The Q-Engine Architecture.
A proprietary orchestration layer designed for high-fidelity Retrieval Augmented Generation (RAG) and zero-retention inference. We bridge the gap between your raw data and state-of-the-art Large Language Models.
The RAG Pipeline
Public models hallucinate because they rely on training data memory. QUAICU relies on your documents.
Step 1: Ingestion
We connect natively to your data silos. QUAICU parses unstructured text, splits it into semantic chunks, and converts them into high-dimensional vector embeddings.
Step 2: Retrieval
When a user queries the system, QUAICU performs a semantic search and applies a Cross-Encoder Reranker to grade the results for relevance.
Step 3: Generation
The system constructs a prompt with retrieved context. The model generates clickable citations. If the answer isn't found, it returns "Data Not Found".
Your Data. Your Perimeter.
Zero-Retention
We do not log your prompts. We do not use your inputs to train models. Your data exists in memory only for milliseconds.
RBAC
QUAICU respects your existing permission structures. We mirror your Active Directory or SSO permissions.
Encryption
All data is encrypted at rest (AES-256) and in transit (TLS 1.3). Vector stores are isolated per tenant.