Not every business task carries the same financial weight.
That is why, at AINNA, we look at 智能路由 AI as a cost-control mechanism, not just a technical 设计.
Rather than routing every request straight to the most expensive AI model, the 系统 assigns work through a clear execution hierarchy:
规则 → 解析器 → 自动化 → SLM → LLM
高-volume, repetitive work should never consume premium inference capacity.
Structured transactions and rules-based processes should be settled deterministically.
Only judgement-heavy exceptions and complex reasoning should reach the larger models.
The objective is not to spend more on AI.
The objective is to match the right intelligence, to the right layer, for the right financial outcome.
For Malaysian SMEs, this translates into lower token consumption, reduced compute load, faster closing and reconciliations, and more predictable monthly operating costs.
Classify first. Automate where possible. 升级 only when justified.
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