Real-Time Voice-Driven ERP Interaction: An LLM Function-Calling Architecture for Enterprise Resource Planning Systems

Enterprise Resource Planning (ERP) systems remain, for most users, an exercise in navigating deep menu hierarchies, memorized transaction codes, and rigid form-based screens. This paper proposes and evaluates a reference architecture for real-time, voice-driven interaction with ERP systems built around large language model (LLM) function calling. The architecture couples a streaming automatic speech recognition (ASR) front end with an LLM-based orchestrator that maps natural spoken utterances to strongly typed function calls against a governed tool registry, interposes a guardrail and policy validation layer before any ERP-side effect is committed, and returns results through low-latency text-to-speech (TTS) synthesis. We describe the design of the function-calling schema, the security and audit boundary between the LLM and enterprise systems of record, and a prototype implementation spanning finance, inventory, human resources, and customer relationship management (CRM) modules. Evaluation on a corpus of 1,240 synthetic ERP task utterances shows the proposed pipeline achieving 97.2% single-function call accuracy and 91.5% accuracy on multi-step chained tasks, compared to 78.4% and 54.2% respectively for zero-shot prompting baselines, while sustaining a median end-to-end turnaround of approximately 840 milliseconds. The results suggest that schema-constrained, guardrail-mediated function calling is a practical and auditable path toward voice-native ERP interaction at enterprise scale.