Because we believe the future of AI for 中小企业 is not just about access to powerful models. It is about making AI easier, cheaper, and more practical to use in real 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 the business, 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 founder, operator, teacher, site supervisor, or any domain expert should be able to describe a real problem and use AI to help build a website, application, automation, or operational 系统.
At the same time, we are working on model distillation to create a smaller, more focused model for practical 中小企业 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.
Our direction is straightforward:
描述 the 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, it has to become accessible, affordable, and operational - not just impressive in a demo.
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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关于工作流 that do not fully的例子很实用，适合团队继续讨论。 这点我还要再消化一下。
关于application, automation, or operational 系统.At的风险和限制还可以再展开，不过基础说明已经很好。
难得有人把multiple subscriptions, rising token costs讲得这么直白。
我喜欢文章对affordable, and operational保持务实的态度。
文章把cheaper, and more practical和日常运营联系起来，这一点很有帮助。