Semantic Search
Search technology that understands the meaning behind a query rather than just matching keywords. Enables AI agents to find relevant information even when customers describe problems in unexpected or varied ways.
Beyond Keyword Matching
Traditional keyword search fails when customers don't use the exact terms in your documentation. A customer asking "my app keeps crashing when I try to pay" won't match a help article titled "Troubleshooting Payment Processing Errors" using keyword search—there's no word overlap. Semantic search understands that both are about the same problem because it operates on meaning, not just text matching.
How It Works
Semantic search converts both the customer's query and your knowledge content into mathematical representations called embeddings—numerical vectors that capture meaning. Similar concepts end up close together in this vector space. When a customer asks a question, the system finds knowledge content that's semantically close to the question, even if the exact words are completely different.
This is foundational technology for AI customer service. Without semantic search, AI agents can only find answers when customers happen to use the right keywords. With it, they can understand what customers mean and retrieve relevant information regardless of how the question is phrased. The result is dramatically better coverage—the same knowledge base answers more questions because the retrieval system is smarter.