Modern AI applications often route every request to a single large language model, leading to high costs, increased latency, and inefficient resource utilization. Simple tasks such as summarization or extraction do not require expensive frontier models, while complex reasoning tasks demand higher-quality models. Existing systems lack an intelligent mechanism to dynamically select the most suitable model based on task complexity, quality requirements, cost, and latency constraints. We are solving this problem by building an intelligent AI routing layer that automatically analyzes each request and directs it to the optimal LLM, ensuring high-quality responses while minimizing cost and improving reliability. The system also provides observability, fallback mechanisms, and routing transparency, enabling enterprises to scale AI applications efficiently without sacrificing performance.
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