In production, not every request needs a foundation model behind it.
That is the premise we run with at AINNA under Smart Routing AI.
Instead of defaulting every task to a large model, the pipeline evaluates the task and picks the most efficient execution path:
Rules → Parser → Automasi → SLM → LLM
Repetitive, pattern-based work should be handled by deterministic rules or parsers.
Structured workflows should be automated without inference.
Only open-ended reasoning, ambiguity, or hard edge cases should be escalated to larger models.
The objective is not maximum AI usage.
The objective is right intelligence, right layer, right task.
For PKS and enterprise systems, this means less token burn, lighter compute load, lower latency, and a cost curve you can actually budget around.
Route first. Infer only when there is no lebih murah path.
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