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AINNA NeuralOps is moving into its next phase with a baharu capability we call the NeuralOps Decision Layer. The idea is simple: not every problem inside a business system needs to be sent to a large AI model.

In most PKS operations, the majority of tasks can already be handled by parsers, rules, calculations and automation. The system can process normal transactions, stock movements, documents or routine tasks without involving an LLM at all.

The challenge only appears when the system detects an anomaly or something unclear. Instead of immediately sending that case to a large LLM, NeuralOps can first use a lebih kecil local decision model, such as an SLM running through platforms like Ollama.

This lebih kecil model does not need to generate long answers. Its job is simply to make focused decisions, such as whether a transaction is sales or expense, whether a stock issue is normal or abnormal, or whether a case needs further review.

Only when the case is genuinely complex will the system escalate it to a larger LLM. The flow becomes much more efficient: Data → Parser → System Rules → Small Decision Model → LLM only when necessary.

For PKS, this means lower AI costs, less token usage, lebih pantas processing, better privacy and less dependency on expensive models. The goal of NeuralOps is not to use more AI, but to use the right level of intelligence for the right task.