AI Engineering Blueprint for On-Premises Retrieval-Augmented Generation Systems
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911562111909888 |
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| author | Weeger, Nicolas Winkler, Jakob Stiehl, Annika von Kistowski, Jóakim Uhl, Christian Geißelsöder, Stefan |
| author_facet | Weeger, Nicolas Winkler, Jakob Stiehl, Annika von Kistowski, Jóakim Uhl, Christian Geißelsöder, Stefan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01395 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI Engineering Blueprint for On-Premises Retrieval-Augmented Generation Systems Weeger, Nicolas Winkler, Jakob Stiehl, Annika von Kistowski, Jóakim Uhl, Christian Geißelsöder, Stefan Software Engineering 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. |
| title | AI Engineering Blueprint for On-Premises Retrieval-Augmented Generation Systems |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2604.01395 |