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

The Problem: Context Rot in Production RAG Pipelines

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 The silent killer is "Context Rot": retrieval pipelines returning semantically perfect but factually outdated context - superseded docs, old API specs.

The Solution: KU-Gateway

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.

Stress Test: Zero to Revenue Challenge

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
• Build autonomous agents that utilize time-gated context

Resources

• Repo for the math: https://github.com/VLSiddarth/KU-Gateway.git
• Join the stress-test/hackathon: https://api.knowledgeuniverse.tech/