Project Overview
Traditional enterprise RAG (Retrieval-Augmented Generation) systems mostly stop at: Query → Retrieve → Generate. But in real production environments, the problem is often not model capability, but: retrieval result version conflicts, context missing or pollution, multi-source knowledge inconsistency, inability to trace error sources, inability to close-loop repair knowledge base.
In enterprise policy libraries, operations SOP, API documentation, multi-version specification systems, wrong Context is more dangerous than wrong answers.
Therefore I designed: Nexus × Neurofy — A RAG Governance Operating System for enterprise knowledge governance.
The core question: "Why do current Agent/RAG systems assume Retrieval is trustworthy by default?"
Most AI applications do: output Guardrail, Prompt restrictions, user input filtering. But almost never: govern Context itself, detect knowledge conflicts structurally, audit retrieval quality at runtime.
So I tried to make: Retrieval → Governance → Generation into a truly runtime-based architecture.
The goal is not: "make a chatbot". But: "make a governable, traceable, repairable AI Runtime".
| System | Positioning |
|---|---|
| Nexus | RAG Governance Runtime |
| Neurofy | Observability & HITL Client |
Neurofy is the Runtime Studio for Nexus, providing Graph visualization, Trace viewing, NDJSON streaming event consumption, and Agent Runtime debugging.