Retrieval-Augmented Generation for Trusted Customer Intelligence: A Scalable Enterprise Architecture for CRM Knowledge Management
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I3P109Keywords:
Retrieval-Augmented Generation, Customer Relationship Management, CRM Knowledge Management, Customer Intelligence, Enterprise AI Architecture, Trusted AI, Sales Intelligence, Knowledge Graphs, Large Language Models, Human-AI CollaborationAbstract
Enterprise customer relationship management systems increasingly operate as repositories of fragmented customer knowledge rather than as unified intelligence platforms. Sales representatives, service agents, customer-success teams, marketing analysts, and executives rely on CRM data, call transcripts, support tickets, contracts, product documentation, knowledge articles, renewal histories, and external market signals, yet these assets are often distributed across incompatible schemas, permissions, and narrative formats. Large language models offer new opportunities for conversational customer intelligence, but their direct use in CRM environments introduces unacceptable risks, including hallucinated recommendations, privacy exposure, stale knowledge, weak auditability, and limited alignment with enterprise decision governance. This paper proposes a scalable retrieval-augmented generation architecture for trusted customer intelligence in enterprise CRM knowledge management. The proposed framework integrates hybrid retrieval, semantic indexing, policy-aware document ingestion, customer-entity resolution, vector and graph-based knowledge representations, evidence-grounded generation, human-in-the-loop feedback, and trust-oriented observability. The architecture is designed to support account planning, opportunity qualification, customer service resolution, churn-risk explanation, sales enablement, executive decision support, and knowledge reuse across customer operations. A design-science methodology is adopted to formalize the artifact, decompose architectural layers, define trust controls, and establish evaluation metrics for retrieval quality, answer faithfulness, decision usefulness, latency, scalability, compliance, and user adoption. The analytical discussion shows that enterprise RAG is not merely an LLM enhancement pattern but a socio-technical knowledge-governance architecture that transforms CRM from a passive transaction system into a source-grounded intelligence environment. The study contributes a conceptual model, evaluation framework, and implementation roadmap for organizations seeking to deploy generative AI in customer-facing functions without compromising factual reliability, security, or managerial accountability.
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