Nutricoach Pro: An Agentic Real-Time Retrieval-Augmented Generation and Multimodal Intelligence System for Precision Clinical Nutrition and Metabolic Health Tracking

Metabolic syndrome is a growing global cardiometabolic burden and is increasingly accompanied by demand for continuous, personalized, and evidence-grounded nutrition support. A 2025 systematic review and modelling analysis estimated that 1.54 billion adults were living with metabolic syndrome in 2023, with global prevalence estimated at 28.4% across women and men. Conventional nutrition trackers provide structured quantitative logging, but manual food search, portion selection, and fragmented workflows can increase interaction burden. Large language models (LLMs) provide a natural conversational interface, yet unrestricted generation can produce unsupported nutritional or clinical statements. This paper presents NutriCoach Pro, an agentic nutrition-intelligence architecture that separates deterministic computation, verified food data, evidence retrieval, multimodal perception, and language generation. The system combines a Mifflin-St Jeor energy-estimation engine, activity-adjusted energy expenditure, configurable macronutrient targets, hydration tracking, a bounded Health Compliance Score, deterministic multi-item food parsing, multimodal meal recognition, a sevendomain clinical knowledge base, local retrieval-augmented generation (RAG), optional cloud-model adapters, schema-controlled database actions, and WebSocket-based real-time synchronization. A prototype evaluation plan is defined for NLP extraction, calorieestimation error, unsupported clinical claims, latency, multimodal recognition, and user task performance. Project-reported prototype figures supplied for this manuscript are F1=96.4%, mean absolute calorie error=12.3 kcal, local-RAG latency=42.1 ms, WebSocket synchronization latency=28.4 ms, and unsupported-claim rate below 1.2%; these values are explicitly treated as provisional until reproduced with a documented held-out test protocol. The contribution is an explainable, modular, privacy-oriented architecture that places hard numerical and safety boundaries around generative Al rather than treating an LLM as the source of truth or as an autonomous Clinical decision maker