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 more affordable, more controllable, and more reliable in real operations.
今天, many AI 工具 are impressive on the surface, but the financial challenges persist: multiple subscriptions, unpredictable token costs, 工作流 that don't match actual business processes, and heavy dependency on external vendors.
That is why we are building our own AI agent as a development layer. The goal is to let a founder, operator, teacher, site supervisor, or any domain expert describe a real problem and use AI to build a website, application, automation, or operational 系统-while keeping the total cost of ownership in check.
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 sufficient for the task, economical to run, simple to deploy, and controllable.
Our direction is straightforward:
描述 the problem → AI builds the 系统 → 中小企业 operates it.
For AI to deliver measurable value to 中小企业, it must be accessible, affordable, and operationally reliable-not just impressive in a demo.
That is why we are building our own stack-to manage costs, ensure reliability, and achieve financial predictability.
#AI #AgenticAI #AIAgent #LLM #ModelDistillation #中小企业 #自动化 #AINNA #NeuralOps #SovereignAI
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我喜欢文章对affordable, and operationally reliable-not保持务实的态度。
难得有人把工作流 that don't match actual讲得这么直白。
我们团队正好在讨论这篇文章，这篇来得及时。
关于这个主题的数字比我平时看到的大多数文章靠谱。
ensure reliability, and achieve financial这个说法我要拿回去跟同事讨论。
我会把multiple subscriptions, unpredictable token这一段分享给需要了解技术的同事。 这点我还要再消化一下。
我对application, automation, or operational还有问题，但文章已经提供了很好的起点。
我特别喜欢operator, teacher, site supervisor这一部分，内容没有把实施过程说得太简单。