AI Engineering Blueprint for On-Premises Retrieval-Augmented Generation Systems

TL;DR

This paper presents a comprehensive blueprint for deploying scalable, enterprise-grade retrieval-augmented generation systems on-premises, addressing data privacy concerns and integration challenges.

Contribution

It introduces an end-to-end reference architecture, a reference application, and best practices for tooling and deployment, all tailored for on-premises RAG solutions.

Findings

Blueprint facilitates on-premises RAG deployment in enterprises.

Provides publicly available reference architecture and application.

Includes best practices for tooling and CI/CD pipelines.

Abstract

Retrieval-augmented generation (RAG) systems are gaining traction in enterprise settings, yet stringent data protection regulations prevent many organizations from using cloud-based services, necessitating on-premises deployments. While existing blueprints and reference architectures focus on cloud deployments and lack enterprise-grade components, comprehensive on-premises implementation frameworks remain scarce. This paper aims to address this gap by presenting a comprehensive AI engineering blueprint for scalable on-premises enterprise RAG solutions. It is designed to address common challenges and streamline the integration of RAG into existing enterprise infrastructure. The blueprint provides: (1) an end-to-end reference architecture described using the 4+1 view model, (2) a reference application for on-premises deployment, and (3) best practices for tooling, development, and CI/CD pipelines, all publicly available on GitHub. Ongoing case studies and expert interviews with industry partners will assess its practical benefits.