What Is Domain Twin? ​

For the manufacturing industry, a company’s competitiveness stems from years of accumulated domain know-how. However, this expert knowledge is often difficult to standardize, transfer, and scale. Preserving and passing on this domain know-how has become essential to maintaining a competitive edge.

Domain Twin is an intelligent decision-making model designed specifically to transform expert knowledge into AI-driven insights.
It enables AI to learn experts’ knowledge and decision-making logic, providing support in daily operations. This not only improves decision-making efficiency but also significantly enhances accuracy—ensuring that professional expertise is effectively preserved, transferred, and applied.

Why do you need Domain Twin?

The manufacturing industry is heavily influenced by global policies and geopolitical developments—such as the recent U.S.-China trade war and the Russia-Ukraine conflict, both of which have had significant impacts on global supply chains.
As of 2025, newly inaugurated U.S. President Trump has announced new trade policies, including tariffs of 10% to 25% on imported goods from Mexico, Canada, and China. For manufacturers worldwide—especially those that have already set up or plan to establish factories in these countries—rising operational costs are now an unavoidable challenge. As a result, setting up factories in the U.S. has become a key strategic consideration.

By preserving veteran expertise within the Domain Twin platform, manufacturers can overcome traditional barriers when expanding to new locations—such as varying workforce skill levels, employee willingness to relocate, language barriers, or concerns about intellectual property becoming foundational knowledge for local competitors.
Domain Twin serves as a factory-specific operational manual for the enterprise, enabling local staff to operate equipment step-by-step based on the platform’s guidance—effectively bridging the gap with headquarters and ensuring seamless transfer of expert knowledge.

 

 

Why Do you need Domain Twin

How to Build My Domain Twin? ​

Profet AI adopts a resilience management methodology, working alongside a team of expert consultants to provide customized digital transformation guidance tailored to each enterprise. The implementation process unfolds progressively—from individual initiatives, to connected workflows, and eventually to organization-wide integration.

This approach ensures that AI technologies and Domain Twin can be seamlessly embedded into business decision-making and daily operations.

Build Your Domain Twin Now

Assisting Businesses in Achieving Judgment Standardization

Currently, enterprises are transitioning from “management standardization” based on past experiences to “process standardization” following systematic workflows. However, with the rise of AI and the advent of GenAI technologies, progressive companies should advance “judgment standardization” with data analysis at its core to enhance their competitiveness.

Management Standardization

  • Master-apprentice teaching
  • Paper-based work
  • Long training time

Process Standardization

  • Digitization
  • System implementation and online training
  • Knowledge can be retained and easily disseminated

Judgment Standardization

  • Intelligentization/ AI-ization
  • Machine learning tools + RPA
  • Decision-making based on data
  • More intelligent prediction

AILM helps enterprises achieve AI workflow standardization, effectively managing the complete lifecycle of each model from concept to actual application. Based on data analysis, AILM enables more intelligent operations and business learning and forecasting!

AutoML + AILM + RMS
Facilitates Seamless AI Integration within Enterprises

Combined with AutoML and AILM, integrates AI application development and deployment, from topic exploration to model building, completing the entire AI lifecycle within the enterprise. AutoML’s No-code format further reduces the usage threshold, integrating AI tools into daily workflows!

Last Step

Regional manufacturing, Rapid deployment

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