RISWIS Applied

RISWIS Applied is a governance layer for RAG and AI systems that controls which retrieved sources are allowed to influence generation. Key features include: - Trust-aware reranking for RAG pipelines - Detection of stale or low-trust retrieval results - Visible raw rank vs policy-weighted rank comparisons - Audit-friendly retrieval decision tracking - ALLOW / REVIEW / BLOCK governance decisions RISWIS sits between retrieval and generation, helping teams govern what their AI is actually allowed to trust instead of blindly accepting semantic retrieval results. What makes it different is that it separates semantic relevance from trust policy, allowing approved and auditable sources to be prioritized before context reaches the LLM. Expected outcomes include: - More trustworthy AI outputs - Reduced hallucination risk from weak retrieval - Better visibility into why an answer was generated - Easier auditing and debugging of RAG systems - Increased confidence in production AI deployments