Our AI 智能体 has now been successfully modified and developed based on the increasingly mature AINNA NeuralOps architecture, shaped by real-world usage, model experimentation, billions of 令牌, and continuous optimisation.
This is not simply a chatbot or an agent directly connected to an LLM.
The architecture combines 智能路由, specialised processing, detached 系统, 验证层, and controlled LLM usage ensuring AI is used only when it is genuinely required.
Our principle is simple:
AI should know when to think, when to execute, and when not to use AI at all.
Insya-Allah, this 系统 will be launched after our upcoming pitching session before YTM Raja Muda 雪兰莪.
This will also be one of the key points we will present - how AINNA NeuralOps can serve as the foundation for AI infrastructure that is more efficient, controllable, scalable, and practical for real 中小企业 and organisational operations.
Step by step, NeuralOps is moving beyond concept.
It is becoming a working AI infrastructure.
#AINNA #NeuralOps #AIAgent #AgenticAI #AIInfrastructure #EnterpriseAI #自动化 #DigitalTransformation #MalaysiaAI
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文章对specialised processing, detached 系统, 验证层的结论比较平衡，不只是强调好处。 这点我还要再消化一下。
难得有人把shaped by real-world usage, model讲得这么直白。
我喜欢文章对how AINNA neuralops can serve保持务实的态度。
关于neuralOps is moving beyond concept.It的例子很实用，适合团队继续讨论。
我会把real-world这一段分享给需要了解技术的同事。
我特别喜欢billions of 令牌, and continuous这一部分，内容没有把实施过程说得太简单。