A new open-source proxy called KU-Gateway is targeting a persistent failure mode in enterprise RAG pipelines: context rot. The problem occurs when vector databases retrieve semantically relevant but factually outdated documents — superseded API specs, deprecated policies, or obsolete product details — that then pollute LLM outputs.
Built by researcher V.L. Siddarth, the proxy sits between the vector store and the language model, applying a mathematical temporal decay score to each retrieved chunk. Stale payloads are physically dropped before synthesis, preventing them from consuming context window space or triggering hallucinations.
During an early access program with a major technology company's managed agents team, the approach reduced token consumption by approximately 50 percent while deterministically blocking stale-data hallucinations. The system is now entering a 14-day public stress test dubbed the "Zero to Revenue" challenge, inviting developers to probe the routing logic and build agents that leverage time-gated context.
The implications extend beyond cost savings. Enterprises deploying RAG at scale have struggled with governance around document freshness, often resorting to manual curation or expensive re-indexing cycles. A deterministic, automated filter could shift that burden from operations to infrastructure.
