Vector Databases store mathematical representations (embeddings) of meaning in high-dimensional space, enabling AI to understand semantic similarity between concepts, documents, and queries. They use specialised algorithms like HNSW and IVF to rapidly search billions of high-dimensional vectors in milliseconds, powering RAG systems, semantic search, recommendation engines, and anomaly detection.
The vector database market has grown rapidly with the AI boom, with platforms like Pinecone, Weaviate, Chroma, and Milvus raising significant funding. Vector databases are essential infrastructure for any organisation building RAG applications, as they provide the retrieval layer that connects LLMs to your proprietary knowledge.
BespokeWorks architects vector database solutions as part of RAG and semantic search implementations. Our deployments include embedding model selection, chunking strategy design, index optimisation, and hybrid search configurations to ensure your AI applications retrieve the most relevant information at production speed.