What is Semantic search?
Semantic search uses embeddings to understand meaning. It matches “sick leave policy” with “absence rules” even without the same words. This is the backbone of accurate assistants and RAG experiences over your documents and data.
How does it work?
Content is first broken into small pieces ('chunks') and converted into unique mathematical codes ('vectors' or 'embeddings') that represent its meaning. Your search query is also converted, and the system quickly finds the content pieces whose codes are the closest match for meaning, not just keywords.
When does it matter? (Examples)
- Employees can’t find policies or procedures by keyword alone.
- Support teams need fast, accurate answers from a large knowledge base.
- RAG assistants must pull the right facts before responding.
Benefits
- Improves findability
- Boosts answer accuracy
- Cuts time to resolution
Risks
- Poor chunking reduces relevance
- Outdated content retrieved
- Access leaks without permissions
Antire and Semantic search
We design chunking, metadata, and evaluation to keep retrieval precise and safe, so your assistants answer from the right sources.
Services
Data platforms and applied AI.
Related words
Vector database