At AINNA, the finance system we use in our daily operations was built by a member of our finance team who had just graduated. She understood the work the system needed to support. With AINNA NeuralOps, she could describe those requirements in plain language and guide the AI agent through development. The system is now used in daily operations and was assessed at Technology Readiness Level 9 (TRL 9) by MOSTI.
We built AINNA NeuralOps in-house for SMEs, 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 system.
NeuralOps gives the AI agent a workflow for turning those requirements into practical steps, from planning and development through testing and deployment. Staff 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 workflows for setting up and managing servers and building web applications and internal systems.
For our finance team, this meant the person who understood the day-to-day work could help build the system she would use to do it.
Ruang pembaca
Apa pendapat anda?
Komen disemak sebelum diterbitkan. Email diperlukan untuk moderasi dan tidak dipaparkan kepada pembaca.
Simpan dulu, terutama karena where work slows down.
Still thinking about including businesses without a dedicated.
The section on neuralOps gives the AI agent is the strongest part for me.
Bookmarked, mostly for where work slows down.
The bit about staff can review the results is what I keep coming back to.
I read this twice. NeuralOps also supports workflows is the part that stuck.