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

2026-04-01

AI Summary

This work addresses the challenge enterprises face in adopting cloud-based Retrieval-Augmented Generation (RAG) systems due to stringent data compliance requirements, compounded by the absence of a standardized reference architecture for on-premises deployment. To bridge this gap, we propose the first enterprise-grade, on-premises RAG engineering framework grounded in the 4+1 architectural view model. Our solution encompasses an end-to-end reference architecture, a deployable open-source reference application, a localized toolchain, and a customized CI/CD pipeline. This comprehensive framework fills a critical void in the standardized implementation of enterprise-scale, on-premises RAG systems. The complete blueprint and source code have been open-sourced on GitHub, and preliminary validation through industry partnerships and expert interviews demonstrates its practical utility and feasibility.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Consent frameworks and practices on the web

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.

Problem

• Retrieval-Augmented Generation
• on-premises deployment
• enterprise AI
• data protection regulations
• AI engineering blueprint

Innovation

• on-premises RAG
• enterprise AI architecture
• 4+1 view model
• CI/CD for AI
• retrieval-augmented generation