Submitted to: Rob LoCascio, CEO, Uare.ai Submitted by: Wolfgang Höltgen, user since the Eternos migration (3 years) Via: Featurebase Feedback Portal The Problem: The current platform offers only two access levels: private (creator only) or public (everyone). There is no intermediate layer. Role-Based Access Control (RBAC) at platform level would require maintaining multiple separate twin instances updated independently, which is not scalable. What Already Exists: My Retrieval Skill already defines seven roles with corresponding filter markers embedded in the training corpus: #Filter_Kind_unter_18_Start/End, #Filter_Familie_Start/End, #Filter_Ehefrau_Start/End, and others. The logic is ready. The platform enforcement is missing. What I Need: A middleware layer between the retrieval process and the LLM that: Reads the authenticated user's assigned role Retrieves chunks from the knowledge base as usual Filters out any chunk containing a #Filter_[Role] marker that excludes that role, before passing content to the LLM Passes only the permitted content to the LLM for response generation_ This is classical attribute-based access control applied to the retrieval pipeline. The filtering logic is a simple string match on start/end markers. One corpus, one twin, multiple filtered views depending on the authenticated role. Business Relevance: This requirement is universal across professional and enterprise deployments. A consultant cannot expose client-confidential data to all subscribers. A CEO cannot grant board-level access to all employees. A doctor cannot give an assistant access to patient records. Without content-level ABAC, the platform cannot serve professional and enterprise use cases. This is a foundational requirement, not a feature enhancement. Requested outcome: Is this architecture on the roadmap? If yes, what is the estimated timeline?