Hybrid AI Router with Dynamic LLM Selection

Hybrid AI Router is an intelligent orchestration platform designed to optimize AI inference by dynamically routing user queries to the most appropriate language model. Instead of relying on a single model for every request, the system analyzes each query and determines whether it can be efficiently handled by a lightweight local language model or should be forwarded to a powerful cloud model for advanced reasoning. The platform focuses on four key objectives: Reduce latency by processing simple requests locally. Lower inference cost by minimizing unnecessary cloud API usage. Preserve privacy by keeping sensitive or lightweight tasks on-device. Maintain response quality by routing complex reasoning tasks to a larger cloud model. The application includes a modern web dashboard that displays: Selected AI model Query complexity Confidence score Response latency Evaluation status Generated response The backend is built with FastAPI and exposes REST APIs that communicate with both the local and cloud language models. The frontend provides an intuitive interface for testing routing decisions and visualizing AI performance metrics in real time. This project demonstrates how hybrid AI systems can balance performance, privacy, and operational cost while delivering an improved user experience.