Control what enters the intelligence loop.

RetrievalControl names a critical layer in production AI: the boundary between an intelligent system and the knowledge it can access. Semantic relevance alone does not establish authorization. Retrieval infrastructure increasingly needs identity, permissions, classification, tenant boundaries, source trust, policy, freshness, and provenance to determine which information may actually enter an agent’s context.

RetrievalControl.com has unusually clear positioning for secure RAG and agentic infrastructure. Enterprise security guidance increasingly treats retrieval authorization as its own enforcement problem because vector similarity determines relevance, not whether the requesting identity should receive the information. Production architectures therefore introduce retrieval-time authorization, source boundaries, classification filters, tenant isolation, audit logging, and policy enforcement before retrieved content reaches the model. RetrievalControl could anchor a RAG security company, retrieval authorization layer, context firewall, agent knowledge gateway, vector governance platform, enterprise search control plane, or policy engine positioned directly between AI systems and organizational knowledge.