Designing Backend-Centric Architectures for Retrieval-Augmented Enterprise Data Platforms

Abstract

The increasing adoption of retrieval-augmented systems in enterprise environments has intensified the need for architectural designs that can reliably manage large-scale, heterogeneous, and governed data. This study investigates backend-centric architectures as a foundational approach for designing retrieval-augmented enterprise data platforms. Using a design science–oriented methodology, multiple backend architectural configurations were evaluated across performance, reliability, and governance dimensions under representative enterprise workloads. The results demonstrate that architectures emphasizing modular backend services, hybrid and semantic indexing strategies, and policy-aware orchestration achieve superior retrieval efficiency, scalability, fault tolerance, and compliance effectiveness compared to monolithic or frontend-driven designs. Structural analysis further reveals distinct architectural patterns that align with progressive improvements in system behavior and enterprise trustworthiness. The findings highlight that backend-centric design not only enhances retrieval accuracy and responsiveness but also enables robust governance without imposing prohibitive overhead. This study provides a systematic architectural perspective and practical guidance for developing scalable, reliable, and trustworthy retrieval-augmented enterprise data platforms.