Because we believe the future of AI for 中小企业 in logistics is not just about access to powerful models. It is about making AI easier, cheaper, and more practical to use in real logistics operations.
今天, many AI 工具 are impressive, but the challenges remain: multiple subscriptions, rising token costs, 工作流 that do not fully fit our logistics operations, and heavy dependence on external providers.
That is why we are building our own AI agent as a development layer. The goal is simple: a logistics 经理, warehouse supervisor, fleet coordinator, or any domain expert should be able to describe a real operational problem and use AI to help build a website, application, automation, or operational 系统 that works for our logistics operations.
At the same time, we are working on model distillation to create a smaller, more focused model for practical 中小企业 logistics use cases. We are not trying to build the biggest model. We are trying to build one that is good enough for the task, cheaper to run, easier to deploy, and more controllable. For us, that means faster routing, better inventory prediction, and lower cloud costs.
Our direction is straightforward:
描述 the logistics problem → AI builds the 系统 → 中小企业 operates it.
If AI is going to create real value for 中小企业 in logistics, it has to become accessible, affordable, and operational — not just impressive in a demo. It has to work on the ground, in our warehouses, and on our delivery routes.
That is why we are building our own stack.
#AI #AgenticAI #AIAgent #LLM #ModelDistillation #中小企业 #自动化 #AINNA #NeuralOps #SovereignAI
