At AINNA, I am building and training an AI model purpose-built for SME 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 Language 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 SME domains: management reporting, finance, HR, sales, inventory, customer service, and website operations.
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 SME-specific responses before the distilled knowledge is transferred into a smaller, focused model.
My current workflow is: Advanced LLM Teacher → SME Dataset → Distillation → LoRA/QLoRA → Specialised SME Model → NeuralOps Agent Harness. The idea is to let the heavy teacher models do the knowledge transfer, then put the smaller 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 Detached Systems. 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 SME 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, Finance, Inventory, CRM, and reporting platforms. The model does not need to memorise the whole company; it learns how to understand SME operations, and the real systems feed it current, verifiable facts.
The architecture I am building is essentially: Business Systems → NeuralOps → Smart Routing → Specialised SME Model / Tools / Advanced LLM → Validation → Action. My goal is to make AI practical for SMEs: smaller, specialised, cost-efficient, controllable, and wired into real business workflows rather than sitting on the side as a conversational assistant.
#AINNA #NeuralOps #AI #AgenticAI #SME #LLM #KnowledgeDistillation #LoRA #QLoRA #OpenSourceAI #Automation #BusinessIntelligence
