Nani AGI: A Personalized Multimodal AI Architecture

Abstract

Nani AGI is a personalized, multimodal, and proactive artificial intelligence architecture designed to support natural and continuous human–AI interaction beyond conventional question-and-answer systems. The proposed architecture integrates large language model-based conversational reasoning with persistent memory, voice interaction, emotion-aware processing, multimodal perception, personalization, task planning, and authorized computer-level automation within a unified framework. The methodology follows a modular system-development approach in which perception, cognition, memory, decision-making, and action are organized as interoperable layers. The proposed implementation uses Python-based speech processing, language identification, speech synthesis, large language model services or compatible inference frameworks, and operating-system automation interfaces. The evaluation framework defines task completion, contextual continuity, personalization, intent recognition, response latency, and interaction effectiveness as principal measures, with comparative and ablation-based testing proposed for future empirical validation. The architectural analysis provides a structured foundation for more adaptive and user-centric AI assistants. The work contributes an AGI-inspired personal AI architecture and identifies future research directions in autonomous planning, multimodal reasoning, long-term memory, privacy, and real-world system integration.