Embeddings
Mathematical representations of text that capture meaning, enabling semantic search.
Why it matters
Embeddings let AI understand that 'car' and 'automobile' mean the same thing. This powers semantic search, recommendations, and content matching.
In practice
When our Chat Agent needs to find relevant docs, it uses embeddings to match visitor questions to the most semantically similar help pages.
See this in practice
How Leap Laboratory uses this concept in real work.
In our service
Automation Architecture→Example use cases
Related terms
Vector Database
A database storing information as mathematical vectors for semantic search.
RAG (Retrieval-Augmented Generation)
A method where AI retrieves relevant information from external sources before generating a response.
Semantic HTML
HTML that describes content meaning, not just appearance. Foundation of agent-readable pages.