Many people think Smart Routing in AI is simply about choosing which model to use. In NeuralOps, we see it differently. Smart Routing determines the most appropriate type of inference for each task. Some tasks require no AI inference at all and can be handled through deterministic logic, business rules, database queries, or specialised parsers. Others may require an SLM for classification and intent detection, a local LLM for more complex private-data reasoning, a cloud LLM for difficult cases, or multiple specialist agents for higher-complexity workflows.
The router evaluates factors such as task type, privacy, cost, latency, confidence, context size, and operational risk before deciding the execution path. A simple order-status lookup should not consume LLM tokens. Invoice extraction may only require a parser. Transaction classification may be handled by a small model. Larger models should only be activated when the task genuinely requires deeper reasoning.
This is why, in NeuralOps, Smart Routing is not just Model Routing. It is Inference Routing + Execution Routing + Validation Routing.
The objective is not to use as much AI as possible. The objective is to use the smallest, most efficient and most reliable level of intelligence required for each task.
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