Semantic search

Semantic search converts both the query and every indexed item into embeddings and ranks items by vector similarity. A shopper typing "something to keep laptop dry in monsoon" matches a waterproof laptop sleeve even though the product title shares no words with the query. A support user asking "why did my payout bounce" matches an article titled "Failed settlements".

It is the retrieval mechanism inside RAG, but also a product feature in its own right: catalogue search, help-centre search, internal document search and case lookup all improve when intent rather than vocabulary drives results. Good implementations add filters (category, price, availability, permissions) and usually a keyword leg for exact terms, which is hybrid search.

Semantic search is not a chatbot and does not generate text; it returns existing items. It also does not understand in a human sense: it finds things that are statistically similar in meaning, which is powerful for paraphrase and poor for identifiers, negation and precise constraints ("under 5,000 rupees" is not reliably respected by similarity alone).

• Product manager: Search that fails on natural phrasing loses conversions and creates support tickets; semantic search fixes the largest class of those failures.
• Support manager: Customers and agents find the right article on the first attempt more often, which lowers handle time and repeat contacts.
• CTO / Head of Engineering: It can be added to an existing search stack incrementally, usually as a second ranking signal rather than a replacement.
• Data lead: Search logs of unmatched queries become a direct feed for content gaps and catalogue enrichment.

Keyword search assumes users know the words in the document. They rarely do: they describe a problem, a need or a situation in their own terms, often in a mix of languages. Semantic search exists to close that vocabulary gap so search works the way people ask. The trade-off is that meaning-similarity is fuzzy: it can rank a related-but-wrong item highly, miss exact codes, and needs filters and a keyword leg to handle constraints. It is best treated as one signal in a ranking system rather than the whole of it.

• E-commerce catalogue search that handles descriptive, multilingual and misspelt queries with stock and price filters
• Help-centre search for a SaaS product that matches problem descriptions to the right article
• Case and precedent lookup for an insurance claims team by describing the situation
• Finding relevant lessons and resources in an education platform from a student's own words
• Locating past delivery exceptions and resolutions in a logistics operations console

Technique / practiceA search technique built on embeddings and vector indexes, usually combined with keyword retrieval and filters. Eazyware delivers it as a standalone feature or as the retrieval layer of RAG under Retrieval & Knowledge Engineering.

Eazyware service that covers it: Retrieval & Knowledge Engineering. Starting prices are on the pricing page.