I am currently building and training an AI model specifically for SME use through AINNA NeuralOps. The objective is not to create another general-purpose chatbot, but to develop a specialised intelligence layer that understands real SME operations and can work directly with business 系统.
I am not training a 大 语言 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 SME use cases such as management reporting, 财务, HR, sales, inventory, customer service, and website operations.
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 SME-specific responses before the useful knowledge is transferred into a smaller specialised model.
My current workflow is: Advanced LLM Teacher → SME Dataset → 蒸馏 → LoRA/QLoRA → Specialised SME Model → NeuralOps 智能体 安全带. The aim is to use powerful models for teaching, while using smaller models for day-to-day operational workloads.
The specialised model is then harnessed inside NeuralOps, together with 智能路由, specialised agents, databases, APIs, and 分离式系统. 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 系统 such as PHP, Python, SQL, or business rules. For routine intelligence, NeuralOps can use the specialised SME model. Only more complex reasoning tasks are escalated to larger models, helping reduce unnecessary inference cost and dependency on external AI providers.
实时 business data also remains inside operational 系统 such as MySQL, HR, 财务, 库存, CRM, and reporting platforms. The model does not need to memorise the entire company. It learns how to understand SME operations, while the actual 系统 provide current and verifiable information.
The architecture I am building is essentially: 商业 系统 → NeuralOps → 智能路由 → Specialised SME Model / 工具 / Advanced LLM → 验证 → 操作. My goal is to make AI more practical for 中小企业： smaller, specialised, cost-efficient, controllable, and integrated into real business 工作流 rather than existing only as a conversational assistant.
#AINNA #NeuralOps #AI #AgenticAI #SME #LLM #KnowledgeDistillation #LoRA #QLoRA #OpenSourceAI #自动化 #BusinessIntelligence
