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Map of a useful AI Router

An AI Router is not a model list. It is a decision layer that chooses route, budget, privacy, and verification for each task.

The problem it solves

When you have local models, frontier APIs, long tasks, private content, and limited budgets, always calling the same model is poor architecture. A router chooses the path before spending tokens or exposing data.

Minimum layers

  • Task classifier: code, summary, translation, RAG, reasoning, extraction, or creative generation.
  • Privacy policy: what data may leave and what must stay local.
  • Budget: maximum cost per user, task, course, or pipeline.
  • Fallbacks: what to do when a model fails, latency rises, or quality drops.
  • Observability: traces, scores, selected model, cost, latency, and routing reason.
TerminalCode
request -> classify -> policy -> route -> model -> score -> log -> improve

Aulafy rule

The router must explain why it chose a route. If you cannot audit the decision, you do not know whether you are saving money, protecting privacy, or silently making content worse.