Semantic Search

What Is Semantic Search?

Semantic search is a search technique that understands the meaning behind a query rather than just matching keywords. When a customer asks "how do I get my money back?", keyword search looks for documents containing those exact words. Semantic search understands the customer means "refund" and finds the refund policy — even if the word "money" never appears in the document.

This capability is powered by AI embeddings that convert text into numerical representations of meaning. Queries and documents with similar meanings are close together in vector space, enabling the search system to find conceptually relevant results.

How Semantic Search Powers Customer Service AI

Semantic search is the foundation of Retrieval-Augmented Generation (RAG) — the architecture most AI agents use to find accurate information. When a customer asks a question:

1. The question is converted to an embedding (numerical representation of its meaning)
2. Semantic search finds the most relevant documents in the knowledge base by comparing embedding similarity
3. Those documents are provided to the AI as context for generating its response
4. The AI produces a grounded answer based on the retrieved content

This process happens in milliseconds, enabling real-time responses that are both accurate and contextually relevant.

Semantic Search vs. Keyword Search

• Keyword search: Finds exact word matches. Fast and precise for known terms, but misses conceptual matches and fails when customers use different terminology than your documentation.
• Semantic search: Finds meaning matches. Handles synonyms, paraphrasing, and conceptual queries. Better for natural language questions but requires embedding infrastructure.
• Hybrid search: Combines both approaches. Uses keyword matching for precise lookups and semantic matching for conceptual queries. This is the approach most enterprise AI platforms use.

Technical context: Microsoft Research found that combining knowledge graph retrieval with semantic RAG improved factual accuracy by approximately 23%, demonstrating that semantic search combined with structured knowledge significantly outperforms keyword search alone.

The Maven Advantage: Knowledge Graph + Semantic Search

Maven AGI combines semantic search with knowledge graph traversal — understanding not just what content is semantically similar to the query, but how concepts are structurally related. This enables Maven to retrieve not just the most relevant document, but the entire context of related policies, workflows, and product details needed for accurate resolution.

Maven proof point: K1x achieved 80% resolution with Maven AGI across complex financial queries, where accurate retrieval of the right policies and procedures is essential — demonstrating that semantic search quality directly drives resolution quality.

Frequently Asked Questions

Can semantic search work across languages?

Yes. Multilingual embedding models can match queries in one language against documents in another. A customer asking a question in French can match against English documentation, enabling multilingual support without translating the entire knowledge base.

How accurate is semantic search?

Accuracy depends on embedding quality, content organization, and query complexity. For well-structured knowledge bases with clear, comprehensive content, semantic search achieves high retrieval accuracy. Performance degrades with ambiguous queries, poorly organized content, or highly specialized terminology not well-represented in the embedding model.

Does semantic search replace keyword search?

Not entirely. Hybrid approaches that combine semantic and keyword search perform best in practice. Keyword search excels at finding specific identifiers (order numbers, product codes) while semantic search excels at understanding natural language questions.

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