Vector Database
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Vector Database

Quick definition

A vector database stores data as mathematical vectors (embeddings) and enables searching based on semantic similarity, rather than exact word matches.

Detailed explanation

Illustration for Vector Database

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. Mario uses a vector database to retrieve the most relevant context for every conversation.

Synonyms

embedding databasesemantic search engineAI memory

Examples

1

Match-AI's knowledge base is stored in a vector database. When Mario writes an email to a CFO in the logistics sector, he automatically retrieves the most relevant case studies, objection scripts, and product information โ€” in less than 0.1 seconds.

2

A customer service bot uses a vector database with all 500 product manuals. Customer asks a complex question about a specific configuration โ€” 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.

Match-day approach

Match-AI implements vector databases as the knowledge layer of Mario. We index your sales materials, customer conversations, and product documentation so Mario always uses the most relevant context.

Visual representation of Vector Database
Vector Database

Learn more

Wil je weten hoe je vector database effectief inzet in jouw organisatie? Neem contact op met Match-day.

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