AI Support & Agents

Semantic Search

Definition

Semantic search is a search technique that matches content by meaning rather than by exact keywords, using embeddings to find results that are conceptually related to the query even when they share no words with it.

Also known as: vector searchmeaning-based searchneural search

How it works

Both the query and the content are turned into embeddings, then compared by similarity in a vector database. A search for "my card was declined" can surface an article titled "Payment failures and how to fix them" because their meanings are close, even without a shared keyword.

This contrasts with traditional keyword (lexical) search, which relies on literal term overlap. Many production systems combine the two — called hybrid search — to get both precision and semantic recall.

Why it matters for support

Customers rarely phrase questions the way your documentation does. Semantic search closes that gap, which is why it dramatically improves self-service and is the retrieval step that feeds accurate, grounded answers to an AI support agent.

Frequently asked

What is the difference between semantic search and keyword search?

Keyword search matches literal terms; semantic search matches meaning using embeddings, so it can find relevant results that use entirely different words from the query.

Is semantic search the same as RAG?

No. Semantic search is the retrieval step that finds relevant content. RAG uses that retrieved content and passes it to a language model to generate a grounded answer.

Related terms

Embeddings

Embeddings are numeric vector representations of text (or images, code, etc.) that capture meaning, so that pieces of content with similar meaning sit close together in a high-dimensional space..

Vector Database

A vector database is a database built to store and search embeddings — high-dimensional vectors — by similarity, so you can quickly find the content whose meaning is closest to a query..

Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation (RAG) is a technique where an AI model retrieves relevant information from your own knowledge sources at query time and uses it to ground its answer, instead of relying only on what it memorized during training..

Knowledge Base

A knowledge base is a structured, searchable library of articles — how-tos, FAQs, troubleshooting guides, and policies — that lets customers or agents find answers without contacting support directly..

Self-Service Rate

Self-service rate is the share of customer questions resolved through self-service resources — help center, FAQs, portals, or automation — without a customer needing to contact a support agent..

Put these ideas to work

Macha is an AI agent layer that sits on top of the help desk you already run — Zendesk, Freshdesk, Front, Intercom, or Gorgias.

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