At AINNA, the 财务 系统 we use in our daily operations was built by a member of our 财务 team who had just graduated. She understood the work the 系统 needed to support. With AINNA NeuralOps, she could describe those requirements in plain language and guide the AI agent through development. The 系统 is now used in daily operations and was assessed at 科技 Readiness Level 9 (TRL 9) by MOSTI.
We built AINNA NeuralOps in-house for 中小企业, including businesses without a dedicated IT team. The people who handle a process every day often know it best. They know which data they need, where work slows down, and what a report should show. That knowledge gives them a starting point for building a 系统.
NeuralOps gives the AI agent a workflow for turning those requirements into practical steps, from planning and development through testing and deployment. 员工 can review the results at each stage against how they actually work. They can get started without knowing how to code.
The AI agent can run directly on a company’s own server. NeuralOps also supports 工作流 for setting up and managing servers and building web applications and internal 系统.
For our 财务 team, this meant the person who understood the day-to-day work could help build the 系统 she would use to do it.
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看第二遍才注意到including businesses without a dedicated的细节。
这篇文章适合团队用来开始讨论neuralOps also supports 工作流。
文章把员工 can review the results和日常运营联系起来，这一点很有帮助。
我特别喜欢where work slows down这一部分，内容没有把实施过程说得太简单。
我喜欢文章对neuralOps gives the AI agent保持务实的态度。
这篇文章把neuralOps also supports 工作流讲得比一般的AI介绍更具体。