintenance effort, model selection, and who will own relevance tuning.

Website:Elasticsearch

6. OpenSearch

A flexible alternative for teams that prefer open-source technology and control over their search infrastructure. It supports semantic and hybrid search, but you need to configure an embedding model and build the product-ranking experience around it.docs.opensearch.org+1

Evaluate: operating costs, model hosting, synonym configuration, and the engineering work needed to achieve production-quality commerce search.

Website:OpenSearch

Other options worth adding for retail-focused evaluations include Klevu, Searchspring, and Meilisearch. The first two are commerce-oriented managed solutions; Meilisearch is worth considering for a more developer-led implementation.bcloud.ai+1

2. How the options compare

This is a high-level shortlist, not a guarantee that every feature is included in every plan or integration. Validate exact capabilities with a representative catalog and queries.docs.coveo.com+4

3. What to look for in a platform

Semantic search and synonym recognition solve related but different problems.

• Semantic search: Understands meaning and intent. A search for “shoes for standing all day” can find comfortable work shoes even if the product descriptions don't use that exact phrase.
• Synonym recognition: Maps related terms, such as “sofa” and “couch,” or industry-specific terms and abbreviations.
• Hybrid search: Combines semantic relevance with exact keyword matching. This matters for product IDs, SKUs, model numbers, sizes, and technical specifications.

For most product catalogs, I'd prioritize hybrid search with both automatic semantic understanding and manually controllable synonyms.

During a demo, test these five cases:

Synonyms and alternate terminology

Search “sneakers” when the catalog uses “trainers.”

Natural-language intent

Search “a lightweight backpack for weekend trips.”

Exact matching

Search an exact SKU and confirm the correct product ranks first.

Attribute accuracy

Search “black waterproof boots under $150” and verify that results respect the constraints.

Measurable improvement

Compare zero-result rate, search click-through rate, add-to-cart rate, and conversion against your existing search.

4. My recommendation

I'd start with Algolia, Bloomreach, and Constructor if you're buying a managed product discovery platform. Add Coveo if your environment is enterprise-heavy, or Elasticsearch/OpenSearch if you want to build and control the search stack yourself.

To narrow this to the best two or three options, one question: What kind of product catalog are you building search for?

Catalog type

B2C e-commerce / online retail

B2B products, industrial parts, or wholesale

Multi-vendor marketplace

Internal product catalog or enterprise search

Approximate catalog size

Under 10,000 products

10,000–500,000 products

Over 500,000 products

Preferred implementation

Managed platform / ready-to-use API

Customizable search engine / developer-led

Open to either

[Find the best-fit platforms]

Several e-commerce search platforms offer semantic search, AI-powered query understanding, and synonym handling to help customers find products even when they use different words from those in a product title or description.

The key distinction is that semantic search understands the meaning and intent of a query, while synonym handling maps alternate terms—such as “sneakers” and “trainers”—to relevant products. Some platforms provide both automatically; others require merchants to configure synonym rules.help.shopify.com+1

Leading platforms to consider

1. Algolia

(Strong match for automatic synonyms)

Its NeuralSearch combines keyword and semantic search, automatically handling synonyms and spelling errors. It is designed for shoppers who describe products differently from how they're listed in the catalog.www.algolia.com+1

Best for: Flexible, developer-friendly search across e-commerce catalogs.

Official website

2. Constructor

Uses AI, natural-language processing and shopper behavior to understand intent. It automatically generates synonyms, acronyms and query reformulations based on search interactions.constructor.com+1

Best for: Retailers seeking personalized product discovery and behavioral learning.

Official website

3. Bloomreach Discovery

Combines semantic understanding with synonyms learned from shopper search and click behavior. Merchants can also create or modify synonym rules.documentation.bloomreach.com+2

Best for: Large retailers wanting AI-driven search, merchandising and personalization.

Official documentation

4. Klevu

Supports AI-powered product search and synonym expansion. However, its documented synonym management requires merchants to configure synonym entries, so automatic synonym discovery should be verified for the specific offering.support.klevu.com+1

Best for: E-commerce teams wanting product search with merchant controls.

Official documentation

5. Shopify Search & Discovery

Includes semantic search that connects related words, product attributes and concepts. Merchants can also create synonym groups manually. Semantic search availability depends on the store's plan, catalog size and locale.help.shopify.com+1

Best for: Shopify stores that want built-in search improvements without adopting a separate search platform.

Official documentation

6. Searchspring

Provides grouped and one-way synonym functionality to match alternate customer terminology to catalog terms. Its documented synonym feature is primarily merchant-managed, so confirm whether automatic semantic synonym learning is included in the plan being evaluated.help.searchspring.net+1

Best for: Merchandising teams that want direct control over search relevance.

Official documentation

Quick comparison

Sources: official vendor documentation for Algolia, Constructor, Bloomreach, Klevu, Shopify, and Searchspring.

My recommendation

• For automatic synonym handling with minimal manual tuning: start with Algolia NeuralSearch and Constructor.
• For learning from customer search behavior: evaluate Bloomreach Discovery alongside Constructor.
• For an existing Shopify store: begin by testing Shopify Search & Discovery before investing in an external platform.

One important caveat: semantic search does not guarantee that every synonym will be recognized correctly. During evaluation, test real customer queries, including regional terminology, abbreviations, misspellings and product-specific jargon. Compare result relevance and zero-result rates, and verify which capabilities are included in each vendor's current plan.

Improving your product discovery with modern search is a great move. Relying purely on traditional keyword matching often leads to zero-result pages or frustrated users when they don't use the exact terminology your catalog uses.

Here is a structured breakdown of the key considerations you need to weigh when evaluating search solutions featuring semantic search and synonym recognition:

• Data Quality and Preparation: Semantic search (vector search) relies on embeddings that capture meaning, but your underlying product data (titles, descriptions, categories, attributes) still needs to be clean, structured, and up-to-date. Garbage in means blurry embeddings out.
• Domain-Specific Synonyms: Generic English synonym dictionaries won’t cut it for e-commerce or niche catalogs. Consider how easy it is to build and maintain custom synonym sets (e.g., knowing that "sneakers" equals "trainers" or "kicks", or that a specific model number maps to a brand name).
• Hybrid Search Capabilities: The best modern search solutions don't force you to choose between keyword (lexical) search and semantic search. They use hybrid search —combining BM25 (keyword exactness for SKU numbers, specific brands, or exact sizes) with vector search (intent and conceptual matching) to cover all use cases.
• Latency and Performance: Vector/semantic search is computationally heavier than traditional indexing. Ensure the provider can deliver sub-second response times under peak traffic without inflating your infrastructure costs.
• Tuning and Relevance Control: Pure AI models can sometimes return "too smart" or irrelevant results. Look for a platform that gives you relevance tuning knobs—allowing merchandisers to boost specific products, pin results for high-intent queries, or adjust the weight between semantic and keyword scoring.
• Cold Start and Multilingual Support: If you launch new products frequently, check how fast embeddings update. If you operate globally or plan to, evaluate how well the semantic model handles multilingual queries and cross-lingual matching.
• Build vs. Buy (Cost vs. Maintenance): Decide whether you want to implement vector databases (like Pinecone, Milvus, or Qdrant) alongside an open-source engine (like Elasticsearch or OpenSearch) or use a managed, purpose-built discovery platform (like Algolia, Klevu, or Constructor) that handles semantic and synonym layers out of the box.

If you want to narrow this down, tell me:

• What is your current tech stack or search engine?
• Roughly how many products are in your catalog?
• Are you looking for an out-of-the-box SaaS solution or an API-first/open-source approach?

I can provide a more tailored vendor or architectural recommendation based on that.

If you want to improve product discovery, I’d prioritize a hybrid search solution that combines semantic search, synonym recognition, and traditional keyword matching. This helps customers find products when they describe what they need in everyday language, use regional terminology, or search for something using a different name than the one in your catalog.

For example, a customer searching for “comfortable shoes for standing all day” should find relevant work shoes even if those exact words don't appear in the product descriptions. Someone searching for “trainers” should also find products listed as “sneakers.”

The key is choosing a platform that improves relevance without sacrificing exact product matching, filtering, or control over merchandising.docs.opensearch.org+2

1. Solutions worth evaluating

1. Algolia AI Search

(Best first shortlist candidate)

Best if you want a managed solution with minimal search infrastructure work. Its NeuralSearch combines keyword and semantic retrieval, while AI Synonyms, typo tolerance, merchandising rules, analytics, and A/B testing help improve discovery over time.www.algolia.com+2

Trade-off: Evaluate pricing at your query volume and how much ranking control your team needs.

Explore Algolia AI Search

![Synonym rules for search

      | 
          Coveo Merchandising Hub](https://images.openai.com/static-rsc-4/sDLI4eB7gQxmGJfFZ0-fqZ3w0Q-je1LHZ0gvoQqljfAGKDn7rzxRZbgyQJ5awJASSBTuJlCZCtkfO9oIq-X49J2ErB_1IzfBgJPMy4SfbDWBcMNLGMom08R6v2_juQcqq4APIPhQaEOFziQrTtKK-V4l5wd21WlZchTdAsebd846SrM_8p4amX1AWLrSobb3?purpose=fullsize)

2. Coveo for Commerce

Best if product discovery also needs personalization, recommendations, and sophisticated merchandising. Coveo supports intent-aware product ranking, behavior-based relevance tuning, and explicit synonym rules. Its Catalog Semantic Encoder adds semantic matching for product catalogs.docs.coveo.com+2

Trade-off: Verify feature availability, implementation effort, and total enterprise cost for your deployment.

Explore Coveo Commerce

3. Elasticsearch

Best if you have strong engineering resources and need detailed control over relevance. It supports hybrid keyword/vector search, semantic workflows, custom ranking, and configurable search pipelines.www.elastic.co+2

Trade-off: Your team will typically take on more relevance tuning and operational work than with a turnkey commerce platform.

Explore Elasticsearch search

4. OpenSearch

Best if flexibility, infrastructure ownership, and cost control are priorities. It offers configurable semantic and hybrid search, full-text search, filters, and ranking pipelines.docs.opensearch.org+2

Trade-off: You need to configure the embedding model, synonym behavior, relevance tuning, and production operations.

Explore OpenSearch

2. What your search solution must do

These requirements matter because semantic similarity alone can return related but incorrect products. For example, searching for a particular model number should prioritize the exact model, not merely products that are conceptually similar. Hybrid search addresses this by combining lexical and semantic retrieval.www.elastic.co+2

3. How I would evaluate the options

Run a proof of concept with your real catalog and actual customer queries before committing to a vendor.

Suggested proof-of-concept checklist

of evaluation areas reviewed [Reset]

Use the same test set for every platform, and compare against your current search implementation. Include both common queries and difficult edge cases, then run an A/B test if possible.

4. A practical recommendation

• Choose Algolia first if speed to implementation, managed search, and automated synonym discovery are your main priorities. Note that its AI Synonyms feature has specific plan requirements, and its documentation says NeuralSearch customers may not need the separate feature.www.algolia.com+2
• Choose Coveo if personalized discovery and enterprise merchandising are central to your strategy.
• Choose Elasticsearch or OpenSearch if you have the engineering capacity to build and optimize a tailored search stack. Elasticsearch also supports configurable synonym sets that can be updated at search time.docs.coveo.com+2

The final choice should be based on measured relevance and commercial results, not simply which vendor has the most AI features.

5. A few details would help narrow this down

What kind of product discovery are you building? And are you looking for a managed SaaS product or a solution your engineering team can configure and operate? Those two factors will help narrow the shortlist considerably.

If your search bar returns “no results” for “trousers” vs. “pants,” you’re looking for a semantic search or AI-powered site search solution. These tools understand synonyms, intent, and related concepts instead of relying exclusively on exact keyword matches.

For a website or e-commerce catalog, I’d start by evaluating the following providers. The right choice depends on whether you want a plug-in solution or more control over your existing search infrastructure.

Providers worth evaluating

1. Algolia

(Strong starting point)

Offers hosted search APIs and NeuralSearch, which combines keyword and vector-based semantic search. Designed to handle searches where the query wording differs from catalog terminology.www.algolia.com+1

Best for: Teams that want a managed solution with a developer-friendly integration.

Website: algolia.com

2. Constructor

Provides AI-driven product search, personalized ranking, and product discovery, with a focus on retail catalogs and shopping behavior.constructor.com

Best for: Retailers that want search relevance and conversion optimization.

Website: constructor.com

3. Coveo

Offers AI-powered relevance and search across commerce, websites, and enterprise content. Worth considering when search needs to span multiple content types or business systems.constructor.com+1

Best for: Larger organizations with complex search and personalization requirements.

Website: coveo.com

4. Bloomreach

Provides commerce-focused discovery and AI-driven product search, alongside personalization and merchandising capabilities.constructor.com+1

Best for: E-commerce teams looking for a broader customer-experience platform.

Website: bloomreach.com

5. Elastic (Elasticsearch)

Supports keyword search, vector search, and hybrid retrieval, allowing your engineering team to tune how exact matches and semantic matches are combined.www.elastic.co+1

Best for: Teams that want flexibility and control over their search architecture.

Website: elastic.co

6. OpenSearch

An open-source option supporting hybrid search that combines traditional keyword matching with vector-based semantic retrieval.opensearch.org

Best for: Teams that prefer an open-source foundation and are comfortable operating or customizing search infrastructure.

Website: opensearch.org

What capability should you look for?

Ask each vendor to demonstrate these five capabilities against your actual search data.

Hybrid search is particularly useful because semantic search alone can struggle with unusual product names and exact identifiers.www.algolia.com+1

My recommendation

• Want the fastest route to a managed solution? Start with Algolia.
• Running a large e-commerce business? Compare Algolia, Constructor, Coveo, and Bloomreach.
• Have an engineering team and want control? Evaluate Elasticsearch and OpenSearch.

One important distinction: if your current search already works well but simply lacks synonyms, you may only need a synonym dictionary or query-expansion feature. You might not need to replace the entire search engine.

One question to narrow it down

What kind of search bar are you improving?

1. What are users searching?

Products in an online store

Pages or content on a website

Documents or internal business data

Listings in a marketplace or directory

2. How is search currently implemented?

An existing search engine I want to improve

Built into a platform such as Shopify or WordPress

Custom-built search

Not sure

3. Approximate scale (optional)

Under 10,000 items

10,000–1 million items

Over 1 million items

Not sure / not applicable

[Find the best-fit vendors]