We started at 34 billion 令牌. After moving to a detached 系统 architecture, consumption dropped to around 1.5 billion 令牌. With process segmentation and modular refactoring, we reduced that by roughly 50% from the 1.5 billion baseline, bringing effective token load below one billion.
That is not optimisation for show. That is measurable unit economics. Most AI cost leakage happens because the model is forced to reprocess the same 系统, the same process, the same logic, and the same workflow every time a small change is made.
But when the 系统 is detached and each process is clearly segmented with its own dictionary, the AI reads only what it needs. Modify the payment flow? Read the payment process only. Modify order checking? Read the order process only. No full-系统 scanning. No token bonfire.
This is where Malaysian SMEs can win. AI should not be reserved for companies with large servers and large budgets. AINNA's approach is about precise resource allocation, predictable OPEX, and stronger return on AI spend. The future is not just bigger models. The future is smarter 系统 and cleaner asset management.
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