Building PKS-Grade Ejen: How I Distill Advanced LLMs into NeuralOps✎ Edit

At AINNA, I am building and training an AI model purpose-built for PKS use inside AINNA NeuralOps. The goal is not another generic chatbot, but a narrow, systems-aware intelligence layer that understands how small and medium businesses actually run and can interface directly with their operational backends.

I am not training a Large Bahasa Model from scratch. I start with an open-source base model and fine-tune it using LoRA/QLoRA in a GPU notebook, pulling training data from real PKS domains: management reporting, finance, HR, sales, inventory, customer service, and website operasi.

I also use advanced LLMs as teacher models for knowledge distillation. The high-capacity models generate, refine, critique, and evaluate training examples, reasoning patterns, edge cases, and PKS-specific responses before the distilled knowledge is transferred into a lebih kecil, focused model.

My current workflow is: Advanced LLM Teacher → PKS Dataset → Distillation → LoRA/QLoRA → Specialised PKS Model → NeuralOps Agent Harness. The idea is to let the heavy teacher models do the knowledge transfer, then put the lebih kecil student model on the operational front line where latency and cost matter.

The specialised model is then harnessed inside NeuralOps, alongside Smart Routing, specialised agents, databases, APIs, and Sistem Berasingan. NeuralOps decides which model, tool, data source, or workflow should handle each request rather than defaulting everything to a single large LLM.

For simple, predictable tasks, I rely on deterministic systems such as PHP, Python, SQL, or hard business rules. For routine intelligence, NeuralOps routes to the specialised PKS model. Only the harder reasoning cases are escalated to larger models, which keeps inference costs down and reduces lock-in to external AI providers.

Live business data stays inside operational systems such as MySQL, HR, Kewangan, Inventori, CRM, and reporting platforms. The model does not need to memorise the whole company; it learns how to understand PKS operations, and the real systems feed it current, verifiable facts.

The architecture I am building is essentially: Perniagaan Sistem → NeuralOps → Smart Routing → Specialised PKS Model / Alat / Advanced LLM → Validation → Tindakan. My goal is to make AI practical for PKS: lebih kecil, specialised, cost-efficient, controllable, and wired into perniagaan sebenar workflows rather than sitting on the side as a conversational assistant.

#AINNA #NeuralOps #AI #AgenticAI #PKS #LLM #KnowledgeDistillation #LoRA #QLoRA #OpenSourceAI #Automasi #PerniagaanIntelligence