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.
@article{arxiv.2604.01395,
title = {AI Engineering Blueprint for On-Premises Retrieval-Augmented Generation Systems},
author = {Nicolas Weeger and Jakob Winkler and Annika Stiehl and Jóakim von Kistowski and Christian Uhl and Stefan Geißelsöder},
journal= {arXiv preprint arXiv:2604.01395},
year = {2026}
}