EdgeGuard: Cost-Optimized Hybrid AI Router

Enterprise AI deployment faces a massive scaling issue: sending every minor query to massive, paid cloud models drains API budgets rapidly. To solve this for Track 1, I built a highly optimized hybrid AI orchestration backend designed around strict token efficiency. The system architecture intercepts incoming queries and utilizes a lightweight, local LLM as a dynamic intent classifier. If a query is categorized as basic general knowledge or summarization, the local edge engine handles the generation entirely for zero cost. It then runs a self-evaluating cascade to ensure output quality. If the edge model fails its self-evaluation, or if the initial query is classified as high-complexity (like advanced programming or mathematics), the agent instantly escalates the prompt to the highly capable Fireworks AI API. To maximize efficiency, the agent integrates a local hashing cache for instant zero-cost returns on repeated queries. Finally, a persistent telemetry engine runs in the background, logging execution times, routing paths, and exact "Tokens Saved" metrics to a CSV database, providing hard, measurable data to prove the system's cost-saving capabilities.