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Minth Group’s Acquisition of Nissan’s Yokohama Global Headquarters

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Minth Group’s Acquisition of Nissan’s Yokohama Global Headquarters

A Strategic Move in Completing Its Japan Puzzle — Profet AI Looks Ahead with Domain Twin™

Minth Group has recently completed the acquisition of Nissan Motor’s global headquarters building in Yokohama for JPY 97 billion, adopting a sale-and-leaseback structure that enables Nissan to secure liquidity while maintaining operational flexibility.

This transaction is far more than a real estate investment. It is widely viewed as a strategic move through which Minth is placing a critical piece on its global manufacturing map, signaling its long-term commitment to the Japanese market.

Aligned with Minth’s existing global manufacturing footprint and regional growth objectives, Japan is steadily emerging as the company’s next strategic anchor.

Japan Is Not Just a New Site — It Is an Accelerator for Global Capability Replication

As a deeply embedded player in the global automotive supply chain, Minth Group operates 77 factories worldwide and serves more than 70 international automotive brands. Japan and South Korea have been clearly positioned within Minth’s long-term growth roadmap.

Against this backdrop, the Yokohama site represents more than a single asset. It has the potential to become a critical node connecting Japan’s industrial ecosystem, engineering talent, and world-class manufacturing standards with Minth’s global operations.

The key challenge Minth — like many global manufacturers — now faces is not whether success can be achieved locally, but how proven success can be prevented from being diluted across countries, plants, and cultures.

From AutoML Deployment to AILM Accumulation: Proven Results in Minth’s Production Environments

In its ongoing collaboration with Profet AI, Minth has taken the lead in deploying AutoML directly within real production environments, empowering frontline engineers to solve problems using data and AI.

In one automotive trim bending process, yield fluctuations were significant, with defect rates reaching 40–47%. By leveraging the Profet AI platform, engineers independently built models to identify key influencing factors. The first phase alone generated RMB 5.9 million (≈ USD 800k) in tangible benefits, while cultivating internal AI champions who went on to initiate additional projects.

More importantly, these achievements did not remain isolated pilot successes. They have evolved toward AILM (AI Lifecycle Management):

Models, process insights, and improvement know-how are systematically preserved and accumulated into a sustainable AI knowledge base.

Through structured training programs and proposal mechanisms, Minth collected 64 AI proposals in 2024, successfully implementing 10+ projects, with validated solutions already planned for rollout across 70+ global factories.

The True Value of Domain Twin™: Making Success Replicable, Traceable, and Scalable

As Minth expands further into overseas operations and the Japanese market, the core challenge is no longer simply:

“Can we do AI?”

But rather:

“How can teams across different factories, cultures, and experience levels quickly inherit and execute proven best practices?”

This is precisely where Domain Twin™ delivers its core value.

Domain Twin™ is not merely a model management tool. It is an architecture that integrates domain expertise, process understanding, AI models, and improvement logic into replicable, traceable knowledge assets.

Through Domain Twin™, validated AutoML and AILM experiences are distilled into structured knowledge units, enabling new factories — including future expansions in Japan — to operate directly on top of Minth’s globally proven best practices, rather than starting from scratch.

According to Minth’s roadmap, by 2026 internal AI champions will take the lead, allowing Domain Twin™ knowledge to scale globally at minimal marginal cost.

Shaping the Future with Minth: Making AI a Common Language in Global Manufacturing

From the strategic positioning of the Yokohama headquarters to the systematic accumulation of AI knowledge across global plants, Minth Group is entering a pivotal phase — not merely adopting AI, but transforming it into a shared language across countries, factories, and generations of engineers.

Profet AI looks forward to continuing this journey with Minth, using Domain Twin™ as the foundation to convert individual successes into long-term competitive advantage, supporting Minth’s next stage of growth in Japan and across global markets.

Interested in How Domain Twin™ Enables Scalable Global Manufacturing?

If you are exploring how AI can evolve from isolated projects into replicable, enterprise-wide manufacturing capabilities, we invite you to connect with Profet AI and discover how Domain Twin™ is being applied across industries and production environments.

Contact Profet AI today to begin building your Manufacturing Domain Twin™ blueprint.

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When AI Stops Answering Questions and Starts Taking Action

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When AI Stops Answering Questions and Starts Taking Action
Why Enterprises Are Moving Toward Agentic AI

From Personal Productivity to Enterprise Operations, Where the Gap Emerges

Over the past two years, Generative AI has rapidly reshaped how people work. From document drafting and data organization to content creation, general-purpose models such as ChatGPT have delivered significant productivity gains at the individual level.

However, enterprise adoption tells a very different story. According to The ROI of AI 2025 report published by Google Cloud, while more than 90% of enterprises have launched AI initiatives, the vast majority remain stuck at the proof-of-concept (PoC) stage. Only a small group of leading organizations have successfully scaled AI into core operational processes and achieved sustained, measurable business impact.

These findings point to a fundamental issue. The role enterprises require from operational-grade AI is fundamentally different from the role played by today’s conversational AI.

The Core Problem, Crossing the Gap from “Responder” to “Actor”

If model capabilities continue to improve, why do enterprise AI projects still struggle to move beyond PoC?

A closer look at how AI is typically used in PoC deployments reveals a common pattern. AI is primarily tasked with answering questions, generating content, or offering recommendations. This mirrors the design logic of ChatGPT, where AI functions as a passive responder.

Yet in real operational environments, enterprise needs extend far beyond information delivery. Enterprises require AI to support:

  • Executable decisions, insights must translate into concrete actions

  • Process continuity, actions must connect across multiple internal systems

  • Accountability and traceability, outcomes must be reviewable, correctable, and auditable

When a system optimized for conversational quality is expected to handle permissions, workflows, and responsibility, friction is inevitable. This helps explain why many PoCs appear promising in isolation but fail to transition into production environments.

Agentic AI, Redefining AI’s Role Inside the Organization

Against this backdrop, Agentic AI has emerged as a critical path forward.

Unlike general-purpose generative models focused on producing better answers, Agentic AI is designed to plan and execute tasks proactively, within predefined rules and under human supervision. The objective is not better responses, but reliable and repeatable action.

This shift brings three fundamental changes to AI’s role in enterprises.

1. From Data Access to Authorized Action

In traditional architectures, enterprise AI discussions often center on whether a model can access data. In practice, what enterprises truly care about is whether data can be used securely, compliantly, and within governance constraints.

Core enterprise knowledge is typically embedded in systems such as ERP, CRM, internal SOPs, and historical transaction records. These data sources are highly contextual and often sensitive. Once AI begins participating in real operations, enterprises must ensure two things.

First, the AI must understand sufficient business context to support meaningful decisions.
 Second, data access and usage must remain controlled, auditable, and compliant with governance policies.

Agentic AI changes the equation by introducing AI as an authorized system actor. Under platform-level governance and permission controls, AI can not only retrieve enterprise knowledge but also, within approved boundaries, interact with systems through APIs and workflows.

This approach establishes clear behavioral boundaries for AI. Enterprises can gradually expand AI’s operational role while maintaining data sovereignty and compliance, laying a solid foundation for trust in AI-driven task execution.

2. From Recommendations to Completed Actions

The primary value of general-purpose models lies in analysis and recommendation. For enterprise leaders, however, insights alone are insufficient unless they reliably translate into downstream actions.

In practice, enterprises expect outcomes such as:

  • Inventory analysis that automatically generates replenishment requests and triggers procurement workflows

  • Equipment status assessments that create maintenance tickets and notify relevant teams

  • Workflow conditions that automatically update system states or initiate approval processes

Agentic AI is designed to close this execution gap. Through workflow orchestration, tool invocation, and system integration, AI can turn decisions into concrete actions, such as creating tickets, updating records, triggering approvals, or sending notifications, all within authorized boundaries. Human checkpoints can still be retained at critical stages to balance efficiency and risk.

Once AI can actively move processes forward, it becomes a functional node within operational workflows rather than a passive advisory tool.

3. From Black-Box Outputs to Governable Decisions

As AI becomes embedded in higher-impact tasks, enterprise expectations around trust and reliability rise accordingly.

Because general-purpose models rely on probabilistic generation, they may produce responses that appear plausible but lack sufficient grounding. In high-stakes business decisions, this risk becomes unacceptable.

Agentic AI addresses this challenge by embedding decision-making within an explicit governance framework. In enterprise-grade architectures, every AI decision and action must meet clear criteria:

  • Grounded reasoning, decisions are based solely on approved enterprise data sources

  • Traceability, actions can be traced back to documents, system records, or defined rules

  • Monitoring and auditability, decision processes and outcomes can be reviewed and audited

  • Right to refuse, the system can decline to act when data is insufficient or confidence is low

In this model, trust is built not on eloquence, but on consistency, predictability, and auditability. These qualities are essential for AI to participate in long-term operations rather than remain a short-lived experiment.

Scaling Deployment, The Real ROI Inflection Point

Google Cloud’s research further confirms that AI ROI is strongly correlated with deployment depth.

Among early adopters of Agentic AI, more than 80% report clear and measurable business returns. What distinguishes these leaders is a shared mindset shift. They move beyond isolated experiments and treat AI as scalable digital labor.

Only when AI can independently complete tasks within a governance framework can organizations progress from productivity assistance to true operational automation, unlocking exponential value creation.

Conclusion, Enterprise AI Advantage Comes from Deep Integration

The evolution from GPT-style models to Agentic AI reflects a pragmatic shift in enterprise expectations. When organizations demand not just correct answers, but the ability to safely get work done, deep integration into existing processes becomes the decisive factor.

Within this context, Profet AI’s AI Studio (AIS) was purpose-built to meet enterprise Agentic AI requirements. Through no-code workflow orchestration and rigorous permission governance, AIS provides a secure and controllable foundation for deploying Agentic AI in production environments.

By bridging the gap between conversational AI and actionable AI, Profet AI enables enterprises to transform daily operations into continuously compounding operational intelligence, turning AI from a tool into a true organizational capability.

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From Generative AI to Agentic AI: Why MCP Is the Missing Link

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From Generative AI to Agentic AI: Why MCP Is the Missing Link

The Key Foundation for Moving from Generative AI to Agentic AI

If the first wave of AI was about teaching machines to understand and express human language, then we are now standing at the beginning of the second wave—one defined by action. This is the era of Agentic AI.

Agentic AI is no longer a passive system that merely answers questions. It acts as a digital worker with decision-making capabilities. To function effectively, it must operate across multiple systems—querying internal enterprise data, updating records, triggering workflows, or notifying stakeholders through collaboration tools.

Until recently, enabling AI to safely and reliably perform such cross-system actions came at a very high cost.

So how did AI evolve from generative models into truly agentic systems?

The key lies in today’s main topic: MCP (Model Context Protocol).

Before MCP: The “Integration Hell” Problem

Before diving into how MCP works, we need to clarify a fundamental question:

Why do AI capabilities keep improving, yet remain difficult to deploy at scale inside enterprises?

The bottleneck is rarely the model itself—it’s the fragmented data and system landscape.

Enterprise data and tools are typically scattered across different systems. Documents may live in SharePoint, manufacturing data in MES, customer information in Salesforce—each with its own interface and access rules, and no consistent way to connect them.

When enterprises want a model to access multiple systems, engineering teams often resort to the most direct approach: writing custom integration code for every model–system combination. This is commonly known as “glue code.”

In this architecture, developers must repeatedly write and maintain bespoke integrations for every pairing of model and tool. Without a standardized connection protocol, even a minor API change in one system can break dozens of downstream integrations, dramatically reducing overall system stability.

Over time, this point-to-point integration approach leads to what engineers call “integration hell.”

This results in two major consequences:

  • Vendor lock-in: Once an enterprise has invested heavily in integrating a specific model, switching to another model often requires rewriting and retesting the entire integration layer.
  • Reinforced data silos: Since each new data source adds incremental integration cost, enterprises tend to connect only the most critical systems, leaving many valuable but “non-core” data sources outside AI’s reach.

This is why many AI initiatives—despite having sound concepts—never move beyond pilot or demo stages. The cost and risk of integration are simply too high.

Technology and Advantages: The Three Core Components of MCP

In November 2024, U.S. startup Anthropic introduced MCP, bringing order to this chaos.

MCP is not designed to be an all-in-one super platform, nor does it force AI to learn yet another proprietary language. Instead, it defines a standardized communication protocol between AI models and external systems.

The MCP architecture consists of three components:

For development teams, this fundamentally changes the integration model. Instead of writing custom connectors for every AI tool or platform, teams only need to implement an MCP Server once. That server can then be reused across different AI environments—desktop AI tools, developer IDEs, or internal enterprise platforms.

When connection logic becomes reusable, integration costs stop compounding. AI application development and maintenance return to a more controllable and sustainable state. And only when integration costs are under control can Agentic AI realistically enter everyday enterprise workflows.

Beyond Integration: Security, Permissions, and Boundaries

However, even after escaping integration hell, another critical challenge remains: security and access control.

When AI becomes embedded in enterprise processes, the real question is not how much it can do—but what it is allowed to do, and whether those permissions introduce risks such as data leakage or system compromise.

In MCP’s design, AI is not granted unrestricted system access. Instead, it operates within clearly defined interaction boundaries.

In some scenarios, AI may only need read-only access to understand system states or analyze conditions. But once actions involve updating data, sending notifications, or triggering operational workflows, risk increases significantly. These actions must therefore be explicitly governed and allowed only under defined conditions.

Moreover, when users switch projects or responsibilities change, the scope of data visible to AI is updated accordingly—preventing it from retaining unnecessary long-term access.

This emphasis on clear boundaries is not theoretical. The cybersecurity incident known as Ni8mare in early 2026 served as a stark reminder: when automation or AI platforms hold both system access and cross-process control, a breach can impact not just a single tool, but entire operational workflows. At that point, risk stems from the process itself, not individual features.

For enterprises—especially in manufacturing—security also means data sovereignty. MCP does not require raw data to be sent to the cloud. Instead, it supports local data processing and filtering, passing only necessary results to models for reasoning. Data remains under enterprise control, while AI plays a supportive analytical role.

This design allows AI to gain agency while preserving what enterprises care about most: control. AI is no longer just answering questions—but every action it takes remains understandable, manageable, and auditable.

This is precisely why MCP enables Agentic AI to move from concept to practice.

MCP × AI Studio: Bringing Agentic AI into the Enterprise

MCP ensures AI can safely and controllably connect to data and systems. But in real deployments, enterprises quickly encounter the next challenge:

Once AI can read data and invoke tools, how does it actually participate in decision-making?

The key is not just connectivity, but who can see what, who can do what, and under what conditions.

Not every AI agent should have the same visibility or authority in every scenario. Access must be dynamically constrained based on job roles, contexts, and enterprise policies. Some situations allow read-only analysis; others permit action—but only within clearly defined rules.

This is where Profet AI’s AI Studio, an agentic AI collaboration platform, comes into play.

AI Studio enables multiple AI agents—each with different roles and expertise—to collaborate within a single workflow. They cross-validate insights, transform model outputs into actionable enterprise decisions, and ensure that every agent operates strictly within its permitted scope.

A Practical Example: HR Decision Support

HR is one of the most common application scenarios.

In recruitment and retention, the challenge is rarely a lack of data. Instead, the difficulty lies in converting fragmented information into predictive, actionable insights.

Within AI Studio, HR teams move beyond static reports and begin collaborating with AI agents in real decision-making processes. For example, in hiring or retention scenarios, AI can securely analyze historical data and predict attrition risks—allowing HR to intervene before critical decisions are made or problems escalate.

Because HR data is highly sensitive, not every role or situation has full visibility. Through MCP’s permission controls and AI Studio’s collaboration framework, AI agents only access what they are explicitly allowed to see and act upon.

Data ownership remains with the enterprise. AI becomes a decision-support capability—not an additional source of risk.

From Operations to Strategy

From manufacturing floors to core HR decisions, MCP opens the door for Agentic AI to enter enterprise systems, while AI Studio provides the environment for these agents to collaborate, reason, and form judgments together.

When AI evolves from a data-retrieval tool into a system that can predict risk, support decisions, and recommend actions, Agentic AI finally becomes embedded in the core of the enterprise value chain.

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Profet AI Honored in Business Weekly’s “AI Innovation Top 100,” Leveraging Domain Twin™ to Accelerate Business Conversion

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Profet AI Honored in Business Weekly’s “AI Innovation Top 100,” Leveraging Domain Twin™ to Accelerate Business Conversion

The award was accepted by Global Marketing Director Amilia Chen (right) and presented by Betty Hu (left), Director-General of the Secretariat, Ministry of Digital Affairs.

Profet AI has actively advanced the deployment of enterprise-grade AI platforms and real-world applications in recent years, continuously deepening its digital transformation and innovation initiatives. This commitment has been recognized with an award in the inaugural “AI Innovation Top 100” by Business Weekly, highlighting Profet AI’s comprehensive achievements and practical capabilities in AI adoption, application implementation, and organizational enablement.

The Business Weekly “AI Innovation Top 100” evaluation focuses on whether AI solutions genuinely address real pain points, create measurable value, and build sustainable organizational capabilities with demonstrable impact. Profet AI’s selection reflects the judges’ recognition of its success in transforming AI from isolated tools into scalable, process-driven organizational capabilities.

Domain Twin™–Powered AI Assistant for Opportunity Decision-Making and Deal Assignment

Among more than 400 enterprise submissions, Profet AI was selected for its internally developed business opportunity development and decision-making workflow powered by Domain Twin™. Through its “AI Opportunity Development Assistant,” global teams can rapidly generate tailored business development content, while AI models provide opportunity potential scores. These insights enable managers to prioritize deal assignments and resource allocation based on AI recommendations, significantly improving resource efficiency and overall deal conversion speed.

This workflow also progressively quantifies and standardizes decision factors that previously relied on the tacit knowledge of senior staff—such as industry momentum and stakeholder maturity. As a result, overseas offices and new team members can evaluate opportunity quality using a consistent decision logic, reducing onboarding time and minimizing decision discrepancies.

In addition, Profet AI has continuously organized internal AI hackathons in recent years, encouraging employees to address real operational pain points and rapidly validate solutions. This approach moves AI applications beyond individual productivity tools toward cross-functional, reusable process transformation, steadily building organizational innovation momentum and data-driven thinking.

Looking ahead, Profet AI will continue to deepen its AI applications. Through Domain Twin™, the company aims to help more enterprises transform critical know-how into operational, scalable AI assets—creating long-term operational and growth value.

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Profet AI Selected as CIO Taiwan 2026 Elite Vendor

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Profet AI Selected as CIO Taiwan 2026 Elite Vendor

Domain Twin Recognized for Building Enterprise-Grade AI Platform Capabilities

CIO Taiwan has officially announced the results of its 2026 Elite Vendor survey. Conducted in late 2025, the survey collected more than 600 valid responses from CIOs, IT leaders, and digital transformation decision-makers across industries in Taiwan. The findings indicate that AI platforms and governance capabilities have become a critical criteria for enterprise vendor evaluation. Leveraging its proven AI platform and Domain Twin implementation results, Profet AI has been selected as a 2026 Elite Vendor.

As digital technologies and industrial environments continue to evolve rapidly, the key challenge for manufacturers is no longer whether AI is feasible, but whether it can operate sustainably, scale continuously, and integrate deeply into core enterprise processes. Drawing on years of market observation, CIO Taiwan conducts the Elite Vendor survey to capture insights from IT and digital transformation leaders across industries. The 2026 results reveal that enterprises now place strong emphasis on AI platform architecture and governance, particularly in manufacturing, semiconductor, and high-tech sectors. A widely shared and urgent challenge is how to transform isolated project successes into organizational capabilities that can be replicated across sites.

Domain Twin, proposed by Profet AI, is designed to address this exact challenge. Rather than merely digitizing models or data, Domain Twin systematically encapsulates decision logic, process knowledge, and operational experience accumulated by frontline experts into manageable and reusable AI domain models, all governed and deployed within an enterprise-grade AI platform. Through Domain Twin, enterprises can rapidly replicate successful AI applications from a single production line or factory to other sites—or even across global operations—significantly accelerating AI scale-out.

CIO Taiwan notes that a clear trend emerges from the 2026 survey results: when selecting AI vendors, enterprises are no longer focused solely on model accuracy or short-term performance. Instead, they increasingly value whether AI capabilities can be inherited, reused, and governed over the long term. Vendors that can combine robust AI platforms with a Domain Twin methodology—elevating AI from isolated “projects” to a sustainable enterprise capability—are becoming the new market standard.

Profet AI’s selection as a 2026 Elite Vendor reflects strong recognition from Taiwanese enterprises of its ability to drive real-world AI adoption through Domain Twin. By systematizing frontline decision-making expertise and process knowledge into manageable AI domain models, Domain Twin enables enterprises to accelerate AI deployment, expansion, and replication—transforming AI into a core capability that delivers long-term value.

Source:
 CIO Taiwan Announces the 2026 Elite Vendor List of Taiwan’s Most Trusted Technology Partners https://www.cio.com.tw/105827/

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The Age of Physical AI Has Arrived: Five Industrial AI Trends Defining 2026

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The Age of Physical AI Has Arrived: Five Industrial AI Trends Defining 2026

“This is my first big bet of the year.” — Jensen Huang, CEO of NVIDIA

CES 2026 opened with a bold declaration from Jensen Huang:

“This is no longer just about perception. We are entering the ChatGPT moment for robotics and industrial AI.”

Walking the exhibition floor, one shift was unmistakable.
AI discussions are no longer confined to generative models and chat interfaces. Instead, AI is stepping out of the screen and into factories, warehouses, and physical equipment.

AI can now see and hear—but more importantly, it is beginning to understand the physical world and respond in real time. Whether through Physical AI, which interacts directly with real environments, or Agentic AI, which can autonomously act toward goals, CES 2026 marked a turning point: industrial AI is moving from a supporting role to the core of action and decision-making.

Against this backdrop, the key question for industry is no longer just system upgrades—but how human experience and judgment can truly be inherited by AI. This is precisely where concepts like Profet AI’s Domain Twin™ align closely with the trends emerging in 2026.

Industry 5.0: Five Industrial AI Trends Observed at CES 2026

Ahead of CES 2026, Consumer Technology Association (CTA) CEO Gary Shapiro framed the event with a clear message:

“Manufacturing is transforming rapidly. CES 2026 will showcase the building blocks of the next industrial era.”

From how AI understands physics, to how it makes real-time decisions on site, to how systems are deployed and scaled—these technologies converge on a single question:

How will the next industrial era actually be built?

Trend 1: The Rise of Physical AI — AI Becomes Accountable for Action

At CES 2026, Jensen Huang offered a precise definition of Physical AI:

“True Physical AI begins when AI understands gravity, velocity, distance, and safety logic—and is responsible for the real-world consequences of its actions.”

This marks not just a technological leap, but a shift in responsibility. Traditional industrial AI focused on analysis and recommendations. Physical AI directly influences movement—route choices, applied force, and risk-aware actions.

To enable this, NVIDIA showcased two foundational models:

  • Cosmos: A foundation model trained on large-scale synthetic data to help AI learn physical laws in virtual environments, narrowing the gap between simulation and reality.
  • Alpamayo: Designed for autonomous robots, enabling navigation, object manipulation, and collaboration in complex factory settings.

On the application side, Siemens demonstrated a similar approach. Its next-generation industrial Copilot pushes AI tasks closer to the production line—operating with lower latency near equipment, and forming the basis for safe human-machine collaboration.

If Physical AI answers the question “Can AI truly act?”, it also lays the foundation for everything that follows.

Trend 2: Digital Twins and the Industrial Metaverse Become Operational Systems

Once AI can act in the physical world, the next challenge emerges:
How can these capabilities be operated reliably at scale?

This explains the evolving role of Digital Twins and the Industrial Metaverse at CES 2026. They are no longer just engineering simulation tools, but system-level foundations that connect AI capabilities to daily operations.

This shift is especially evident in supply chain and warehouse environments. Global intralogistics leader KION Group showcased highly realistic Digital Twins that simulate warehouse layouts, equipment scheduling, and human-robot collaboration—feeding optimization results directly back into real operations. Digital Twins are no longer limited to planning; they now influence day-to-day decisions.

At the platform level, the collaboration between Siemens and NVIDIA has also matured. Rather than isolated tools, the focus is now on building an industrial AI operating system that spans design, manufacturing, and operations.

Initiatives such as the upcoming Digital Twin Composer (expected mid-2026) and high-fidelity physics simulation integrated with NVIDIA Omniverse point toward a common goal: scalable, reusable industrial systems.

As KION Group CEO Rob Smith summarized:

“We are using Physical AI to make supply chains smarter, faster, and ready for the future.”

Only when Digital Twins become part of operational systems does the Industrial Metaverse truly enable Physical AI at scale.

Trend 3: AMD’s Bet — Edge Computing Becomes the Battleground

In manufacturing and logistics, latency is not a user-experience issue—it is an operational risk. High-speed SMT machines, autonomous mobile robots (AMRs), and real-time warehouse scheduling cannot wait for cloud round-trips.

“As AI adoption accelerates, we are entering the YottaScale era… AMD is building the compute foundation for the next phase of AI.”
— Lisa Su, CEO of AMD

At CES 2026, AMD emphasized pushing inference directly to the edge:

  • High-performance, low-latency inference: Up to 50 TOPS of AI compute enables real-time analysis of sensor data, images, and process states without relying on the cloud.
  • Data-local security architecture: Models run on-premises, keeping sensitive data inside the factory—aligning with rising governance and security demands.

Notably, this edge-AI strategy is blurring the line between automotive and industrial technologies. Software-defined vehicles are essentially high-speed edge data centers, and AMD’s ADAS architectures can be directly applied to factory AMRs and automation systems—demonstrating rapid cross-domain convergence.

Trend 4: From Chatbots to Agentic AI — Toward Hyperautomation

Under the theme “AI for All: Everyday, Everywhere,” Samsung outlined a clear enterprise direction at CES 2026:
AI is no longer reactive—it is becoming proactive.

This is the essence of Hyperautomation, as demonstrated by Samsung SDS. Unlike traditional chatbots that respond to prompts, Agentic AI understands objectives, decomposes tasks, gathers information across systems, and adapts actions dynamically—acting as a true operational agent.

In supply chain management, for example, AI no longer merely flags delivery delays. It proposes alternatives, evaluates impact, and supports faster decision-making.

Hyperautomation is therefore not just about speed—but about reducing cognitive load in increasingly complex enterprise environments. The ability for AI to integrate data, systems, and workflows is rapidly becoming a competitive differentiator.

Trend 5: Robots Gain Fine-Grained Perception and Enter Human Spaces

In the robotics zones at CES 2026, the focus has shifted. The question is no longer how fast or how heavy robots can operate—but how delicately, safely, and adaptively they can work alongside humans.

Historically, industrial robots were confined by cages—not only because of speed, but because they relied on fixed paths and predefined force in structured environments. That constraint is now loosening.

Multiple vendors showcased robots with emerging tactile and fine-grained sensing capabilities. Japanese company FingerVision, for example, demonstrated optical tactile sensors that allow robots to detect pressure, slip, and deformation through their fingertips—adjusting grip in real time. This enables handling irregular or soft objects previously dependent on human dexterity.

As a result, robots are expanding into tasks such as picking, packaging, and precision assembly—areas requiring real-time judgment and adaptation.

CES 2026 also featured non-traditional robot forms, from mobile multi-leg platforms to ultra-light, high-precision robotic arms—designed not for isolation, but for shared human spaces.

This evolution represents a fundamental shift: robots are no longer just mechanical hands, but collaborative partners capable of understanding environments and aligning with workflows.

The Critical Gap: Invisible Experience

CES 2026 showcased a world where technical prerequisites are falling into place. AI understands physics, computes at the edge, orchestrates workflows, and robots leave their cages. Yet beneath these advances lies a deeper, structural challenge:

Has decision-making experience truly been preserved?

While Digital Twins accurately model physical states, they cannot capture the intuition of veteran engineers. At the intersection of automation and workforce transitions, the true urgency for manufacturers is transforming invisible expertise into reproducible intelligence.

This is where Profet AI’s Domain Twin™ fills a critical gap. Rather than modeling states, Domain Twin™ models decision logic—capturing expert trade-offs, parameter judgments, and quality criteria so AI learns how to decide, not just what to simulate.

Through Domain Twin™, Profet AI transforms decades of shop-floor know-how—process tuning, quality assessment, parameter selection—into reusable AI models. These models encode conditional judgment: under which circumstances, what decision should be made.

On top of this, AI Studio, Profet AI’s agentic AI collaboration platform, acts as an internal generative AI engine—integrating documents, records, and organizational knowledge so AI understands not only data, but context.

Together, this architecture directly reflects the Agentic AI and on-site decision trends highlighted at CES 2026—positioning AI as a reliable, scalable partner in real-world operations.

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Feeling the “AI Anxiety”? Where Should AI × Robotics Really Begin?

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Feeling the “AI Anxiety”? Where Should AI × Robotics Really Begin?

“Two or three years ago, people talked about AI with excitement. Today, anxiety seems to outweigh excitement.”

This opening remark by Sophie Chen, Customer Success Manager at Profet AI, cut straight to the point.

A few years ago, AI sparked curiosity and optimism. Today, however, people standing on the production line are asking more urgent questions:
Where should we start? Which process should we choose first? How can the experience of master technicians be translated into models? Why is it so hard to replicate processes across overseas plants? And with labor shortages intensifying, how can quality and efficiency afford to wait?

As the hype around AI fades, manufacturing is confronting a far more realistic and unforgiving question:
Is the factory truly ready for AI to go live—and generate real ROI?

From labor shortages and overseas expansion to the loss of tacit expertise, Profet AI and Primax co-hosted a closed-door event, “Beyond PoC: From Demo to Dollar | AI × Robotics in Action.” Though small in scale, the session pieced together clear answers to what it really takes for AI × Robotics to move from concept to reality.

Elevating the Perspective: Making AI a Common Language in Manufacturing

Profet AI CEO Jerry Huang opened by pointing out how rapidly the manufacturing landscape is shifting. Geopolitical tensions, tariffs, and supply-chain restructuring are forcing companies to build plants and expand capacity across regions at unprecedented speed—exposing a deeper challenge: the widening gap in talent and experience.

“If companies continue relying on hiring and training alone, they will fall behind within three to five years.”

Many organizations have realized that process know-how once sustained by veteran engineers is becoming increasingly difficult to replicate in overseas factories. Huang emphasized that AI matters today not because it is new, but because it can transform experience into a scalable capability.

He described AI in two complementary forms:

  • Machine Learning (the left brain), which handles structured data and predictive tasks
  • Generative AI / LLMs (the right brain), which supports reasoning, interpretation, and decision-making

When combined, AI becomes a carrier of knowledge. The intuition of experienced engineers no longer needs to remain locked in individuals’ minds—it can be encoded into models and transmitted to the next generation of engineers, overseas plants, and even robots.

“What companies really lack isn’t models, but people who can use them,” Jerry noted.
“If a veteran engineer can build a model within three or four hours, the value equation changes completely.”

This is not merely a skills upgrade—it is a cultural shift. When frontline engineers see AI as their tool, rather than something built by external experts, transformation accelerates dramatically.

After the Anxiety: Where Should AI Adoption Actually Begin?

As both a Customer Success Manager at Profet AI and a consultant for Primax’s AI Thinking Workshop, Sophie Chen hears the same question repeatedly—not about technology, but about where to start.

She outlined five common obstacles to real-world AI deployment:

  1. Scenario ambiguity — No clarity on which process to tackle first
  2. Data silos — Data fragmented across manufacturing, QA, R&D, and business units
  3. Talent gaps — A shortage of people fluent in both process engineering and AI
  4. Trust gaps — Veteran engineers skeptical of black-box outputs
  5. Process disconnects — Models built but unable to integrate into daily SOPs

Over the past two years, many companies have stalled at the PoC stage. Models work, reports look impressive—but nothing changes on the shop floor.

“AI implementation doesn’t end with delivering a model,” Sophie emphasized.
“It succeeds only when people actually reach out, use it, and see results.”

The real challenge lies not in technology, but in translation and guided execution.

She introduced Profet AI’s AI Thinking Workshop, a structured methodology that translates process pain points into actionable AI initiatives:

Cross-functional alignment → Topic selection → Problem definition → Prototype → ROI calculation

In Primax’s real-world case, a 30-hour workshop narrowed more than 50 ideas down to 8 AI projects, with an estimated annual impact of NTD 16 million in savings and 20 months of labor time.

“AI is a catalyst,” Sophie concluded, “but decision-making and knowledge always remain in human hands.”

From Inspection to Prediction: Primax × Profet AI in Process Intelligence

Many electronics manufacturers already rely on AOI systems. These systems can identify defects—but they rarely explain why defects occur, nor can they provide early warnings before issues escalate.

This leads to three recurring pain points on the factory floor:

  • Problems are detected too late
  • Parameter adjustments lack clear justification
  • Valuable data remains underutilized

According to Benson Wang, Product Director at Profet AI, this was the starting point of the collaboration with Primax: transforming AOI from a detection tool into a decision-support and predictive system.

Primax brings deep expertise across vision, audio, and human–machine interfaces, along with rich imaging data. Profet AI contributes AutoML and domain AI methodologies to structure that data into models.

He illustrated the collaboration through a dispensing process case:

  • AOI quantifies glue-line image features (overflow, offset, spacing, etc.)
  • Data is fed into Profet AI AutoML for training
  • Key factors correlated with airtightness defects are identified
  • Results are validated through production-line back-testing
  • Models are deployed to Vision Hub for real-time edge inference

Once deployed, AOI no longer merely records defects—it provides predictive signals earlier in the process, enabling engineers to intervene proactively.

“This isn’t about replacing human expertise,” Wang emphasized.
“It’s about providing more stable, evidence-based judgment—reducing trial and error and lowering the risk of batch scrap.”

The model is also highly scalable: once validated at one station, it can be replicated across processes, plants, and even extended to robotic inspection and exception handling.

From Equipment to Autonomous Mobility: Primax’s Technical Foundation for AI Deployment

From Equipment to Autonomous Mobility: Primax’s Technical Foundation for AI Deployment

“Images, sound, and motion data have always existed—but they were never organized. AI makes this data useful for the first time.”
— Tim Feng, Senior Manager, Primax

For Primax, bringing AI into real operations was not a sudden shift. Its longstanding expertise—from camera modules and peripherals to motors and automation equipment—has enabled stable data acquisition and system integration on the factory floor.

This foundation allowed seamless collaboration with Profet AI. Primax ensures data is captured and visible; Profet AI transforms it into models and predictions. The result is a smarter, more replicable production flow.

Beyond equipment, Primax has also explored Autonomous Mobile Robots (AMR)—for a surprisingly practical reason.

“We didn’t build AMRs because it was trendy,” said Eddie Chen, Marketing Deputy Manager at Primax.
“Our restaurant genuinely lacked manpower.”

The company turned its own cafeteria into a real-world testing ground, allowing AMRs to operate among people, learn from daily interactions, and accumulate real operational experience—naturally extending the AI and robotics ecosystem.

Seeing Is Believing: AMR in Action at the Primax Cafeteria

At the end of the event, participants visited Primax’s cafeteria to observe AMRs in operation.

There were no fixed seats or scripted demonstrations. The robot waited near the counter, received tasks, planned routes, navigated crowds, avoided obstacles, adjusted paths, and delivered coffee to designated tables.

This final experience served as a fitting conclusion to the From Demo to Dollar discussion.

AI × Robotics was no longer a concept—it was functioning seamlessly within everyday workflows. For participants, this simple observation completed the picture:

The future factory is already taking shape—not in grand visions, but in normalized, deployable, real-world scenarios.

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Domain Twin™ Across the Semiconductor Manufacturing Flow

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Domain Twin™ Across the Semiconductor Manufacturing Flow

Accelerating R&D to High-Volume Manufacturing and Enabling Reusable Process Know-How

Semiconductor manufacturers operate under continuous pressure to meet production commitments while advancing future technology nodes and capacity expansion. Competitiveness is largely determined by the ability to transition processes from pilot to high-volume manufacturing with minimal delay, stabilize process windows early in the ramp phase, and achieve consistent yield across tools, lines, and fabs.

Although advanced analytics and AI techniques have been increasingly introduced into semiconductor manufacturing, the key challenge is no longer data availability. The limiting factor is how efficiently validated process knowledge can be extracted from experiments, encoded, and reused to shorten development cycles and reduce variability during ramp-up.

AI has been applied to use cases such as process optimization, yield prediction, and fault detection. However, these approaches deliver sustainable value only when they are tightly coupled with process physics, engineering constraints, and accumulated decision logic, rather than operating as standalone statistical models.

Structural Limitations in Current Process Development and AI Deployment

In most fabrication environments, critical process tuning and excursion handling remain highly dependent on senior engineering expertise. Recipe adjustments, parameter tradeoffs, and root-cause hypotheses are often based on tacit knowledge accumulated through experience. This knowledge is rarely formalized, making it difficult to transfer across shifts, teams, production lines, or fabs.

At the same time, manufacturing data is distributed across MES, SPC, FDC, EDA, inline metrology systems, and local experiment records. Although large volumes of data exist, they are not organized in a way that preserves engineering context. As a result, correlations and validated operating ranges identified in one development cycle are not systematically reused, leading to repeated DOE iterations and extended process window convergence.

This fragmentation directly affects NPI timelines, yield ramp speed, and cross-fab consistency, particularly for advanced process nodes and complex packaging flows.

From Data-Centric to Knowledge-Centric Process Engineering

To address these constraints, the industry is increasingly shifting from purely data-driven analytics toward knowledge-centric process engineering. The objective is not only to predict outcomes, but to retain the underlying process logic that connects parameters, responses, and engineering decisions.

Domain Twin™ is positioned as a process knowledge system that captures experimental context, model outputs, and engineering judgment in a structured and reusable form. Rather than treating models and experiments as isolated artifacts, Domain Twin™ organizes them into a persistent representation of process behavior, including validated parameter ranges, response sensitivities, and decision rationale.

By formalizing this information, process knowledge becomes traceable to source data, transferable across tools and fabs, and extensible to new products and technology nodes. This reduces reliance on individual expertise and improves decision consistency during both development and production phases.

AI as an Enabler of Faster Process Window Convergence

As semiconductor manufacturing capacity becomes increasingly standardized, differentiation shifts toward execution efficiency and process maturity. AI delivers value when it accelerates root-cause identification, reduces experimental iterations, and improves yield predictability during ramp-up.

Within Domain Twin™, machine learning models capture nonlinear relationships between process parameters and key responses such as yield, defect density, and uniformity metrics. These predictions are evaluated in the context of historical experiments and engineering constraints, allowing engineers to screen parameter combinations prior to physical trials.

Generative AI further supports interpretation by summarizing trends, highlighting dominant factors, and referencing similar historical cases. This enables faster convergence on stable operating windows while maintaining engineering interpretability.

Platform Overview and Semiconductor Manufacturing Coverage

Domain Twin™ is implemented as an enterprise-grade AI platform supporting on-premise and private cloud deployments. It integrates process data ingestion, model development, experiment tracking, and knowledge management within a unified framework.

The platform supports semiconductor manufacturing workflows across the value chain, from upstream design analysis and yield interpretation to midstream wafer fabrication and downstream assembly and packaging operations. In each case, the emphasis is on reducing the cycle time between learning and execution by retaining validated process knowledge in a reusable form.

Upstream IC Design and Yield Analysis

In design and test stages, engineers analyze yield maps, parametric test results, inline inspection data, and equipment logs to assess the impact of design changes or process variations. Manual consolidation of this information is time-consuming and often inconsistent across teams.

With Domain Twin™, generative AI interfaces with structured design and test datasets to extract yield trends, identify dominant failure modes, and generate traceable analysis outputs. This allows engineers to focus on interpretation and decision-making while maintaining consistency and repeatability in reporting and analysis workflows.

Midstream Wafer Fabrication and CMP Optimization

In wafer fabrication, processes such as CMP, thin-film deposition, lithography, and etch exhibit strong multivariable interactions. Metrics such as removal rate, within-wafer non-uniformity, defect density, and edge effects are sensitive to tool settings, consumables, and environmental conditions.

Process optimization in these modules often relies on iterative DOE, with knowledge distributed across individual tools and engineers. Domain Twin™ consolidates process parameters, experimental paths, and metrology responses into a unified knowledge structure. Machine learning models identify high-impact variables and predict process responses under candidate conditions, while generative AI assists in interpreting trends and potential mechanisms.

 

In CMP applications, this approach enables early estimation of removal behavior and uniformity trends, reducing experimental iterations required to reach a stable process window and improving ramp-up robustness.

Downstream Assembly, Packaging, and Yield Control

In assembly and packaging processes such as wire bonding, die attach, and molding, small deviations in parameters can significantly affect yield and reliability. Variability across machines and sites further complicates process replication.

Domain Twin™ enables structured capture of machine settings, quality metrics, and corrective actions. Predictive models estimate quality outcomes under different parameter combinations, while generative AI supports diagnosis and knowledge reuse. This shifts tuning activities away from trial-and-error toward systematic reuse of validated settings across lines and fabs.

From Pilot Projects to Sustainable Manufacturing Impact

Many AI initiatives struggle to scale because models remain disconnected from process knowledge and engineering workflows. Domain Twin™ addresses this by treating process knowledge as an institutional asset rather than a byproduct of individual projects.

By structuring experiments, models, and decision logic within a unified system, manufacturers can reduce development cycles, stabilize yield earlier, and replicate proven processes across fabs. AI thus becomes an integrated component of process engineering, supporting high-volume manufacturing and long-term competitiveness.

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Nankang Tire × Profet AI: From the Era of Fuel to the Age of “Digital Tires” — The Road to Traditional Industry Transformation

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Nankang Tire × Profet AI: From the Era of Fuel to the Age of “Digital Tires” — The Road to Traditional Industry Transformation

After six decades in business, why must Nankang Tire, a true reflection of Taiwan’s manufacturing industry, undergo transformation?

Founded in 1959, it has witnessed Taiwan’s evolution from OEM to brand creation and accompanied the automotive industry from fuel-powered to electric vehicles. Its product line continues to expand—ranging from passenger car tires to off-road and winter tires—with more than 3,000 specifications accumulated.

However, in today’s data- and algorithm-driven age, traditional methods are failing: products are increasingly diversified, delivery times shorter, and demand more volatile. Any small error can snowball into massive costs.

“We produce in small batches with high variety,” said Fu-Chieh Chang, Deputy Manager of Product Development, “and that complexity forces us to respond faster. If we still rely on manual scheduling and paper records, we’ll be dragged down by inefficiency.”

The root problem lies in scattered information, unshareable experience, and inconsistent communication—each link operates in isolation, lacking a platform that enables full-chain collaboration.

Thus, Nankang Tire began to reevaluate its entire process—from R&D to production, from inventory management to order forecasting—step by step identifying where automation and datafication could be applied.

The New “Kung Fu” of Traditional Manufacturing: How Nankang Tire Uses Data to Push R&D Limits

“Tire manufacturing is a tough business,” Chang admitted. “We’re squeezed between global giants like Michelin and Continental on one end, and low-cost competitors on the other. We have to survive in the middle.”

Nankang exports to 189 countries, with 90% of its production sold overseas. Yet efficiency and cost remain a constant tug of war.

“The R&D cycle for a new tire pattern is long—it takes one to two years from concept to market,” said Chang. “It involves design, mold trials, verification, and corrections—each step takes time.”

To shorten both R&D and production, Nankang Tire partnered with Profet AI to introduce AI into key processes. From R&D to marketing, Profet AI is now applied in four major areas:

1. Contact Patch Prediction

Previously, engineers had to draw, mold, and run a contact experiment to check how the tire touches the road—a process that took several days and significant manpower. “Sometimes it took half a month to get results, and if the contact was uneven, we had to start over,” said Chang.

Using Profet AI’s AutoML, Nankang imported thousands of R&D data points—covering mold design, dimensions, and weight—to build a contact patch prediction model. Now, engineers simply input design parameters, and within seconds, the model predicts contact length and width while comparing against historical best samples.

The experimental cycle shortened from 12 days to about 3.5 days, dramatically cutting mold-testing costs and manpower.

2. Simulating Snow with AI — Reinventing Rubber Compound Design

Performance depends not only on tread pattern but also on rubber compounds. “Our product range is vast, and matching the right compound is key,” said Chang. For the European market, winter tires must balance low-temperature flexibility with wet-surface grip—two traits that often conflict.

“Sometimes, when low-temperature stability improves, wet grip drops,” he explained. Taiwan’s warm climate and lack of low-temperature test facilities make R&D even harder.

With Profet AI, Nankang transformed past test data into predictive models that simulate how different compounds behave in cold conditions. By inputting formula ratios and material properties, engineers can now predict glass transition temperature (Tg) and modulus variation, helping identify key performance factors early.

Though minor deviations remain, the results show great potential: tests that once had to wait for winter can now be simulated virtually—speeding up R&D and market response.

3. Turning the Master’s “Touch” into Models — Tread Length Optimization

“This step requires adjusting length per specification. It used to rely on craftsmen’s intuition,” said Chang. “With so many specifications, human error was unavoidable,” often leading to scrap and rework, increasing costs.

Through Profet AI, the system now automatically recommends optimal parameters based on environmental and machine conditions. When changing specifications, operators input batch and temperature data, and the AI provides recommended values and deviation alerts.

Analysis showed that inaccurate settings once caused annual rework losses of over one million NTD. With AI, Nankang expects significant waste reduction and to turn personal know-how into collective factory intelligence.

4. Forecasting Orders with AI — Smarter Scheduling

Nankang also fed historical ERP order data into Profet AI for automated trend modeling. Each month, sales teams input recent data, and the system forecasts next month’s demand—supporting production planning and material preparation.

Early tests show the model can predict demand changes with promising accuracy. In the future, Nankang aims to anticipate hot-selling sizes, streamline production flow, and optimize resource allocation—reducing both idle time and waste.

“Traditional industries are like martial arts heroes,” said Chang with a smile. “They must constantly refine their inner strength through new techniques. For us, AI is that new ‘kung fu’—rebuilding our foundation.”

From Resistance to Co-Creation: How “Touch” and “Data” Learn to Communicate

For many employees, AI first appeared as an unfamiliar rival, fast and precise but speaking in data, not intuition. Craftsmen spoke the language of feel and experience, so friction was inevitable.

Chang admitted the hardest part was converting experience into data. In processes like calendering, factors such as room temperature, water temperature, and roller speed all affect results. “Some craftsmen say this compound needs to run slower, others say faster, but those terms ‘slow’ or ‘fast’ were never quantified.”

To address this, Nankang focused on mindset, mentorship, and motivation. R&D and line supervisors personally accompanied workers to fine-tune models. “That companionship matters, when they see results, the resistance fades,” said Chang.

But more important, he added, was a sense of achievement. When workers saw their expertise transformed into models and shared across the factory, they felt proud, their experience was no longer invisible but institutionalized.

To sustain this learning culture, Nankang established a skills assessment system, tracking employees’ learning and application progress. “Supervisors can see their team’s growth, and employees can see their own progress.”

“AI can be a tool, an assistant, even a consultant,” concluded Chang. For Nankang Tire, the goal of AI adoption is not just technical integration but the moment when people truly learn to move forward together with AI.

Nankang Tire × Profet AI: From the Era of Fuel to the Age of “Digital Tires” — The Road to Traditional Industry Transformation Read More »

Profet AI and Intelligent Systems Innovation (ISI) Sign Memorandum of Agreement to Advance AI Transformation in the Philippines

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Profet AI and Intelligent Systems Innovation (ISI) Sign Memorandum of Agreement to Advance AI Transformation in the Philippines

Profet AI, a leading industrial AI software company from Taiwan, officially signed a Memorandum of Agreement (MOA) with Intelligent Systems Innovation (ISI), a Philippine technology company specializing in intelligent systems, automation, and applied AI solutions. This partnership marks a significant milestone in accelerating the country’s digital transformation and strengthening collaboration between Taiwan and the Philippines in the field of applied artificial intelligence.

A Shared Vision for AI-Driven Industrial Transformation

The MOA signing ceremony took place at Profet AI’s headquarters in Taipei, with representatives from both organizations in attendance, including Dr. Elmer Dadios, Founder and Chairman of ISI, and Dr. Alvin Culaba, Distinguished Professor of Mechanical Engineering at De La Salle University, alongside Profet AI’s executive team led by Global General Manager Jonathan Yu.
The event symbolizes a strong commitment to fostering cross-border cooperation in AI education, research, and industrial innovation.

ISI operates at the intersection of industry, academia, and government collaboration, focusing on the development of intelligent systems and automation technologies. Supported by the Philippine Department of Science and Technology (DOST), industry leaders, and top universities, ISI’s multidisciplinary team of engineers, researchers, and innovators plays a vital role in advancing AI research and driving its real-world adoption across sectors.

Empowering Industry, Academia, and Society

Through this partnership, Profet AI and ISI aim to strengthen the capabilities of local industries, including semiconductors, electronics, FMCG, and utilities, by leveraging Profet AI’s Domain Twin™ Platform and its library of over 5,500 industrial AI use cases. The collaboration will support companies in transforming expert knowledge into AI-driven assets, improving production efficiency, quality, and resilience.

In addition, the two parties will collaborate to promote AI education and talent cultivation in universities and research institutions, helping bridge the knowledge and skill gap in the rapidly evolving digital manufacturing landscape. This initiative aligns closely with the Philippine government’s Industry 4.0 and Smart Manufacturing roadmap, promoting sustainable growth and innovation through intelligent automation.

Accelerating AI Adoption Across the Philippines

The partnership represents a shared belief that AI adoption must go beyond proof-of-concept—to deliver tangible, scalable impact in daily operations and decision-making. Profet AI’s Domain Twin™ framework, which integrates AutoML, AILM, and AI Studio, provides manufacturers with a no-code, ready-to-deploy environment that accelerates digital transformation and achieves measurable ROI within 90 days.

“We’re honored to collaborate with Intelligent Systems Innovation to bring real-world AI transformation to the Philippines,” said Jonathan Yu, Global General Manager of Profet AI. “Together, we are not just introducing technology, but empowering industries, educators, and innovators to co-create the future of intelligent manufacturing.”

“This partnership represents a new chapter in AI education and industry collaboration,” added Dr. Elmer Dadios, Founder and Chairman of ISI. “By working closely with Profet AI, we can equip local enterprises and academic institutions with the tools and knowledge needed to thrive in the AI era.”

Shaping the Future of AI in the Region

With the signing of this MOA, Profet AI and ISI are paving the way for sustainable, inclusive AI transformation across the Philippines and beyond—empowering businesses, education, and communities to harness AI for growth and resilience.

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