Vector database
A vector database stores data as mathematical vectors (embeddings) and enables searching based on semantic similarity, rather than exact word matches.
Detailed explanation
Traditional databases search for exact matches. A vector database understands meaning: search for 'costs' and you also find results about 'rates', 'investment', and 'budget'. This makes vector databases essential for AI systems working with unstructured data.
For AI sales systems, vector databases are the memory layer. They store all customer conversations, product documentation, case studies, and prospect information in a way that AI can directly search. AI-agent uses a vector database to retrieve the most relevant context for every conversation.
Synonyms
Examples
Your Company OS' knowledge base is stored in a vector database. When AI-agent writes an email to a CFO in the logistics sector, he automatically retrieves the most relevant case studies, objection scripts, and product information, in less than 0.1 seconds.
A customer service bot uses a vector database with all 500 product manuals. Customer asks a complex question about a specific configuration, the bot finds the exact relevant section in milliseconds.
When to use this?
Vector databases are indispensable for RAG systems, semantic search, recommendation systems, and any AI application working with large amounts of unstructured text.
Your Company OS approach
Your Company OS implements vector databases as the knowledge layer of AI-agent. We index your sales materials, customer conversations, and product documentation so AI-agent always uses the most relevant context.
Related terms
Want to know more about Vector database?
Want to know how to put Vector database to work in your organisation? Get in touch with Your Company OS.