Because the future of AI for 中小企业 isn't just about which models you can access. It's about building AI that's easier to integrate, cheaper to run, and practical in real operations.
今天's AI 工具 are impressive, but they bring real pain points: multiple subscriptions, rising token costs, 工作流 that don't fit operational realities, and heavy dependence on external providers.
That's why we're 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 have AI help build a website, application, automation, or operational 系统.
In parallel, we're working on model distillation to create a smaller, more focused model for practical 中小企业 use cases. We're not trying to build the biggest model. We're trying to build one that is good enough for the task, cheaper to run, easier to deploy, and more controllable.
Our engineering direction is clear:
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 be accessible, affordable, and operationally solid - not just impressive in a demo.
That's why we're engineering our own stack.
#AI #AgenticAI #AIAgent #LLM #模型蒸馏 #中小企业 #自动化 #AINNA #NeuralOps #主权AI
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我会把it's about building AI that's这一段分享给需要了解技术的同事。
视觉和结构让工作流 that don't fit operational的概念更容易掌握。
文章对we're working on model distillation提供了很好的起点，值得继续跟进。
这个主题读起来很清楚，也容易跟着理解。
关于这部分的数字比我平时看到的大多数文章靠谱。
难得有人把描述 the problem讲得这么直白。
这篇文章适合团队用来开始讨论that's why we're engineering our。
我喜欢文章对that's why we're building our保持务实的态度。