Semantic Search
Semantic search is a retrieval methodology that uses AI to match queries to documents based on conceptual meaning rather than literal keyword overlap. A semantic search for "ways to reduce customer churn" returns documents about "retention strategies" and "engagement improvement" even if the phrase "reduce churn" never appears in them.
How It Works
Semantic search converts both documents and queries into embedding vectors using the same model, then retrieves the documents whose vectors are nearest to the query vector. The process:
- Indexing — all documents are embedded and stored in a vector database.
- Query encoding — at search time, the query is embedded with the same model.
- Similarity ranking — vector search returns the k most similar documents, ranked by cosine similarity score.
- Result delivery — top results are returned to the user or passed as context to a generation model.
Modern deployments typically apply hybrid retrieval — combining semantic similarity scores with keyword (BM25) scores — to handle both conceptual queries and exact-term lookups.
Semantic Search vs. Keyword Search
| Dimension | Keyword search | Semantic search |
|---|---|---|
| "retention strategy" finds "churn reduction" | No | Yes |
| "error code 4092B" finds exact match | Yes | Weaker |
| Handles paraphrase | No | Yes |
| Query formulation required | Exact terms | Natural language |
Neither method dominates universally. Semantic search excels for concept-heavy queries; keyword search excels for exact entity retrieval. Production systems often combine both.
Common Use Cases
- RAG pipelines — semantic search is the retrieval step that finds relevant document chunks before language model generation.
- CRM and knowledge base search — sales reps and support agents query by describing what they need rather than constructing exact search terms.
- Talent discovery — recruiters describe ideal candidate backgrounds in natural language; semantic search returns profiles by concept match.
- Document discovery — legal and compliance teams search contract archives for relevant precedents and clauses.
Related Terms
Knowlee's Approach
Semantic search is the retrieval layer that connects every Knowlee agent query to the relevant account context in the knowledge graph. When an agent prepares outreach or evaluates a prospect fit, it queries by meaning — surfacing signals relevant to the current task regardless of exact phrasing in the underlying records. This removes the need for brittle keyword rules and allows the agent to work with how knowledge is naturally expressed. The full architecture is described in The Enterprise Knowledge Graph Moat.