I built a deterministic proxy to drop stale context (Cuts token burn by ~50%). Stress-testing it this week. [P]

Reddit r/MachineLearning Tools
open-source proxy rag context-rot token-efficiency enterprise vector-db

Summary

A developer built an open-source proxy (KU-Gateway) that drops stale context from vector database retrievals before LLM synthesis, cutting token burn by ~50% and preventing stale-data hallucinations. The tool is now opening for a 14-day stress test/hackathon.

Hey everyone, I’ve been researching why enterprise RAG pipelines fail in production. The silent killer is 'Context Rot', retrieval pipelines returning semantically perfect but factually outdated context (superseded docs, old API specs). I built an open-source proxy (KU-Gateway) that sits between the vector DB and the LLM. It mathematically scores context chunks for temporal decay and physically drops stale payloads before synthesis. I just ran an EAP with a major tech company's managed agents team, and it dropped their token burn by ~50% while deterministically stopping stale-data hallucinations. I’m opening up the managed API layer for a 14-day stress test (The "Zero to Revenue" challenge). I want to see if the community can break the routing logic or build autonomous agents that utilize time-gated context. Repo for the math: https://github.com/VLSiddarth/KU-Gateway.git If you want to join the stress-test/hackathon: https://api.knowledgeuniverse.tech/
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