Neural Graph–Augmented Retrieval for Trustworthy Generative Voice AI in Healthcare Insurance across Providers and Members

Abstract

Healthcare insurance telephony demands conversational AI systems that are simultaneously accurate, compliant, and low-latency—requirements that standalone large language models cannot reliably satisfy. This paper presents a comprehensive architectural framework for Retrieval-Augmented Generative Voice AI (RAG-VAI) specifically designed for real-time healthcare insurance interactions across members, providers, and pharmacies. The proposed system integrates semantic retrieval, neural graph-augmented knowledge reasoning, constrained generative response generation, and real-time voice execution to enable accurate, explainable, and scalable conversational interactions. Standard RAG architectures relying solely on vector similarity search are insufficient for healthcare insurance domains, where coverage rules, eligibility constraints, and benefit structures are highly relational and interdependent. The architecture introduces a neural knowledge graph layer that enriches semantic retrieval with structured reasoning over healthcare entities, enabling contextual disambiguation and coverage validation before generative response formulation. We examine knowledge modeling strategies, multi-stage retrieval pipelines, neural graph reasoning, voice-to-voice execution patterns, latency optimization techniques, and governance controls for RAG-VAI systems. The paper argues that retrieval augmentation is not merely an enhancement to generative models but a foundational control mechanism required for trustworthy, enterprise-scale voice AI in regulated healthcare insurance environments. This framework advances both the technical design of knowledge-grounded voice AI and the governance practices necessary for compliant healthcare telephony intelligence at a national scale.