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Profet AI Supports GIS in Advancing Manufacturing AI from Deployment to Organizational Capability

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As competition intensifies across the high-end consumer electronics supply chain, manufacturers are facing greater pressure to improve quality, delivery speed, and operational resilience across increasingly distributed global supply networks. Profet AI, a manufacturing-focused AI software company, has continued to deepen its collaboration with General Interface Solution (GIS) Holding Ltd., supporting GIS in expanding AI applications from yield, quality, and efficiency improvement on the production floor to cross-process knowledge extraction, model development, and organizational capability building.

Over roughly two years of collaboration, the two companies have completed more than 100 AI projects and built over 1,500 AI models across manufacturing scenarios including process improvement, quality analysis, and anomaly prediction. Through Profet AI’s platform and implementation methodology, GIS has moved beyond using AI to solve isolated production line issues. AI is gradually becoming a working method that supports continuous improvement and decision-making across engineering teams.

From Customer Requirements to Supply Chain Competition, AI Becomes a Key Driver of Manufacturing Upgrade

GIS Chairman and Chief Strategy Officer Chou Hsien-ying said the company’s AI adoption was driven not only by the rapid development of AI technology, but also by customer expectations around quality, delivery, and response speed. As competition increases in the high-end consumer electronics supply chain, manufacturers must continue strengthening their operational capabilities to remain competitive in an environment shaped by supply chain diversification and rising resilience requirements.

“AI adoption is not simply about following a technology trend,” Chou said. “What matters is whether AI can truly enter company operations and help us continuously improve quality, delivery, and response speed, so we can better serve our customers and supply chain partners.”

He also noted that the rapid rise of regional supply chains has made supply chain security and resilience a higher priority for both countries and enterprises. For manufacturers, the key question is no longer only where to deploy production capacity, but how to use AI to improve quality, speed, and overall competitiveness across operations.

More Than 100 AI Projects Create New Room for Process Improvement

In terms of tangible results, GIS has used AI to re-examine process data that was previously considered close to its improvement limit, uncovering new opportunities for optimization.

On one optical display film production line, for example, the defect rate related to particle contamination had previously approached 10%. With Profet AI’s support in building models, analyzing key factors, and adjusting process parameters, the defect rate was reduced to nearly zero. In another glue coating process, bubble-related defects were previously around 0.3% to 0.4%. After AI-driven analysis and improvement, the rate was reduced to approximately 0.2%, cutting defects by about half.

The significance of these results goes beyond individual metric improvements. They also helped engineering teams recognize that many processes that appear stable or already within target can still reveal new room for optimization through AI and data analysis.

From SOPs to Cross-Process Correlation, Turning Senior Engineering Experience into Replicable Capability

Manufacturing improvement has traditionally relied heavily on the experience accumulated by senior engineers, which is then translated into standard operating procedures. However, most conventional SOPs remain limited to a single station or process. When problems involve cross-process correlations, manufacturers still depend heavily on the judgment of experienced engineers.

Through its collaboration with Profet AI, GIS has further transformed process data and know-how accumulated across different stations into models. This helps engineering teams identify correlations across processes and gradually shift process improvement from individual experience toward capabilities that can be continuously accumulated, replicated, and transferred through systems.

“In the past, many improvements depended on strong engineers and senior engineers because they were the ones who held cross-process know-how,” Chou said. “The major value of AI is that it helps us identify these cross-process correlations and codify them into models and operational language. This allows process improvement to gradually move from relying on people to relying on models and systems.”

Profet AI Provides Not Just Tools, but a Method Companies Can Internalize

Speaking about why GIS chose Profet AI, Chou said GIS valued Profet AI’s manufacturing experience and its proven implementation experience across relevant industries. More importantly, Profet AI does not simply help enterprises complete individual projects. It provides a platform and methodology that helps companies build their own AI application capabilities.

Chou described the collaboration as a capability-building process. The GIS team first learned from Profet AI how AI can be introduced into production and manufacturing workflows. The two teams then jointly identified improvement topics and worked together to move from learning to implementation, with GIS’s internal teams collaborating closely with Profet AI’s consultants.

“Profet AI is more like giving us the fishing rod, not just the fish,” Chou said. “Through this process, we learned how to develop our own improvement know-how and AI application culture within the company.”

Jerry Huang, Co-founder and CEO of Profet AI, said the value of manufacturing AI lies not only in building models, but in turning those models into capabilities that can continuously operate within the enterprise.

“True manufacturing AI should not stop at isolated projects or one-time improvements,” Huang said. “It must turn frontline know-how into enterprise AI assets that can be managed, replicated, and scaled. The GIS case shows how AI can move from single-process improvement to becoming a method for continuous organizational progress.”

Through this collaboration, Profet AI and GIS demonstrate a complete path for manufacturing AI, from project implementation and process improvement to organizational capability building. As global manufacturers continue to address supply chain restructuring, knowledge transfer, and operational efficiency challenges, turning frontline experience into a Domain Twin™ that can be preserved, scaled, and replicated is becoming an important direction for enterprises seeking to move AI from deployment to long-term competitive advantage.

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From SEMI E187 to Agentic AI Governance: Semiconductor AI Security Can No Longer Stop at the Equipment Layer

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From SEMI E187 to Agentic AI Governance: Semiconductor AI Security Can No Longer Stop at the Equipment Layer

Profet AI hosts Crossover Talks in Hsinchu with Taisys, Zentera Systems, and HPE to explore security governance architecture for Agentic AI

From left: Jonathan Yu, General Manager of Global Sales, Profet AI; Andy Hu, Senior Sales Manager of Compute & Digital Sales, HPE Taiwan; Jeff Tsai, Taiwan Country Manager, Zentera Systems; Jerry Huang, CEO, Profet AI; Jonathan Wu, Manager of Solution Consultant Dept., Taisys; Jun-Hsin Ho, Chairman, Taisys; and James Yang, Special Assistant to CEO, Profet AI.

semiconductor equipment cybersecurity is moving from standard-setting to procurement, validation, and supply-chain implementation.

SEMI E187 has given the industry a more consistent baseline for cybersecurity requirements in fab equipment. For semiconductor manufacturers and equipment suppliers, this means cybersecurity is no longer something to be added later by IT. It must be built into equipment across design, deployment, operation, and maintenance.

But this is only the starting point.

As AI agents begin entering the factory, reading process data, connecting to MES, EAP, FDC, and ERP systems, and participating in task execution, the security question is no longer limited to whether the equipment itself is secure.

The more critical questions become:

・Who issued the instruction?
・Was the action initiated by a person, or executed on their behalf by an AI agent?
・Does the agent have the right permissions?
・Which tools did it call, what data did it access, and which systems did it connect to?
・If abnormal behavior occurs, can the enterprise stop it in real time, trace the full sequence of actions, and audit it afterward?

This is the new governance challenge brought by Agentic AI.

SEMI E187 Addresses Equipment Security. AI Agents Extend Governance Upward.

The importance of SEMI E187 lies in its ability to establish a clearer cybersecurity baseline for semiconductor fab equipment. As equipment becomes increasingly connected within production environments, its built-in security posture directly affects line stability, operational continuity, and supply-chain resilience.

But Agentic AI changes the shape of risk.

In the past, equipment and enterprise systems were mostly operated by people. Accounts, permissions, network boundaries, and operation logs were largely designed around human users. In the Agentic AI era, AI is no longer limited to answering questions. It may act on behalf of users, access data, call tools, connect systems, and execute multi-step workflows across enterprise environments.

This means companies can no longer focus only on whether the equipment is secure. They must also ask whether the behavior around equipment and systems is secure.

In other words, SEMI E187 is an important starting point for equipment cybersecurity. But as AI agents move closer to equipment, data, and operational workflows, enterprises need to extend governance upward into identity, platform control, network security, and AI runtime infrastructure.

The Risk of Agentic AI Comes Not Only from the Model, but from Behavior


In the early stages of generative AI adoption, enterprise security discussions often focused on data leakage, hallucination, and whether employees were entering sensitive information into external tools.

Those risks still matter. But Agentic AI goes further.

The defining difference of an AI agent is that it can act. It can read data, interpret tasks, call tools, execute workflows, and decide the next step based on context. Once an AI agent is granted system access, the risk is no longer limited to inaccurate answers. It may come from incorrect actions, excessive permissions, unauthorized tool use, or behavior that takes place outside the enterprise’s visibility.

This is why Agentic AI governance cannot stop at model safety.

Enterprises need to manage:

・Whether the agent’s identity is trustworthy
・Whether its permissions match the task
・Which tools and data the agent is allowed to use
・Whether its behavior follows an approved path
・Whether every access, action, exception, and escalation leaves an auditable record

As AI moves from being a tool to becoming a digital worker, the center of governance shifts from protecting systems to governing behavior.

A Four-Layer Architecture: Governing AI Agents from Intent to Action


To prepare for the Agentic AI era, enterprises cannot rely on isolated tools or single-point security controls. They need a connected governance architecture that spans identity, platform, network, and runtime infrastructure.

Layer 1: Identity and Authorization


When AI agents begin executing tasks on behalf of users, identity verification can no longer stop at confirming whether an account has logged in successfully.

Enterprises need to distinguish whether an action was initiated directly by a person or executed by an AI agent. If it was executed by an agent, which user, department, or workflow does that agent represent? If the action involves a high-risk operation, should a human approval step be required before execution?

This is where Taisys plays an important role in the architecture. By supporting trusted identity and human-in-the-loop authorization, enterprises can retain a clear confirmation point before AI agents perform sensitive operations. This helps prevent agents from executing high-impact actions without verified user intent.

This will be especially important in future factory environments. As AI agents gradually support equipment monitoring, data retrieval, workflow recommendations, and task execution, people may not manually operate every system. But they still need to retain control over critical decisions and high-risk actions.

Layer 2: Platform Governance


For Profet AI, the key to Agentic AI is not only building agents. It is operating and governing them.

Enterprises will not have just one AI agent. They will have many agents across departments, workflows, sites, and operational tasks. These agents may connect to different data sources, call different tools, use different models, and accumulate different forms of domain know-how and skills.

Without unified platform governance, AI agents can easily become another form of Shadow AI. They may appear to improve efficiency on the surface, while the enterprise loses visibility into which agents are running, what data they are using, which tools they are calling, what records they leave behind, and how problems can be traced when something goes wrong.

This is where Domain Twin™ provides value.

Domain Twin™ helps manufacturers start from their own domain and bring together data, workflows, know-how, permissions, tools, and audit trails into a governable AI operating environment.

At the platform layer, enterprises need to govern not only conversations, but the entire operating context of the agent. This includes model configuration, tool use, knowledge-base access, ACLs, guardrails, tool permissions, audit logs, and the security of external skills or MCP tools.

AI agents should not only know how to act. They must also know what they are allowed to do, what they are not allowed to do, and why each action was taken.

This is one of the most critical, and often underestimated, layers for manufacturers moving AI from isolated applications into real operations.

Layer 3: Network Security and Zero Trust


When AI agents begin connecting to equipment, systems, databases, and application services, network-layer governance must also evolve.

In the past, enterprises often assumed that internal networks were relatively trusted. In the Agentic AI era, that assumption becomes more dangerous. Because AI agents can act with greater autonomy and speed, excessive permissions, incorrect instructions, malicious prompts, or external compromise can allow abnormal behavior to spread more quickly.

Zentera Systems’ Zero Trust architecture plays a key role at this layer. Its focus is not assumed trust, but continuous verification, least privilege, connection tracking, and anomaly containment.

If platform governance defines the approved path an agent should follow, network governance helps verify whether the agent is actually operating within that path.

・Is the agent connecting only to approved systems?
・Is it attempting to access data it should not touch?
・Is there lateral movement or unauthorized access?
・Can abnormal behavior be isolated or blocked when it occurs?

These capabilities will determine whether enterprises can safely allow AI agents to participate in mission-critical workflows.

Layer 4: AI Runtime Infrastructure


For Agentic AI to enter enterprise operations, companies cannot focus only on the application layer. They must also consider the compute, deployment, and operational infrastructure behind it.

HPE’s role in this architecture is not to directly address SEMI E187 equipment compliance. Rather, HPE provides enterprise-grade compute and infrastructure support so that AI agents can run in on-premises, edge, or data-center environments with greater security, stability, and manageability.

As AI agents move closer to the factory floor, enterprises must govern not only agent identity and permissions, but also the infrastructure that carries AI workloads. That infrastructure must be secure, reliable, maintainable, and suitable for long-term operations.

For semiconductor and high-tech manufacturers, AI is not finished once it is deployed. It must be operated over time, managed across sites, maintained reliably, and balanced across security, performance, and availability.

This is often one of the most overlooked foundations when companies try to move AI from PoC to ROI.

From Equipment Compliance to Operational Governance


The semiconductor industry is now facing the convergence of two forces.

On one side, equipment cybersecurity is becoming more standardized, procurement-driven, and validation-oriented. SEMI E187 reflects the industry’s expectation that equipment should have a clearer security baseline before entering the fab environment.

On the other side, Agentic AI is pushing AI beyond knowledge search and assisted analysis, moving it closer to process data, equipment, enterprise systems, and operational workflows.

When these two forces converge, the question is no longer only:

・Is this equipment secure?
・Is this model useful?
・Can this agent complete the task?

The more important question becomes:

Can the entire chain of AI-driven behavior be governed when an agent begins interacting with core enterprise assets?

This is the next challenge for semiconductor and high-tech manufacturers.

AI agents may support quality anomaly analysis, equipment status interpretation, process parameter recommendations, engineering troubleshooting, cross-site knowledge reuse, and even supply-chain or customer workflow coordination. These scenarios involve years of accumulated manufacturing know-how and highly sensitive operational data.

Without governance, the more powerful AI becomes, the greater the operational risk may be.

With the right governance foundation, AI can become a scalable operating capability that helps enterprises preserve knowledge, shorten decision cycles, and improve consistency across sites.

The Advantage of Agentic AI Depends on Whether Enterprises Control Their Own Domain


For manufacturers, the most valuable AI will not come from generic models alone. It will come from the enterprise’s own domain.

Data, workflows, equipment experience, exception-handling logic, engineering judgment, quality knowledge, and cross-site deployment experience are the real sources of manufacturing competitiveness.

This is why the key to Agentic AI is not handing enterprise know-how to external tools. It is converting that know-how into AI assets the enterprise can govern, reuse, and trace.

This is the core positioning of Domain Twin™.

Domain Twin™ helps manufacturers transform frontline know-how dispersed across people, equipment, workflows, and systems into governable and reusable AI assets. When AI agents operate based on the enterprise’s own domain, and are governed across identity, permissions, tools, network boundaries, and runtime infrastructure, AI can move from isolated tools into real operational capability.

For manufacturers, this is not just a technology upgrade. It is also a matter of knowledge retention, operational control, and long-term competitiveness.

SEMI E187 Is the Starting Point. Agentic AI Governance Is the Next Step.


SEMI E187 reminds the industry that equipment cybersecurity must be considered from the design stage.

Agentic AI adds another layer of urgency: when AI begins participating in operations and decisions, governance must also be designed before deployment.

These are not competing concepts. They are connected.

SEMI E187 establishes an important security baseline for equipment. In the Agentic AI era, enterprises need to extend upward into identity, platform governance, network security, and AI runtime infrastructure, so that AI agent behavior can be authorized, controlled, traced, and audited from intent to action.

In the future semiconductor factory, people, AI agents, equipment, and systems will work more closely together. People will still be responsible for critical judgment and authorization. AI agents will connect data, tools, and workflows. Equipment and systems will carry increasingly real-time and complex production tasks.

In this environment, enterprises do not need more AI tools that cannot be governed. They need a governance foundation that allows AI to enter operations safely.

AI agents are fast and powerful. But the more autonomous they become, the less enterprises can afford to focus only on functionality.

Agentic AI that can truly enter real operations must be authorized, controlled, traced, and audited.

This will be a critical step for semiconductor and high-tech manufacturers moving from AI PoC to measurable ROI.

Profet AI helps manufacturers build a governable, auditable, and secure foundation for Agentic AI through Domain Twin™, enabling AI to move beyond proof of concept and safely enter real operations.

If your organization is evaluating AI agents, preparing for SEMI E187-related cybersecurity requirements, or looking to move AI from isolated tools into governable operational capability, please fill out the form below to connect with the Profet AI team.

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Profet AI Launches “Domain Twin Co-Lab” and Partners with NTUT College of Management to Cultivate AI Talent

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Profet AI Launches “Domain Twin Co-Lab” and Partners with NTUT College of Management to Cultivate AI Talent

Profet AI today held a memorandum of understanding signing ceremony with the College of Management at National Taipei University of Technology (NTUT), marking the launch of its first university-focused donation initiative, the Profet AI Domain Twin Co-Lab. As part of the collaboration, Profet AI will donate its Domain Twin platform as a key resource for teaching and research, with the goal of helping students engage earlier with the next wave of AI applications and build practical capabilities in human-AI collaboration, workflow understanding, and application design. The partnership will also cover industrial AI applications, academic research, talent development, and broader industry-academia collaboration.

Left: Max Chen, Chief R&D Officer of Profet AI;
Right: Morris Fan, Dean of the College of Management at National Taipei University of Technology

This is also Profet AI’s first donation initiative specifically designed for universities. As AI continues to evolve from content generation to task execution, the capabilities expected of future talent are also changing. It is no longer enough to simply know how to use AI tools. Students increasingly need to understand how AI can truly enter workflows, connect with knowledge and systems, and generate action in real-world scenarios. For Profet AI, this collaboration is not just about bringing a platform into the classroom. It is about bringing the mindset and capabilities required for the next generation of AI applications into teaching and research at an earlier stage.

Profet AI’s Domain Twin platform brings together both AutoML and Agentic AI capabilities. Unlike many AI tools that remain focused on question answering, search, or isolated assistance, Domain Twin is designed to move AI from “answering questions” to “executing tasks.” It supports AI agents, workflows, automation, and tool integration, enabling AI to better align with real workplace needs. At the same time, the platform incorporates governance and security mechanisms to address enterprise requirements for control, accountability, and manageability when deploying Agentic AI.

This collaboration also builds on an existing foundation between the two sides. Professor Morris Fan, Dean of the College of Management at NTUT and a long-time advisor to Profet AI, has previously collaborated with the company through coursework that combined Profet AI’s AutoML capabilities with real industrial case studies. These efforts gave students exposure not only to AI tools themselves, but also to how AI can be applied to real business problems and industry settings. This latest collaboration extends that foundation further into Agentic AI applications, advancing the relationship from course-based exchange to a broader and more structured framework for academic collaboration and talent cultivation.

“AI is rapidly evolving from generative content creation to Agentic AI that can understand, reason, and take action.” Said Fan., “The value of this collaboration is not simply that students gain access to a new tool. More importantly, they gain an earlier understanding of how AI will truly enter workflows, knowledge environments, and decision-making contexts in the future. We hope this partnership will help students build stronger interdisciplinary integration and practical skills, while also creating more meaningful connections between academia and industry.”

“We have always believed that the most competitive talent of the future will not simply be those who know how to use AI, but those who know how to make AI work together with knowledge, tools, and systems to drive real action inside workflows. By donating Domain Twin, we hope students can understand earlier that the next generation of AI is not just a chatbot, but a working partner that can help execute tasks, move processes forward, and support decision-making. This is not only the starting point of Profet AI’s first university donation initiative, but also the beginning of a broader model we hope to extend to more campuses in the future as we work with academia to cultivate talent that is better aligned with the next stage of Agentic AI.” Mentioned Max Chen, Co-founder and Chief R&D Officer of Profet AI.

For Profet AI, the collaboration with NTUT’s College of Management marks an important step in advancing industry-academia engagement and talent development. It also serves as a starting point for future campus partnerships. Looking ahead, Profet AI aims to expand Domain Twin and its AI capability development model to more universities, deepen academic collaboration and industry connection, and help more young talent not only learn how to use AI, but also understand how AI can truly enter industrial and workplace settings.

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Profet AI and PSTC Academy Partner to Build Cross-Border AI Talent Pipeline and Advance AI-Driven Data Center Operations

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Profet AI and PSTC Academy Partner to Build Cross-Border AI Talent Pipeline and Advance AI-Driven Data Center Operations

[TAIPEI / BANGKOK, April 23, 2026] — Profet AI, a Taiwan-headquartered AI software company focused on manufacturing, today announced a strategic collaboration with PSTC Academy to jointly advance AI talent development and AI-powered data center innovation across Taiwan and Southeast Asia. The collaboration will bring together Profet AI’s Domain Twin capabilities with PSTC Academy’s training, certification, and digital infrastructure expertise to create more practical pathways from learning to deployment. PSTC Academy publicly positions itself around data center, cloud, and AI training and consulting, with a regional footprint across Thailand and several APAC markets.

Left, Poramet Ruangnoo, Group CEO & Co-Founder of PSTC Academy; Right, Mark Chen, Special Assistant to CEO of Profet AI

[Image: Left,  Poramet Ruangnoo, Group CEO & Co-Founder, PSTC Academy.; Right, Mark Chen, Special Assistant to CEO, Profet AI.

The collaboration will center on two key areas. The first is academic and certification development. Both parties plan to work toward a cross-border AI certification model that connects hands-on training with internationally recognized certification pathways, helping students and early-stage talent move more directly from software proficiency to job-ready validation. As part of this effort, Profet AI will provide practical training on AI tools and workflows, while PSTC Academy will contribute certification-oriented frameworks and standards to support the joint development of applied AI courses for manufacturing and enterprise use cases.

The second area is AI-enabled data center operations. By combining Profet AI’s domain twin with PSTC Academy’s data center domain expertise, the collaboration is expected to explore practical AIOps use cases such as predictive maintenance for UPS, cooling systems, and generators, as well as real-time anomaly detection across infrastructure, network traffic, and power consumption. The goal is to help data center operators improve uptime, operational visibility, and service stability.

The collaboration will also explore an AI-as-a-Service model for colocation and infrastructure customers. This may include AI-ready environments that allow customers to access pre-integrated AI tools within PSTC-managed infrastructure, as well as subscription-based access to AI capabilities through local service models. For industrial customers with stricter data governance requirements, the two sides also plan to support localized model training within secure data center environments, helping enterprises keep sensitive operational data within tightly controlled infrastructure.

“AI adoption now needs to go beyond experimentation. What enterprises increasingly need is a practical path that connects talent development, operational deployment, and scalable infrastructure,” said Jerry Huang, CEO of Profet AI. “Through this collaboration with PSTC Academy, we hope to help build a more execution-ready ecosystem, from university and professional training to real-world AI applications in data center and enterprise environments.”

“PSTC Academy has long focused on building digital infrastructure capability across the region,” said Poramet Ruangnoo, Group CEO & Co-Founder of PSTC Academy. “By working with Profet AI, we see an opportunity to connect certification, applied AI training, and infrastructure operations more closely, and to support both workforce development and next-generation data center services.”

As AI moves deeper into industrial and enterprise operations, the ability to pair practical talent development with trusted deployment environments is becoming increasingly important. Profet AI and PSTC Academy believe this collaboration can help accelerate that shift by linking applied learning, certification pathways, and infrastructure-ready AI adoption in a more structured and scalable way.

About Profet AI

Profet AI is an AI software company focused on manufacturing. Its Domain Twin platform, powered by AutoML, AI Studio, and AILM, helps enterprises preserve, scale, and replicate frontline know-how as an enterprise AI brain where knowledge never retires. Serving manufacturers across Asia, Profet AI enables smarter, more scalable operations from a single production line to multi-site, cross-border manufacturing.

About PSTC Academy

PSTC Academy is a Thailand-based training and consulting institution focused on data center, cloud, and AI infrastructure. Founded in 2022, the organization provides professional training, certification-related programs, and consulting services for digital infrastructure development across multiple APAC markets.

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Zentera Systems and Profet AI Partner to Deliver Zero Trust Security for Agentic AI in Manufacturing​

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Zentera Systems and Profet AI Partner to Deliver Zero Trust Security for Agentic AI in Manufacturing

Partnership pairs Profet AI's trusted Domain Twin™ platform with Zentera's Ensage™ AI to protect AI agents operating in sensitive production environments

Zentera Systems and Profet AI today announced a go-to-market partnership to secure agentic AI deployments in manufacturing environments. Under the partnership, Zentera’s Ensage AI platform will provide Zero Trust security in the compute and network layers for Profet AI’s Domain Twin™ platform, which is used by more than 300 manufacturers across the semiconductor, electronics, PCB, EMS, and advanced materials industries. The companies will demonstrate the integrated solution at RSA Conference 2026, North Hall, Booth #4618, March 23–26.

Profet AI has established itself as Asia-Pacific’s leading no-code AutoML software for manufacturing. Its Domain Twin™ platform enables manufacturers to digitize the tacit expertise of veteran engineers – process tuning, quality assessment, parameter optimization – into reusable AI models that can be replicated across production lines, factories, and global operations. Customers include major brands in the EMS, semiconductor OSAT, IC design, display panel, and materials sectors. As Profet AI expands its platform’s agentic AI capabilities through AI Studio, its enterprise-grade agent collaboration environment, securing the connections between AI agents, production data, and enterprise resources has become a critical priority.

“AI agents can deliver tremendous value in manufacturing, but security cannot be an afterthought,” said Jerry Huang, CEO of Profet AI,  “Our customers trust Domain Twin with their most valuable competitive asset — decades of accumulated production expertise. Partnering with Zentera gives us infrastructure-level Zero Trust protection that ensures this knowledge stays secure as our platform’s agentic capabilities scale.”

Closing the Security Gap for Manufacturing AI Agents

As manufacturers deploy AI agents across production systems, predictive maintenance platforms, and data analytics workflows, these agents create new machine-to-machine paths between AI runtimes, databases, MCP servers, and external LLMs. Traditional security tools lack visibility into these connections and cannot enforce granular access controls at the computing and infrastructure level.

Ensage AI extends Zentera’s Zero Trust Architecture to address security blind spots created by agentic AI — including unauthorized access, shadow AI, privilege escalation, IP spoofing, man-in-the-middle attacks, and uncontrolled data movement between AI systems and enterprise resources in the enterprise compute and network environments.

“Profet AI is solving one of manufacturing’s most critical challenges — preserving and scaling the irreplaceable expertise that drives competitive advantage,” said Dr. Jaushin Lee, CEO of Zentera Systems. “As that knowledge becomes embedded in AI agents that actively interact with production systems, securing those agent pathways at the network layer is essential. This partnership ensures manufacturers can scale agentic AI while meeting corporate governance, risk, and compliance requirements with confidence.”

Infrastructure-Level Zero Trust for Agentic AI

Ensage AI monitors and controls agentic behavior across three enforcement planes:

  • Inbound Controls – Authenticate and authorize who can access and operate AI runtimes
  • Outbound Controls – Govern which LLMs, tools, and domains agents can reach
  • Internal Controls – Restrict agent access to sensitive enterprise and operational resources

The platform provides complete visibility into agent-to-resource traffic, enforces policy-based access controls, prevents unauthorized data leaks, and creates audit trails from the network layer for compliance and governance. Unlike cloud-routed SASE models, Ensage AI operates on-premises or in hybrid environments – critical for manufacturing and industrial operations where latency, data sovereignty, and operational resilience are paramount.

Live Demonstration at RSA Conference

At RSA Conference 2026, the companies will demonstrate Ensage AI protecting Profet AI’s agentic platform in a live workflow scenario. The demonstration will showcase how Zero Trust Architecture secures MCP queries, agent-to-resource communications, and access to sensitive manufacturing data — all while maintaining the performance and flexibility AI applications require.

RSA Conference attendees are invited to visit Booth #N4618 to see the demonstration and discuss secure agentic AI deployment strategies with engineers from both companies.

About Zentera

Zentera Systems is the Zero Trust security company that protects what moves across enterprise networks – whether it is a user, a workload, or an AI agent. The company’s solutions deploy as an overlay on top of any infrastructure – IT, OT, cloud, or hybrid – to enforce Zero Trust security without re-architecting the network. With the launch of Ensage AI, Zentera now extends this same foundation to secure the autonomous AI agents operating inside enterprise environments. Zentera is headquartered in Silicon Valley and trusted by Global 2000 enterprises across semiconductor, financial services, healthcare, and public sector.

About Profet AI

Profet AI is a Taiwan-headquartered AI software company focused on manufacturing. Its Domain Twin™ platform—powered by AutoML (Machine Learning), AI Studio (Agentic AI), and AILM (AI governance and lifecycle management), helps enterprises preserve, scale, and replicate frontline know-how as an enterprise AI brain where knowledge never retires.

Serving over 300 manufacturers across Asia across industries such as electronics, semiconductors, PCB, IC design, display panels, and advanced materials, Profet AI enables smarter, more scalable, asset-light operations from a single production line to multi-plant, cross-border manufacturing.

Zentera, Ensage, and CoIP are trademarks of Zentera Systems, Inc., in the United States and other countries. All other trademarks cited here are the properties of their respective owners.

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Profet AI Launches ‘Domain Twin’ AI Platform to Secure Critical Manufacturing Technologies and Strengthen Global Supply Chains

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Profet AI, a Taiwan-based artificial intelligence company, today announced the launch of Domain Twin, an AI-powered solution designed to help manufacturers protect critical technologies, optimize supply chains and enhance global competitiveness. The launch comes amid a period of rapid transformation in the global semiconductor industry, underscored by TSMC’s recent announcement of a $100 billion investment in Arizona to build fabrication plants. As companies accelerate international expansion, they face growing challenges, including technology transfer, supply chain restructuring and rising operational costs. Profet AI’s Domain Twin aims to help manufacturers navigate these shifts while maintaining operational efficiency and safeguarding intellectual property.

“With increasing globalization and the restructuring of supply chains, companies must find ways to secure their competitive advantages while ensuring long-term sustainability,” said Jerry Huang, co-founder and CEO of Profet AI. “The ‘Domain Twin’ AI platform empowers manufacturers by digitizing domain expertise, facilitating knowledge transfer, and driving AI adoption across industries.”

Addressing Industry Challenges with AI-Powered Solutions

The manufacturing industry faces growing concerns over technology migration, supply chain disruptions, and rising operational costs. Profet AI’s ‘Domain Twin’ solution directly addresses these challenges:

  • Preventing Technology Leakage: Ensures that critical research, production, and quality control data remain within the company.
  • Optimizing Supply Chains: AI-driven analytics provide real-time market insights, enabling better inventory management and production planning.
  • Enhancing Global Competitiveness: AI-powered automation and no-code tools allow businesses to scale operations efficiently.

The platform is designed to empower 80% of a company’s core workforce by equipping them with AI capabilities, transforming them into next-generation AI-enabled professionals. Through AutoML (Automated Machine Learning) and AILM (AI Lifecycle Management), employees can build predictive models and accelerate AI deployment without extensive technical knowledge.

Rapid Deployment with No-Code AI Technology

Unlike traditional digital twin systems that require extensive customization, ‘Domain Twin’ utilizes a No-Code AI platform for fast implementation. This approach reduces deployment time and allows manufacturers and supply chain partners to adapt quickly to evolving market demands.

“By integrating AI across operations, companies can make more informed decisions, reduce dependency on specific markets, and create a more resilient global supply chain,” Huang added.

Profet AI Launches ‘Domain Twin’ AI Platform to Secure Critical Manufacturing Technologies and Strengthen Global Supply Chains Read More »