AI, Data & Intelligence
Semantic Search
Search that understands meaning rather than exact keywords, for websites, product catalogues, intranets and support portals, combining vector and keyword retrieval with relevance tuning and analytics.
Capability overview
What semantic search involves
Keyword search fails when users describe what they need in their own words. A shopper searching for a warm jacket for Manali in December, or an employee asking how to claim internet expenses, gets poor results if the catalogue or policy uses different terms. Semantic search matches intent, while still honouring exact matches for SKUs, part numbers and names.
Unlike a chat assistant, semantic search returns ranked results rather than a generated answer, which keeps it fast, cheap and transparent. We build hybrid search with engines such as Elasticsearch, OpenSearch, Typesense or Algolia, add rerankers for top results, and tune relevance using real query logs.
What is included
Search capabilities
Hybrid ranking
Keyword and vector scores combined so both descriptive queries and exact identifiers return the right results.
Multilingual queries
Multilingual embeddings that let users search in Hindi or other languages against English content, and handle transliterated terms.
Facets and filters
Category, price, availability, department or date filters that work together with semantic relevance.
Business rules
Boosting, pinning and merchandising rules for promotions, preferred content or regulatory priority.
Search analytics
Zero-result queries, click-through rates and refinements tracked to guide relevance tuning and content gaps.
How we work
How we deliver semantic search
Query log analysis
Existing search logs studied to find failing queries, common intents and vocabulary mismatches.
Relevance judgements
A set of queries with expected top results defined with merchandisers or content owners.
Index and ranking build
Content indexed with embeddings and metadata, and ranking configured for hybrid retrieval.
Offline and A/B testing
Relevance measured against judgements, then compared with the old search on live traffic.
Continuous tuning
Monthly review of analytics to adjust synonyms, boosts and content coverage.
Related capabilities
Related capabilities in Generative AI & LLM Engineering
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Generative AI Strategy
Generative AI Strategy within our generative ai & llm engineering services, scoped after a short discovery conversation.
Generative AI Applications
Applications that produce new content for your business, such as proposals, product descriptions, marketing variants, reports and images, with brand rules, approval workflows and provenance built in.
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Questions & answers
Questions about Semantic Search
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionNo. Semantic search returns a ranked list of pages or products. A chatbot generates an answer. Search is faster and cheaper per query and often the better first step, and it also powers retrieval for chat assistants.
Better search usually reduces zero-result and abandoned searches, which matters because searchers tend to be high-intent visitors. We measure impact through an A/B test rather than assume it.
Yes. Recent Elasticsearch and OpenSearch versions support vector search, so semantic capability can often be added to your current cluster instead of replacing it.
For a catalogue or knowledge base of moderate size, six to ten weeks is typical, including relevance testing and a controlled A/B comparison.
Next step
Discuss semantic search with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.