A vector database stores embeddings, the numeric representations of meaning, and is optimized to find the nearest matches to a query at speed and scale. Instead of matching exact keywords, it finds content that is similar in meaning.

This makes it the retrieval engine behind semantic search and RAG. When a user asks a question, the system embeds it, the vector database returns the closest passages, and a model uses them to answer. Good indexing keeps this fast even across millions of documents.

arosplatforms selects and tunes the vector store as part of the broader retrieval architecture, balancing accuracy, latency, and cost. We keep the choice modular so the storage layer can evolve as your data grows without forcing a rewrite of the application above it.