EdgeForge AI: Explainable Hybrid AI Router

EdgeForge AI addresses a major enterprise AI challenge: choosing between local AI for privacy and cloud AI for advanced reasoning. Instead of forcing a single approach, it uses an explainable hybrid routing engine that intelligently decides whether each request should run locally on AMD-powered hardware, in the cloud, or through a hybrid workflow. Every routing decision is transparent. The platform evaluates privacy sensitivity, request complexity, local hardware capability, confidence, latency, and estimated cost before selecting the optimal execution path. Sensitive information is automatically detected, masked, and protected before any cloud processing occurs. The Explainability Dashboard helps users understand every decision through an Executive AI Briefing, Routing Decision Graph, Route Comparison Matrix, Privacy Viewer, Trust Analytics Dashboard, and detailed execution traces. The backend is built with FastAPI using Clean Architecture, while the frontend uses React, TypeScript, and Tailwind CSS. The platform includes modular routing strategies, provider abstraction with automatic fallback handling, analytics collection, and real-time observability. EdgeForge AI is designed for privacy-sensitive industries including healthcare, financial services, manufacturing, cybersecurity, and smart cities. Instead of sending every request to the cloud, EdgeForge AI keeps suitable workloads local to reduce latency, API costs, and data exposure, while using cloud models only when additional reasoning capability is required. Key capabilities include explainable AI routing, hybrid local/cloud execution, privacy-aware prompt masking, executive decision summaries, interactive routing visualization, trust analytics, resilient provider fallback, and an enterprise-ready interface. EdgeForge AI demonstrates how AMD-powered edge computing and modern foundation models can work together to deliver secure, transparent, and cost-efficient enterprise AI.