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

A research team from the University of Würzburg and other institutions has released a pioneering blueprint specifically for building enterprise-grade Retrieval-Augmented Generation (RAG) systems on-premises. Published on arXiv and accepted at the ICSA 2026 conference, the work addresses a critical gap: while RAG—which grounds AI responses in retrieved documents—is popular, most existing architectures assume cloud deployment. For finance, healthcare, and legal firms bound by GDPR or other data sovereignty laws, this is a non-starter. The blueprint provides a complete, open-source framework to deploy these AI systems entirely within a company's own secure infrastructure.

The blueprint is structured around three core components. First, it offers an end-to-end reference architecture described using the professional 4+1 view model, covering logical, process, development, and physical views plus key scenarios. Second, it includes a fully functional reference application that organizations can deploy and adapt. Third, it details best practices for the entire development lifecycle, including tooling and CI/CD pipelines for robust, maintainable systems. All code and documentation are publicly available on GitHub, enabling immediate practical use.

This work moves beyond theoretical discussion to provide actionable engineering guidance. The researchers are validating the framework through ongoing case studies and expert interviews with industry partners to assess its real-world benefits and scalability. For any organization prohibited from sending sensitive data to third-party APIs like OpenAI or Anthropic, this blueprint provides the missing manual for building powerful, private, and compliant AI assistants that leverage internal knowledge bases.