Semantic Search

A search method that finds content based on meaning rather than exact keyword matches, so your AI understands what customers actually mean when they ask a question.

Technical definition

Semantic search converts both the query and the searchable documents into vector embeddings, then uses similarity metrics (typically cosine similarity) to find the closest matches in the embedding space. Unlike lexical search (BM25/TF-IDF), semantic search captures synonyms, paraphrases, and conceptual relationships. Hybrid approaches combine semantic and keyword search for best results.

How Verabase uses Semantic Search

Verabase uses hybrid semantic search in its RAG pipeline. When a customer asks a question, the query is embedded and compared against all document chunk embeddings in your knowledge base. The top-k results are combined with keyword matches using reciprocal rank fusion, ensuring both meaning-based and exact-match results are surfaced. This hybrid approach outperforms either method alone.

Why it matters for support teams

Customers don't ask questions using the same words as your documentation. Someone searching 'can I get my money back?' needs to find your 'Refund Policy' article. Semantic search bridges this vocabulary gap automatically, which directly increases the AI's answer accuracy and deflection rate.

Related terms

How Verabase Handles Self-Healing Knowledge

The standard industry approach relies on support agents manually flagging stale content and technical writers updating documentation weeks later. Verabase completely upends this model through an autonomous AI platform that identifies knowledge gaps the moment an AI agent encounters a question it cannot answer.

Rather than requiring manual intervention, our visual RAG engine drafts a proposed fix. It analyzes the context of the user's question, reviews your existing knowledge base for conflicts, and creates a clear, structured article or snippet. All you need to do is click 'Approve'. This ensures your AI agents continually get smarter over time without adding to your support team's workload.

Security First Architecture

Many legacy platforms bolt AI onto their existing infrastructure, creating potential data leaks between tenants or exposing sensitive internal documents to end-users. Verabase is built from the ground up with a Bring Your Own Key (BYOK) architecture, enterprise-grade access controls, and strict semantic boundaries. This means your private engineering documents never accidentally leak into customer-facing agent responses.