AI Native

FAQ

What is the difference between AI-native and traditional AI applications?

Traditional AI applications typically overlay AI functionality onto existing systems (i.e., the "AI+" model), where AI exists as an independent module, leading to prominent data silos and difficulties in model iteration. In contrast, AI-native approaches treat AI as a core component of the infrastructure from the very beginning of system design, deeply coupling data flows, business logic, and inference engines to support continuous learning and adaptive optimization. Simply put, traditional AI is "adding AI to a system," while AI-native is "building a system with AI."

What key technologies are needed to support AI-native architecture?

AI-native architecture typically requires the following key technologies: 1) MLOps/LLMOps: for full lifecycle management of models; 2) Data Lakehouse: supporting unified storage and real-time processing of multimodal data; 3) Elastic inference infrastructure: such as GPU clusters and edge inference nodes; 4) Low-code/no-code AI development platforms: lowering the barrier to AI application; 5) Explainable AI (XAI): ensuring transparency and trustworthiness of model decisions. Mangxu Software's Mingtai Digital Infrastructure Ecosystem integrates these technologies, providing out-of-the-box AI-native capabilities for industries.

What are the typical application scenarios of AI-native in the digital infrastructure field?

In the field of digital infrastructure, typical AI-native applications include: 1) Intelligent resource scheduling: automatically allocating computing, storage, and network resources based on real-time load; 2) Predictive maintenance: using AI models to predict equipment failures and intervene in advance; 3) Security agents: automatically identifying and responding to network threats; 4) Digital twin optimization: leveraging AI to optimize the mapping relationship between virtual models and physical entities in real time; 5) Natural language interaction: users managing infrastructure through conversational interfaces. Mangxu Software's Mingtai Digital Infrastructure Ecosystem has mature practices in these scenarios.

How can enterprises migrate to an AI-native architecture?

Enterprises migrating to an AI-native architecture are advised to follow a three-step approach: First, assess the data maturity and AI readiness of existing systems, identifying high-value scenarios; Second, select 1-2 core business modules for AI-native transformation pilots, establishing data loops and model iteration processes; Third, gradually expand AI-native capabilities across the entire system, while cultivating internal AI talent and establishing governance mechanisms. Mangxu Software can provide full-process support from consulting to implementation, helping enterprises achieve a smooth transition.

Is AI-native only suitable for large enterprises?

Not necessarily. Although large enterprises have more data and computing resources, the AI-native concept is equally applicable to small and medium-sized enterprises. By adopting mature AI-native platforms (such as Mangxu Software's Mingtai Digital Infrastructure Ecosystem), SMEs can obtain out-of-the-box AI capabilities at a lower cost without needing to build underlying infrastructure themselves. The key lies in selecting a solution that matches business scale and focusing on the rapid implementation of high-value scenarios.