Private On-Premise AI Reference Architecture
A general reference pattern for organizations handling sensitive data who cannot rely on a public AI API.
The pattern below is a general, widely-used reference for deploying AI on infrastructure you control, not a description of any specific client's implementation. The right sizing, model choice, and security controls depend on your actual data sensitivity and compliance requirements.
Ingestion & Preprocessing
Internal documents/data (SOPs, machine manuals, reports) are cleaned and chunked inside the local network — never sent to a public API.
Vector Database
Chunks are converted to embeddings and stored in an on-premise vector database, keeping context retrieval entirely on internal infrastructure.
Local Model Inference
The language model runs on-premise (or a closed private cloud), sized to available GPU/CPU capacity and latency requirements.
Application Layer
The interface (internal chat, dashboard, API integrations) accesses the model through an internal gateway with logging and an audit trail.
Security & Access
Role-based access control, encryption at rest and in transit, and network segmentation limit who can touch sensitive data.
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