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I am currently building and training an AI model specifically for PKS use through AINNA NeuralOps. The objective is not to create another general-purpose chatbot, but to develop a specialised intelligence layer that understands real PKS operations and can work directly with business systems.

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, with datasets derived from real PKS use cases such as management reporting, finance, HR, sales, inventory, customer service, and website operasi.

I also use advanced LLMs as teacher models for knowledge distillation. These more capable models help me generate, improve, critique, and evaluate training examples, reasoning patterns, edge cases, and PKS-specific responses before the useful knowledge is transferred into a lebih kecil specialised model.

My current workflow is: Advanced LLM Teacher → PKS Dataset → Distillation → LoRA/QLoRA → Specialised PKS Model → NeuralOps Agent Harness. The aim is to use powerful models for teaching, while using lebih kecil models for day-to-day operational workloads.

The specialised model is then harnessed inside NeuralOps, together with Smart Routing, specialised agents, databases, APIs, and Sistem Berasingan. NeuralOps determines which model, tool, data source, or workflow should handle each task instead of sending everything to one large LLM.

For simple and predictable tasks, I use deterministic systems such as PHP, Python, SQL, or business rules. For routine intelligence, NeuralOps can use the specialised PKS model. Only more complex reasoning tasks are escalated to larger models, helping reduce unnecessary inference cost and dependency on external AI providers.

Live business data also remains inside operational systems such as MySQL, HR, Kewangan, Inventori, CRM, and reporting platforms. The model does not need to memorise the entire company. It learns how to understand PKS operations, while the actual systems provide current and verifiable information.

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 more practical for PKS: lebih kecil, specialised, cost-efficient, controllable, and integrated into perniagaan sebenar workflows rather than existing only as a conversational assistant.

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