Enterprise RAG Tool

K2view’s Micro-Database™ technology organizes structured data by your business entities, like customers, orders, and loans — enabling K2view RAG to retrieve it with semantic awareness of schemas and relationships, all at conversational speed.

At the same time, unstructured content such as docs, emails, and logs is chunked and vectorized into entity-based or generic Micro-DBs. This dual capability means structured and unstructured data are accessible side by side, giving GenAI a unified, context-rich foundation for retrieval.

K2view RAG enforces security and governance at the most granular level, down to individual users, entities, and data attributes.

Every retrieval request is automatically checked against your access policies, so GenAI can only retrieve and expose the information each user is authorized to see. With built-in auditing, masking, and compliance controls, your sensitive data stays protected and never leaks into LLMs — giving you the confidence to adopt RAG securely.

K2view’s RAG tool is fully open, giving you the flexibility to use it with any LLM and vector database.

Whether you’re standardizing on OpenAI, Anthropic, or open-source LLMs, or managing embeddings in third-party vector stores, K2view integrates seamlessly. This ensures your GenAI initiatives stay future-proof, without locking you into a single model or tech stack.

K2view accelerates RAG deployment so you see value in weeks, not months.

With automated discovery, entity-based design, prebuilt integrations, reusable data products, and an embedded evaluation framework, setup and maintenance are faster and simpler. The result: you launch faster, reduce costs, and scale production-grade GenAI use cases across your business.