今天, we had the opportunity to present AINNA at MIGHT, Cyberjaya. This pitch wasn't just about showcasing another AI product – it was about sharing how we're building an AI architecture that truly serves the people we work with, centred on one simple principle use the right intelligence for the right task.
Our focus is NeuralOps: an orchestration layer that routes workloads between rules, specialised parsers, smaller models, LLMs and detached 系统, depending on what each task actually requires. The aim is simple – cut down on wasted resources like hallucination, token usage, compute and power, while improving reliability and scalability for the businesses we serve.
Beyond the technical details, we explored the bigger picture. Our proposal would see MIGHT acting as a strategic ecosystem bridge, AINNA providing the technology and execution layer, and Saudi Arabia serving as an infrastructure and scaling base for localisation, industry deployment, and wider GCC expansion. For Malaysian 中小企业, this opens up 新 possibilities to grow beyond borders.
We're not obsessed with building bigger models just to keep up with trends. What matters is how efficiently businesses can use intelligence, infrastructure and energy – especially when AI operates round-the-clock within real business processes. Our customers need solutions that are both powerful and practical.
We know the journey is far from over, but every discussion like this brings us closer to our goal. It helps us validate our direction, revisit our assumptions, and strengthen what needs to be fixed before we scale further. 从 马来西亚, we're building towards a future where AI serves businesses across industries and markets seamlessly.
#AINNA #MIGHT #NeuralOps #ArtificialIntelligence #AgenticAI #SovereignAI #MalaysiaAI #Cyberjaya #SaudiArabia #DigitalTransformation
Ruang pembaca
Apa pendapat anda?
Komen baharu dihantar untuk semakan terlebih dahulu. 名称 dan email diperlukan, tetapi email tidak dipaparkan kepada pembaca.
总结部分让especially when AI operates round-the-clock的重点更加清楚。
难得有人把revisit our assumptions, and strengthen讲得这么直白。
同意作者对specialised parsers, smaller models, LLMs的判断，但执行起来还有难度。 这点我还要再消化一下。
文章对token usage, compute and power的结论比较平衡，不只是强调好处。
今天, we had the opportunity这个说法我要拿回去跟同事讨论。
收藏了，主要是为了our customers need solutions。
这篇内容让我更容易理解为什么cut down on wasted resources值得关注。
这篇文章适合团队用来开始讨论industry deployment, and wider GCC。
关于what matters的风险和限制还可以再展开，不过基础说明已经很好。
关于AINNA providing the technology的实际落地部分最吸引我。 这点我还要再消化一下。
这篇文章对round-the-clock的解释很清楚，实际操作的重点也很容易理解。
我会把our proposal would see这一段分享给需要了解技术的同事。