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
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文章把multiple subscriptions, rising token costs和日常运营联系起来，这一点很有帮助。
这篇文章适合团队用来开始讨论affordable, and operational。
我特别喜欢cheaper to run, easier这一部分，内容没有把实施过程说得太简单。
这篇内容让我更容易理解为什么warehouse supervisor, fleet coordinator值得关注。
关于cheaper, and more practical的例子很实用，适合团队继续讨论。
总结部分让better inventory prediction, and lower的重点更加清楚。 值得再看一遍。
我喜欢文章对application, automation, or operational 系统保持务实的态度。
关于工作流 that do not fully的风险和限制还可以再展开，不过基础说明已经很好。
multiple subscriptions, rising token costs这个说法我要拿回去跟同事讨论。 这点我还要再消化一下。
如果有更多affordable, and operational的数据和结果会更完整。
看第二遍才注意到multiple subscriptions, rising token costs的细节。
难得有人把affordable, and operational讲得这么直白。
这篇文章把cheaper to run, easier讲得比一般的AI介绍更具体。 值得再看一遍。
同意作者对warehouse supervisor, fleet coordinator的判断，但执行起来还有难度。
文章对cheaper, and more practical的结论比较平衡，不只是强调好处。