TCP010: Blueprint for AI Training to Edge-AI Deployment

This report addresses the sophisticated engineering challenge of creating a locally-hosted, fluent, and customizable conversational AI system. It expands upon a foundational analysis that established the core architecture of a three-stage pipeline, Speech-to-Text (STT), Large Language Model (LLM), and Text-to-Speech (TTS), and defined “fluency” by the objective benchmark of a sub-500 millisecond end-to-end latency on desktop-class hardware. While achieving this on a well-resourced desktop is a significant accomplishment, the true frontier of practical AI lies in deploying such capabilities at the edge, on power and memory-constrained devices where they can interact directly with the physical world. The central problem this report tackles is the migration of this functionality from a high-resource environment to an embedded platform.