Semantic Search

Semantic search is a search approach that understands what you mean, not just what you type. Traditional keyword search looks for exact word matches — if you search for "reducing staff churn," it will not find a document titled "Improving Employee Retention" because the words are different. Semantic search understands that these phrases mean the same thing and returns relevant results regardless of the specific words used. This capability is powered by embeddings — numerical representations of text that capture meaning. By converting both queries and documents into embeddings and comparing their similarity in vector space, semantic search finds results based on conceptual relevance rather than lexical overlap.

Semantic search operates in two phases. During indexing, all documents are processed through an embedding model to create vector representations, which are stored in a vector database. During search, the user's query is similarly embedded, and the vector database finds the stored document vectors most similar to the query vector. Modern semantic search systems often use hybrid approaches that combine semantic similarity with traditional keyword matching (BM25). This captures both meaning-based relevance and exact term matches, which is particularly important for queries containing specific names, codes, or technical identifiers that should be matched literally. Re-ranking is another common enhancement. After initial retrieval returns a set of candidate results, a cross-encoder model re-evaluates each result in the context of the specific query, reordering them for maximum relevance. This two-stage approach balances speed (fast initial retrieval) with accuracy (precise re-ranking).

For organisations with large knowledge bases, semantic search transforms information accessibility. Employees spend less time crafting the perfect search query or browsing through irrelevant results. They can ask questions naturally and find relevant information even when they do not know the exact terminology used in the source material. This has measurable impact. Studies consistently show that knowledge workers spend 20-30% of their time searching for information. Semantic search can reduce this dramatically by delivering relevant results on the first query rather than requiring multiple attempts with different keywords. Semantic search is also the retrieval engine behind RAG systems. The quality of a RAG application is directly tied to the quality of its retrieval — better search means more relevant context, which means more accurate AI responses.

Semantic search powers internal knowledge portals where employees query company documentation, customer support systems where incoming queries are matched to the most relevant help articles, legal research tools that find relevant precedents by describing situations rather than citing case numbers, and e-commerce platforms where customers describe what they want in natural language. In each case, semantic search improves both user experience (more relevant results) and operational efficiency (faster information retrieval, fewer support escalations, better self-service rates).