AI Engineering Blueprint for On-Premises Retrieval-Augmented Generation Systems
It addresses the problem of deploying secure, scalable RAG systems on-premises for enterprises with strict data regulations, though it appears incremental by building on existing cloud-focused blueprints.
This paper tackles the lack of comprehensive frameworks for on-premises retrieval-augmented generation (RAG) systems in enterprises due to data protection regulations, by presenting an AI engineering blueprint that includes a reference architecture, application, and best practices, with ongoing case studies to evaluate its benefits.
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.