The Architecture Behind Agentix AI's Zero-Data-Leak Boundary
Take a deep dive into the engineering behind our permission-aware Retrieval-Augmented Generation (RAG) pipeline.
The Challenge of Access Controls in AI
One of the hardest problems in building enterprise AI is respecting existing access controls. If an intern asks an LLM for the company's financial projections, the model shouldn't answer unless that intern has explicit permission to view the source documents. Traditional LLM training bakes data directly into the model's weights, making targeted access impossible.
Dynamic Permission Resolution
Agentix AI solves this by completely decoupling the intelligence (the LLM) from the knowledge (your data). When a query is made, our routing layer first authenticates the user. It then performs a semantic search exclusively across documents the user is authorized to view. The LLM is only provided the context from these authorized chunks.
Stateless AI Inference
Furthermore, the inference instances running the LLM are stateless. Once an answer is generated, the context window is immediately flushed. We guarantee a Zero-Data-Leak boundary, ensuring that sensitive documents from one department can never inadvertently bleed into the answers provided to another.
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The Architecture Behind Agentix AI's Zero-Data-Leak Boundary
