AI-Based Personal Assistant System

Abstract

The established landscape of virtual personal assistants is dominated by monolithic, cloud-dependent platforms. While effective in their respective ecosystems, these systems present inherent limitations in terms of user-defined functionality, data privacy, and architectural flexibility. This paper introduces a novel framework for an AI-based personal assistant, codenamed "Archon," designed to address these limitations through a modular, on-device architecture. The proposed system, implemented in Python, utilizes a decoupled component model for speech processing, natural language understanding (NLU), and task execution. A key contribution is the implementation of a schema-driven customization engine that enables users to dynamically define new command structures and integrate them with local system functions or external APIs without requiring modifications to the core codebase. This paper presents a detailed exposition of the system's architecture, including its software stack and data flow mechanisms. A comprehensive performance evaluation is conducted, measuring key metrics such as command recognition accuracy, execution latency, and resource utilization. The empirical results demonstrate that our system provides a robust and highly performant alternative to prevailing commercial solutions, establishing a new paradigm for user-centric, privacy-conscious, and fully customizable AI assistant technology.