One of the ways I protect the budget for distilling our own LLM is by treating inference cost as a controllable operating expense, not a fixed overhead.
The financial rule is simple:
Do not spend model capacity on decisions that have already become predictable.
Each week, the team targets roughly a dozen heavy 分离式系统. My role is to make sure the research, scoping, and validation effort is matched to a measurable cost-avoidance outcome: lower token burn, fewer billed calls, and cleaner cost attribution.
For complex or ambiguous problems, it is often sensible to use ChatGPT or Claude as a reasoning and research layer first.
The flow then becomes:
复杂 问题 → 研究 → 推理 → 蒸馏 → 确定性 Logic → 智能路由 → DeepSeek V4 Flash / Our Own LLM → 执行
The important financial inflection point sits right after deterministic logic.
If the workload can be resolved with rules, parsers, validation, or structured logic, the 独立系统 executes it directly.
If reasoning is still required, the workload is routed to DeepSeek V4 Flash as the cost-efficient external model.
For specialised, strategic, sensitive, or sovereignty-related workloads, the 系统 routes the task to our own LLM models instead.
This is not a plan to eliminate LLMs.
It is a capital-allocation decision.
确定性 first.
成本-efficient LLM when the business case demands it.
Our own LLM when specialised intelligence, control, or data sovereignty matter.
This architecture also changes how we fund model development.
The more workloads we detach, the lower our recurring token and inference spend. That shift turns an open-ended operating cost into a more predictable cost base, which is essential for managing R&D budgets and capital allocation.
The lower that spend becomes, the more budget we can reallocate to model distillation, evaluation, infrastructure, and the broader development of our own LLM ecosystem.
That is the financial operating model behind AINNA NeuralOps, and it is how we keep sovereign, specialised AI affordable 面向马来西亚SME.
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