Neum AI is a framework for managing vector embeddings at large scale to power LLM contextualization via RAG. It extracts data from document storage and NoSQL sources, processes contents into vector embeddings using configurable chunking and selection strategies, and ingests them into vector databases for similarity search. The platform includes built-in connectors for common data sources, embedding services, and vector stores, supports real-time data synchronization, and provides distributed architecture for high-throughput processing.