An advanced RAG architecture where an AI agent autonomously decides which questions to ask, which tools to use, when to retrieve information, and how to aggregate results. Represents a major trend in 2026 for more intelligent and adaptive retrieval systems.

State-of-the-art AI agent memory system using ASMR technique that achieved ~99% accuracy on LongMemEval benchmark with multi-agent orchestrated pipeline.

Multi-modal tensor DB for vectors/images/texts/videos with hybrid embedding + metadata/tensor search. Supports multimodal RAG datasets with versioning. Data lake scale vs pure vector stores like Qdrant.

Graph-vector DB for multimodal data (images/videos/docs/embeddings) with hybrid vector similarity + graph traversal + metadata/keyword filtering. Enables complex multimodal RAG queries. Combines FAISS vectors with graphs unlike pure vector DBs like Qdrant.

Built into the Salesforce platform, Data Cloud Vector Database ingests various large datasets from customer interactions, classifies and organizes unstructured data, and merges it with structured data to enrich customer profiles and store as metadata in Data Cloud. It enhances generative AI by providing more relevant, accurate, and up-to-date responses through improved data retrieval and semantic search capabilities.

Instaclustr offers comprehensive managed services for vector databases, handling deployment, configuration, ongoing maintenance, tuning, optimization, scalability, security, and data protection. This allows organizations to offload the complexities of managing their vector database infrastructure and focus on their core business objectives.