What it is
Semantic search is AI-powered search that uses embeddings to understand the meaning behind a query, not just the literal words. When a customer searches for 'my order is late', semantic search matches articles about shipping delays, delivery tracking, and estimated arrival times — even if none of those articles contain the word 'late'.
Why it matters
Traditional keyword search fails when users do not know the right terminology. Semantic search can improve self-service because it finds relevant content regardless of how the question is phrased.
Key components
- Vector embeddings
- Similarity scoring
- Index management
- Hybrid search (keyword + semantic)
How it connects
Data Cloud powers semantic search in Salesforce. Agentforce agents use semantic search to retrieve grounding data from Knowledge articles and CRM records during conversations.
Good to know
Semantic search is only as good as your indexed content. Ensure your Knowledge articles, FAQs, and product documentation are comprehensive and up to date.
Related terms
Knowledge Management
The systems and processes for organizing, storing, and making business information accessible to AI agents so they can provide accurate, contextual answers.
Data Cloud
Salesforce's unified data platform that centralizes customer data from all sources, making it available for agents, analytics, and insights in real time.
Grounding
Connecting AI responses to your actual business data so agents answer with facts, not hallucinations.
RAG (Retrieval-Augmented Generation)
A technique that makes AI smarter by fetching relevant information from your data before generating a response. The AI "looks it up" instead of guessing.
Semantic Search in practice
Articles from the Funnelists Team that put this term to work.
