A Protected Organizational Intelligent Assistant for Purchasing Analytics Integrating ERP and Supplier Management Platforms via Retrieval-Augmented Methods and API Management Layer

Abstract

The Modern enterprise procurement ecosystems are increasingly characterized by distributed architectures, heterogeneous data sources, and rapidly evolving supplier networks. Despite significant advances in Enterprise Resource Planning (ERP) and Supplier Relationship Management (SRM) platforms, organizations continue to face challenges in achieving unified purchasing intelligence, real-time decision automation, and secure cross-platform interoperability. This research proposes a Protected Organizational Intelligent Assistant (POIA) designed to enhance purchasing analytics by integrating ERP systems and supplier management platforms through Retrieval-Augmented Generation (RAG) methods and an API management layer.

The proposed architecture emphasizes secure orchestration of enterprise data streams while enabling contextual intelligence for procurement decision-making. The assistant leverages semantic retrieval mechanisms to unify structured and unstructured procurement data, enabling enhanced visibility across sourcing, contract management, and supplier performance evaluation. Additionally, API gateway integration ensures controlled and scalable interoperability between SAP-based ERP environments and external procurement ecosystems.

Existing literature highlights the importance of integrated supply chain systems in improving operational efficiency and decision accuracy (Akkermans et al., 2020). However, gaps remain in achieving intelligent autonomy and contextual reasoning across distributed procurement infrastructures. The proposed model addresses this gap by combining semantic intelligence frameworks with enterprise-grade integration layers. Prior studies also emphasize decision-support systems in complex logistics environments, reinforcing the relevance of intelligent orchestration mechanisms in enterprise systems (D. J. Power, 2001).

Furthermore, the research builds upon advancements in intelligent procurement systems and AI-driven enterprise assistants, particularly in secure and context-aware architectures (Venkiteela, 2025). This study extends such frameworks by incorporating retrieval-augmented reasoning and API-mediated governance for enterprise-scale deployment.

The findings suggest that the integration of RAG-based intelligence with ERP-SRM interoperability significantly enhances procurement transparency, reduces decision latency, and improves supplier risk assessment. The study contributes a scalable architectural blueprint for next-generation procurement intelligence systems, offering both theoretical and practical implications for digital enterprise transformation.