We already have AINNA WebCLI built and running in production as part of our AI 智能体 infrastructure.
背后 it sits NeuralOps - combining 智能路由, 分离式系统, specialised parsers and multi-model orchestration to decide when AI is actually needed, which model should take the call, and when a task should bypass the LLM entirely.
For routine 工作流, this architecture can cut unnecessary token usage by up to 90%.
Our moat is the architecture, workflow intelligence and execution efficiency behind the agent.
#AINNA #NeuralOps #AgentAI #AI基础设施 #WebCLI #企业版AI
Official 跨-网络 Resource
Ruang pembaca
Apa pendapat anda?
Komen disemak sebelum diterbitkan. 电子邮件 diperlukan untuk moderasi dan tidak dipaparkan kepada pembaca.
关于multi-model的例子很实用，适合团队继续讨论。
这篇文章对which model should take的解释很清楚，实际操作的重点也很容易理解。
这篇文章把workflow intelligence and execution efficiency讲得比一般的AI介绍更具体。
总结部分让背后 it sits neuralops的重点更加清楚。
这篇文章适合团队用来开始讨论背后 it sits neuralops。