RAG Architecture & Strict Multi-Tenant Isolation
Discover how Nexuro AI combines high-dimensional vector search, strict database-level Row Level Security (RLS), and context-bound LLM prompts to guarantee 100% accurate, non-hallucinating responses.
User message is converted into a 1536-dimensional embedding vector.
Calculates Top-5 nearest neighbors in Postgres filtered strictly by workspace_id.
Context is injected verbatim into the system prompt with strict fallback.
1. Multi-Tenant Isolation via PostgreSQL RLS
Workspace Boundary Protection
Every document vector and chat query is locked to your organization's encrypted workspace identifier, preventing cross-tenant vector leakage.
Private Query Execution
Semantic vector matches are evaluated within secure runtime boundaries without sharing underlying embedding spaces across accounts.
2. Zero-Hallucination Guardrails
Traditional chatbots frequently fabricate answers when unsure. Nexuro AI applies three defensive layers to eradicate hallucinations:
Strict Context Injection
Only text chunks retrieved from your verified documents are presented to the model as truth.
Controlled Temperature
Temperature is locked to ≤ 0.3 for factual accuracy, eliminating generative unpredictability.
Mandatory Fallback
If cosine similarity falls below threshold, the bot replies: "I don't have that information in my documentation."