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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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AI Enters the “Second Half”: How Profet AI Turns Investment into Real Productivity

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AI Enters the “Second Half”: How Profet AI Turns Investment into Real Productivity

From Proof of Concept to Proven Performance: How Profet AI Empowers Manufacturers to Turn AI Investment into Real-World Productivity

Once, Blockbuster held the world’s largest trove of user data but still lost to Netflix, who began as a mail-order DVD service. Nokia, the longtime ruler of the mobile phone market, fell to Apple amid the smartphone revolution. These stories remind us: in waves of technological change, seeing the shift but failing to act is often riskier than not seeing it at all.

Profet AI’s Global General Manager Jonathan Yu notes that AI stands at a similar inflection point today: “Whoever can move AI beyond proof-of-concept—integrating it into real decisions and processes—will take the lead in the next generation.”

On October 29, Profet AI hosted “Beyond PoC: From Demo to Dollar — The Ongoing Realization of AI Investment,” bringing together academic experts and industry partners to explore how AI can move from concept validation to value creation.
Professor Morris Fan, Dean of the College of Management at NTUT and Chairman of the Chinese Institute of Industrial Engineers, analyzed the global gap between AI theory and practice.
James Yang, Executive Assistant to the CEO, shared strategies and challenges in enterprise adoption of generative AI—revealing how companies can turn demos into real, measurable value.

Decoding the World: From AI Theory to Value Realization

“In the past, we said ‘seeing is believing.’ In the world of AI, it should be the other way around—‘to believe is to see.’”
 Professor Morris Fan opened with this statement, emphasizing a key mindset for AI adoption: only by believing first can organizations unlock value.

He described the past decade as AI’s “first half.” From AlphaGo’s victory over Lee Sedol in 2016 to AlphaEvolve, which can now generate its own questions and answers, AI has proven superhuman capabilities in specific domains. But the next question for enterprises is: “How do we play the second half?” In other words, no matter how powerful a model is, if it doesn’t integrate into workflows, decisions, or products, it remains stuck in the proof-of-concept (PoC) stage.

True value realization isn’t about the success of a single project—it’s a continuous cycle. Fan outlined three layers for AI implementation:

  1. Production AI-Landing – smart manufacturing operations
  2. Operation AI-Landing – intelligent business management
  3. Product AI-Landing – AI-enabled products

These layers continuously calibrate and reinforce one another: production data feeds product development; market needs loop back into operational decisions—forming a complete, closed-loop system.

Fan also reminded attendees that AI deployment is never “one click and done.” Enterprises must use version control, access management, and health monitoring to ensure long-term stability. Beyond setting realistic goals, they must build human-in-the-loop validation to keep decisions grounded.

He cited a cautionary study: when people rely heavily on AI-generated content, brain activity drops by an average of 47%. “After eight minutes, you forget what you were even writing,” he warned. “Blindly trusting AI earns you zero points. Only those who truly understand processes and data relationships are qualified to talk about AI.”

The Starting Line for Enterprise Gen AI: Exit and Succession

Many companies are racing to invest in AI—but does that guarantee entry to the “second half”?
 This was the question explored by James Yang in his session on enterprise-level generative AI.

“PoC was meant to mean Proof of Concept—but it’s become the Prison of Concept,” James declared, pinpointing a widespread issue: countless projects never make it beyond demo stage. According to MIT research, 95% of companies that invest in AI see no tangible return. “If AI is just a chatbot, it’s an island. Only when it connects to processes can it become true enterprise productivity,” he stressed.

James explained that the real goal isn’t to keep AI confined to one department, but to build a corporate AI brain—a system that captures, governs, and applies knowledge across the organization.
 Drawing from MIT’s findings, he summarized four traits shared by companies that successfully deploy AI:

  1. Embed into process – AI must be part of daily operations, not just an FAQ tool.
  2. Leverage ecosystem – Stop comparing models and frameworks; focus on integration, not reinvention.
  3. Empower creation – Enable employees to quickly build their own AI Agents, rather than routing every need through the CoE.
  4. Be pragmatic – “When the boss says, ‘Let’s do AI,’ the first thing everyone does is buy GPUs,” Yang quipped. Many firms spend money before identifying the real problems they want to solve.

Following this logic, Profet AI is developing a new generation of connected architectures. Through standardized technologies like MCP (Model Context Protocol), enterprise systems will be able to interact with AI more smoothly—making knowledge-based AI truly actionable.

However, Yang stressed that to turn AI into a corporate asset, two pain points must be addressed: cost and cybersecurity.
 He shared a story from his time at MediaTek:
 “After API integration, we burned through NT$120,000 worth of tokens per day for two days—NT$240,000 total. That invoice was painful for everyone to see.”
 The incident taught him the importance of strict cost and access control when deploying Gen AI platforms.

To that end, Profet AI plans to collaborate with Zentera, a Silicon Valley partner, to co-develop an AI agent management and protection architecture—ensuring that enterprises can deploy Gen AI with cost efficiency and data security.

From Product to Culture: Becoming a Company Where “Knowledge Never Retires”

From global AI trends to enterprise adoption challenges, the conversation ultimately returned to one question:
 How can AI become an enduring organizational capability?

Profet AI’s technical team has embedded the idea of “From Demo to Dollar” into its platform design. At the center lies the Enterprise AI Brain, which records, governs, and reuses knowledge.
 From AutoML to AILM to AI Studio, the platform helps companies not only solve problems—but also remember how they solved them—transforming AI into an evolving corporate memory that preserves and extends expertise.

Jonathan Yu shared that Profet AI now operates in 11 countries, serving over 300 clients, 70% of which are publicly listed companies. Amid global shifts in manufacturing, he believes the biggest challenge isn’t building new factories—it’s preserving organizational know-how and helping new teams get up to speed quickly.

He emphasized that successful digital transformation is not just about adopting tools—it’s about upgrading organizational thinking.
 “Our most successful customers share one trait: they treat AI not as an outsourced service, but as part of their corporate culture,” Yu noted.
 From internal education and cross-department collaboration to data governance and decision optimization, these companies embed AI as a long-term capability, not a one-off project.

“We aim to be a company where knowledge never retires,” Yu concluded.
 When organizations can capture experience and extend wisdom, AI truly moves beyond proof-of-concept—becoming a lasting force for productivity and innovation.

This is the first of many events in the Beyond PoC series. We are planning to bring this event to other cities in Taiwan and event abroad. 

Please fill in the form below if you would like to sign up to get exclusive invites to our future events.

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Digital Twin Meets Domain Twin: A New Era of Intelligent Manufacturing

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Digital Twin Meets Domain Twin: A New Era of Intelligent Manufacturing

As the manufacturing industry rapidly advances into the era of Industry 4.0, companies are adopting AI technologies at an unprecedented pace. According to Data Bridge research, AI in manufacturing is projected to grow at a CAGR of 17.20% between 2022 and 2029, with the market expected to surpass $5.3 billion by 2029.

Among the leading technologies enabling this transformation is the Digital Twin — a powerful solution that simulates physical equipment and processes using real-time data and predictive models. It supports use cases such as predictive maintenance, performance optimization, and real-time monitoring.

However, Digital Twins alone often fall short of delivering true operational intelligence, because they simulate the “what” of machine behavior but lack the ability to understand the “why” behind system performance. This is where Domain Twins come into play.

What Are Digital Twins?

A Digital Twin is a virtual representation of a physical asset, system, or process that mirrors real-time behavior using sensor data and modeling. They provide clear benefits, including:

  • Real-time monitoring of equipment
  • Predictive maintenance alerts
  • Process optimization through simulations

But despite these strengths, Digital Twins face common limitations:

  • They lack human expert judgment and reasoning
  • Over-reliance on historical data reduces adaptability to new or unexpected situations
  • High retraining costs if production conditions change

For example, a Digital Twin may flag a maintenance issue based on sensor thresholds, but it may not recognize a subtle material inconsistency—something a seasoned engineer would immediately notice.

Introducing Domain Twins: Expert Knowledge Made Scalable

To address these gaps, Profet AI introduces the concept of the Domain Twin: an AI-powered solution that digitizes expert knowledge, turning human insights into machine-interpretable rules and models.

While Digital Twins simulate machines and processes, Domain Twins simulate expert reasoning and decision-making. They work together to create a comprehensive, intelligence-driven manufacturing system.

Digital Twin vs. Domain Twin: Better Together

The reality of modern manufacturing is that human experience still bridges the gap between raw machine data and operational decisions. The relationship between Digital Twins and Domain Twins can be seen as a three-layer system:

  • Top Layer (Enterprise Applications & Digital Twin): Simulation and data analytics tools like ERP, MES, and BI systems.
  • Middle Layer (Human Expertise & Domain Twin): Engineers interpret data, applying contextual insights.
  • Bottom Layer (Equipment & Automation): Machines generate real-time data and execute production.

This synergy shows how Domain Twins complement rather than replace Digital Twins. They empower AI to not only detect anomalies but also understand the reasons behind them, and suggest explainable, actionable insights.

4 Key Manufacturing Challenges Solved by Domain Twins

1. Data Silos and Integration Barriers

Most Digital Twins can’t easily integrate with existing ERP or MES systems, creating fragmented data environments.

Domain Twin Advantage:
Standardizes and modularizes expert knowledge, enabling seamless replication across plants and breaking down data silos.

2. Tacit Knowledge Loss

Years of engineering expertise—material behaviors, process tweaks, root cause intuition—are often undocumented and not machine-readable.

Domain Twin Advantage:
Captures this hidden expertise and embeds it into models, ensuring knowledge is preserved and transferable.

3. Data Overload Without Insight

Sensors generate endless data, but without context, it’s hard to act on it effectively.

Domain Twin Advantage:
Adds expert reasoning to AI models, transforming raw data into meaningful, executable recommendations.

4. Low Trust in AI Decisions

When AI outputs are black boxes, plant managers and engineers hesitate to rely on them.

Domain Twin Advantage:
Boosts explainability through embedded expert logic, increasing trust and making AI adoption smoother and more practical.

Real-World Impact of Domain Twin Technology

Developed by Profet AI, the Domain Twin is already proving its value in industries such as:

  • semiconductors
  • Electronics manufacturing
  • Chemicals
  • Precision manufacturing

     

Benefits achieved:

  • Shortened AI deployment time
  • Improved decision accuracy
  • Increased operational resilience

By integrating Domain Twins into manufacturing systems, these companies have enhanced their ability to adapt to disruptions, scale operations globally, and capture value from their AI investments faster.

Looking Ahead: Smarter Manufacturing Through Synergy

As Industry 4.0 matures, AI’s value in manufacturing will be defined by how well it integrates data with human expertise. Digital Twins provide the foundation. Domain Twins complete the picture.

Together, they unlock the next evolution in intelligent manufacturing—moving from passive monitoring to active, explainable, and scalable decision-making.

Final Thoughts

Profet AI’s mission is to bridge the gap between data and intelligence. By enabling Domain Twins, we’re helping manufacturers future-proof their operations with AI that truly works — not just in theory, but on the shop floor.

Interested in learning how Domain Twins can elevate your factory operations?
Contact Profet AI to explore the next milestone in AI-powered smart manufacturing.

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Key AI Technologies in Manufacturing: A Comparative Analysis of Digital Twin vs. Domain Twin

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Key AI Technologies in Manufacturing: A Comparative Analysis of Digital Twin vs. Domain Twin

In recent years, the rise of Industry 4.0, smart manufacturing, AI applications, and digital transformation has made the concept of the “Digital Twin” increasingly popular in the manufacturing sector. However, as companies begin integrating AI, they encounter several challenges, including insufficient data, talent shortages, and implementation bottlenecks. In response, a new concept has started to gain attention: the “Domain Twin.”

Although the names of these two concepts are similar, their meanings are entirely different. Digital Twin addresses “visible physical problems,” while Domain Twin complements “invisible experiential knowledge.” Only by complementing each other’s strengths and weaknesses can manufacturing move from data-driven to intelligence-driven. This article explores the definitions, differences, and applications of Digital Twin and Domain Twin to help companies make informed decisions in their smart transformation strategies.

What is Digital Twin? A Virtual Replica of Equipment Data

A Digital Twin is a virtual replica of a physical device, system, or process. By connecting sensors and real-time data, it can simulate the state, behavior, and performance of its physical counterpart, helping businesses with monitoring, predictive maintenance, and process optimization.

Core features of Digital Twin include:

  • Creating a data-driven model synchronized with physical assets
  • Real-time simulation of the operation of equipment or systems
  • Commonly used in predictive maintenance, operational status monitoring, and energy efficiency analysis
  • Focused on simulating and monitoring specific machines, processes, or physical equipment

According to the Ministry of Economic Affairs, a globally renowned automobile brand implemented Digital Twin technology and, through integration across various stages from product development to mass production, was able to simulate quality, resource allocation, and process stability in advance, reducing time and cost risks. They also integrated AR for staff training, significantly improving assembly efficiency, accuracy, and on-site safety.

Thus, Digital Twin uses virtual replication and data simulation to help companies better understand equipment conditions, predict risks, and improve overall production and training efficiency. However, while Digital Twin can fully simulate equipment and processes, it cannot capture the experience, judgment logic, and tacit knowledge of seasoned workers, which is where Domain Twin comes into play.

What is Domain Twin? The Key Technology for AI to Mimic Expert Decision-Making

Domain Twin is a different concept that addresses the “human intelligence layer” missing in Digital Twin. It models professional knowledge and industry logic comprehensively, allowing AI to “learn” and reuse human experience. Using a No-Code approach, it can be rapidly applied in different but similar manufacturing scenarios.

In manufacturing, the experience and skills of senior workers are often the result of decades of accumulated wisdom. However, these valuable insights are frequently lost due to retirements or personnel changes. Profet AI’s Domain Twin is designed to solve this issue by digitizing and upgrading the expertise of senior workers in machine calibration, formula optimization, and problem-solving, transforming it into a long-lasting, valuable asset for the business.

Unlike typical AI models, Domain Twin integrates with AutoML (Automated Machine Learning) and AILM (AI Lifecycle Management) platforms to tightly link departments and processes such as R&D, production, and after-sales. This ensures fast end-to-end integration. More importantly, Domain Twin enables key data related to R&D, production, dispatch, testing, etc., to remain internal, safeguarding the company’s core technologies.

Core features of Domain Twin include:

  • Digitizing the knowledge and experience of senior workers into reusable AI model logic
  • No code required, allowing users to directly operate model templates for predictive analysis
  • Designed to address common repetitive issues in manufacturing, such as quality forecasting and defect classification
  • Helping businesses lower AI adoption thresholds, improving modeling efficiency and standardization

For example, after implementing Profet AI’s Domain Twin technology in their PCB production line, a company successfully simulated process parameters like gold and nickel plating in real time. They used AI models to predict the probability of defects and recommend optimal formulas, reducing trial production costs and error rates.
Additionally, through the integration of virtual and real simulations and built-in knowledge modules, they reduced the learning curve for new employees by 40% and accelerated implementation by 50%, creating a more flexible smart manufacturing process.

Comparing Digital Twin and Domain Twin

If Digital Twin is the “shadow” of the factory, Domain Twin is the “brain” of the engineers, because it understands logic, processes, and judgment. It can teach AI to mimic these experiences. Therefore, the focus of Domain Twin lies in virtually replicating industry knowledge and logic, enabling AI to learn and apply this knowledge quickly in various scenarios.

Profet AI’s Vision: Empowering Businesses with AI-Driven Smart Decision-Making

In summary, both Digital Twin and Domain Twin have their own strengths: the former focuses on the virtual simulation of equipment and processes, while the latter infuses human experience and professional judgment. The emergence of Domain Twin fills the gaps left by Digital Twin, making it an essential part of the manufacturing industry’s journey toward smart transformation. Only by complementing each other can these two technologies help the industry overcome transformation bottlenecks and achieve continuous optimization and growth.

At Profet AI, we believe that AI should not be the privilege of a select few experts, but a tool that every business can leverage. Through our Domain Twin solution, companies can quickly transform internal knowledge into repeatable and optimizable smart decision models, truly realizing Knowledge as a Service.

If you would like to know more about Profet AI’s Domain Twin, please fill in the form below to request additional information or schedule a demo.

 

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From U.S. Tariffs to Resilience: Scaling Smart Manufacturing with Domain Twin

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From U.S. Tariffs to Resilience: Scaling Smart Manufacturing with Domain Twin

Insights from Profet AI’s Frontline Experience on How Manufacturers Can Navigate Uncertainty

The United States recently implemented reciprocal tariff adjustments under Section 301 of the Trade Act, imposing additional tariffs of up to 20% on a range of Taiwanese exports. These include critical electronics manufacturing components such as chips, IC packaging materials, and PCB parts, significantly increasing cost pressures on Taiwan’s high-tech industries in the U.S. market. In particular, semiconductor products face tariffs as high as 100% unless they are manufactured at facilities located in the United States, prompting serious concern within the industry about the potential impact.

Through extensive conversations with semiconductor and electronics manufacturing clients, Profet AI has observed a growing consensus:
Even with production lines currently running at full capacity, manufacturers recognize the urgency of developing replicable, transferable process capabilities to address rising costs, shifting orders, and global customer demands—ultimately strengthening operational resilience.

The Semiconductor Industry’s Current Challenges: The Impact of Non-Exemption

Taiwan Still Excluded from Exemptions – Cost Pressures Escalate

Under the updated U.S. tariff policy, many high-tech products exported from Taiwan—including chips, materials, and key electronic components—now face a 20% duty.
While several Asian countries have been able to negotiate lower tariff rates, Taiwan remains subject to 20% tariffs, reducing the price competitiveness of domestic manufacturers in the U.S. market.

Rising Risk of Order Shifts and Diversified Supply Chain Requirements

To reduce overall supply chain costs and risks, many U.S. brand customers are asking suppliers to relocate their production to the U.S. to deal with the cost that may arise with the new tariff rates —intensifying pressure on Taiwanese manufacturers to diversify their global footprint.

Knowledge Transfer Remains a Bottleneck

Many high-tech manufacturing processes still rely heavily on the tacit knowledge and on-site judgment of experienced personnel.
Even with overseas expansion plans in place, manufacturers often struggle with incomplete knowledge transfer and inconsistent process stability, resulting in prolonged ramp-up periods and challenges in achieving reliable yields.

Domain Twin™: Building Transferable Manufacturing Strength to Address Tariff and Order Shift Pressures

Profet AI’s experience working with manufacturing clients reveals that true resilience lies not simply in relocating production, but in the ability to replicate core manufacturing capabilities quickly and effectively across locations.
Faced with rising tariffs and shifting customer demands, manufacturers that proactively develop transferable process intelligence are better positioned to maintain delivery reliability and retain long-term customer trust.

Our solution: Domain Twin™. This technology transforms critical manufacturing knowledge into replicable, deployable digital assets—enhancing consistency and efficiency across multi-site operations.

1. Digitizing Process Knowledge to Enable Replication

Domain Twin™ helps manufacturers capture and structure operational experience, parameter logic, and exception handling procedures into unified digital models—allowing tacit know-how to be standardized, managed, and applied across different production environments.

2. Cross-Site Simulation for Layout and Transfer Optimization

By simulating different regional production conditions, cost structures, and equipment configurations, Domain Twin™ enables enterprises to accurately assess transfer risks and investment requirements, accelerating decision-making and deployment.

3. Reducing Ramp-Up Time and Stabilizing Yields at New Sites

With standardized procedures and data-driven recommendations, new facilities—even those with limited experienced staff—can rapidly adopt proven process logic. This shortens time-to-yield and improves early-stage productivity and consistency.

Tariffs Are Just the Beginning—The Real Challenge Is Scaling Capability

The U.S. retaliatory tariff policy is just one part of the broader transformation pressure facing the industry.
As geopolitical tensions and trade policy uncertainties continue to grow, manufacturing competitiveness will increasingly depend not just on technical expertise, but on the ability to swiftly transfer, replicate, and stabilize operations globally.

Profet AI’s Domain Twin™ enables manufacturers to convert tacit knowledge into explicit, repeatable assets, empowering organizations to adapt rapidly, deploy efficiently, and scale manufacturing capabilities with confidence.

If you would like to know more about how our Domain Twin can help you tackle manufacturing challenges, contact Profet AI to schedule a consultation with our experts.

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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.

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8 Ways that Taiwan Manufacturers are Using AI to Navigate Economic and Geopolitical Uncertainty

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Geopolitical chaos and a looming economic downturn are a growing concerns for manufacturers, given their complex and global operations. Recent threats faced to supply chains, include inflation, trade disputes, war, pandemics, and energy access. A push for rapid decoupling also creates serious headaches for decision-makers.

Taiwan: The Epicenter of Both Geopolitical Uncertainty and AI Technology 

In the face of these challenges, manufacturers in Taiwan are turning to AI to navigate this increasingly complex global economic landscape. Recently, a four-part summit titled Crossover Talks, organized by manufacturing AI solution provider, Profet AI, brought together industry, academia, and technology experts to discuss the opportunities, challenges, and best practices for manufacturers to implement AI and digital transformation in a challenging economic climate.

Taiwan has some of the most advanced manufacturers in the world and is obviously at the forefront of some of the most worrying geopolitical tensions. It is also at the cutting edge of the technologies that will power the AI revolution, such as semiconductors, which makes it the ideal location for this discussion.

Keynote speakers included experts from AUO, Innolux,Gogoro, Cheng Shin Rubber, Kuan Yuan Paper, Asia Giant Engineering, De Licacy Industrial, Taiwan AI Academy, National Cheng Kung University, National Taiwan University, and Feng Chia University.

8 Takeaways

Profet AI has diluted the four events and hours of discussion into eight key takeaways:

  1. Successful Digital Transformation Requires the Marriage of Data Science and Domain Knowledge

Data science teams in enterprises need to work in conjunction with departments and colleagues with vertical domain knowledge. Profet AI preaches that successful digital transformation relies on the use of the correct data combined with domain knowledge in order to generate the actionable intelligence that can drive decisions.

  1. Prioritize Cross-Department Conversation

For an enterprise to successfully carry out digital transformation, it needs the support of management and effective dialogue and cooperation between departments.

  1. Foster a Habit of Data Collection

For traditional companies to become more data-driven, they need to first start analyzing their existing data and also foster a culture of data collection. Stakeholders need to believe in the power of data to engender change, and there also needs to be an investment in the correct AI tools.

  1. Focus on First Using AI to Solve Key Problems, Not Changing the Whole Company

Companies should avoid seeing AI as a panacea to turn the company around. Instead, they should prioritize specific processes that need to be optimized.

  1. Use AI to Replicate the Knowledge of ‘Old Masters’

Traditional manufacturers often over-rely on the wisdom of senior staff, which makes them vulnerable when processes need to be replicated or there needs to be a handover of know-how if they retire. Data-driven companies can utilize AI to create a framework for both personnel succession and the handover of knowledge.

  1. Let Data Enhance Personnel Management

Participants at the summit complained in recent years, being blighted by worker shortages. Companies can utilize AI to highlight the characteristics of workers that have resigned and which present workers have a high probability of leaving, then reach out to them in advance to see if there is any way to help them.

  1. Manufacturers Need to Recruit and Cultivate AI Talent

For digital transformation to be successful, manufacturers need to change their internal culture and also cultivate their own AI talent. Over the last five years, AUO has trained more than 1,000 AI engineers in a joint initiative with the Taiwan Artificial Intelligence Association (AIA).

  1. Everything Begins with Company-Wide Mindset Change

Director of AIA, Benjamin Kuo, believes that as well as fostering AI talent, there needs to be a mindset change in top management so that the company will become a data-driven organization. Only once the company has both the AI mindset and talent can they really lay the blueprint for digital transformation. Leaders need to ensure that AI is embedded into the DNA of their organizations.

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Profet AI Leads the Way: Four Key Strategies for AI Transformation in Businesses Amidst Economic Uncertainty

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Amid growing global economic uncertainty, Profet AI Co-founder and CEO Jerry Huang reflected, “Since 2022, we have traveled across Asia and, through exchanges with numerous companies, deeply felt the pressure and challenges they face in talent upgrading and global expansion. These businesses urgently need to understand how AI can help them proactively deploy strategies and make critical decisions. This experience has strengthened my belief that our 2022 Crossover Talks: AI & the Economic Cycle series was crucial in helping companies identify opportunities amid economic cycles.”

Pain Points and Critical Analysis: Hsing-Chien Tuan and Dr. Chia-Yen Lee Discuss Transformation Strategies at Crossover Talks

In Crossover Talks: AI & the Economic Cycle, Hsing-Chien Tuan, Honorary Chairman of Innolux Corporation, highlighted that even in downturns, businesses should focus on automation, IoT, big data, and AI. He stressed, “Identify pain points with precision, articulate them scientifically, and set meaningful indicators.” Dr. Chia-Yen Lee of NTU added that AI must now be integrated beyond manufacturing into R&D, advising businesses to understand core strengths, explore global and cross-industry expansions, and embrace disruptive technologies.

Dr. Chia-Yen Lee, from the Department of Information Management at NTU, offered profound insights from both academic and practical perspectives. He stated, “Recently, while consulting with semiconductor manufacturers, I often mention that we used to only need to modify the manufacturing site, but now we need to involve AI, even in research and development, and the future potential may be even greater.” Dr. Lee proposed four key actions: first, businesses should deeply understand their core competencies and invest in key areas; second, when expanding, consider international layout and cross-industry expansion; third, explore new markets through geographic expansion; and finally, integrate disruptive technologies and establish new businesses or services through mergers and acquisitions, while paying attention to retaining talent and ensuring the long-term value of technology.

Dr. Lee further suggested that businesses should embark on their AI journey from five aspects: accelerating time (building a digital nervous system), increasing efficiency (promoting digital transformation and intelligence), improving quality (building a learning organization and strengthening education and training), increasing revenue (achieving production-sales balance), and reducing costs (reducing variation and risk).

Asia-wide Consensus Spanning Two Years: PacRim Group Shares the Inevitability of AI Transformation from a Thai Perspective

This perspective extends across Asia. At the 2024 AI Leadership Summit in Bangkok, Porntip Iyimapun, CEO of PacRim Group, a pioneer in global leadership development and cultural transformation with nearly 30 years of experience, presented similar views: “Digital transformation, leadership transformation, and corporate culture transformation are all important parts of the same plan. The introduction of AI is inseparable from these transformations. Thai enterprises need to make comprehensive adjustments in these areas to successfully integrate AI into their business strategies. Without adopting AI, enterprises will gradually be eliminated.” It not only reflects PacRim Group’s professional insights in leadership development and cultural transformation but also confirms the core position of AI in modern enterprise transformation.

AI Helps Businesses Address Economic Challenges and Future Growth

In Profet AI Crossover Talks, multiple companies shared successful cases of using AI tools. Brandon Yeh, Vice Chairman of De Licacy, shared how AI can be used to achieve technology transfer and solve talent recruitment difficulties. Richard Hsieh, Associate Manager of Kuan Yuan Paper, introduced an innovative management method combining OKR, zero errors, and AI. Kenny Chiu, Deputy General Manager of Shuttle Service, shared the experience of using AI to predict and reduce employee turnover.

These cases clearly demonstrate the application potential of AI in various industries and fields. From manufacturing process optimization to human resources management, AI is helping businesses address various challenges, improve efficiency, reduce costs, and prepare for future growth.

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AI Drives Thailand Market Innovation: Taiwanese Expertise Tailors Solutions

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Driven by global economic shifts and the digital transformation wave, AI has become a core tool for businesses worldwide to maintain competitiveness. Thailand, with its strategic geographic location and thriving automotive, food manufacturing, and retail industries, presents immense potential and demand. For the first time, Profet AI, in collaboration with HexaTech Solutions and PacRim Group, hosted an AI Leadership Summit in Bangkok, sharing Taiwan’s successful experiences to tailor AI solutions for the Thai market and facilitate talent training and upgrading.

Speakers at the AI Leadership Summit (from left to right: Chalermkit Rochanavibhata, partner at PacRim Group; Dr. Sampan Silapanad, president of the Electronics and Computer Employers Association; Dr. Howard Hsieh, secretary-general of the Taiwan AI Association; Jonathan Yu, global business manager at Profet AI)
Learning from Taiwan’s Success: Three Key Elements of Digital Transformation

Taiwan’s successful digital transformation offers valuable insights. Dr. Howard Hsieh, secretary-general of the Taiwan AI Association, pointed out that AI is not merely an IT project but requires full engagement and talent upgrading. Taking Innolux 4.0’s digital transformation as an example, the integration of technology, methodology, and mindset (organization and people) is crucial. Taiwan’s experience demonstrates that successful digital transformation requires technological advancements, methodological applications, and continuous optimization of organizational culture, enabling the transition from small-scale projects to full-scale implementation and achieving the status of a lighthouse factory.

Thailand’s AI Landscape: Urgent Need for Transformation to Remain Competitive

In Thailand, the urgency of digital transformation has garnered widespread attention. Dr. Sampan, president of the Electronic & Computer Employers’ Association, emphasized that Thai businesses risk being eliminated from the market if they fail to adopt AI swiftly. He stated, “I envision that within the next five years, over 50% of Thai companies will be AI-oriented; otherwise, Thailand will disappear from the map. This will be the key to entering a highly competitive world.” This perspective underscores the decisive impact of AI on the competitiveness of Thai businesses.

Tailored AI Solutions: Creating Custom Solutions for the Thai Market 

To address the specific needs of the Thai market, Profet AI, HexaTech Solutions and PacRim Group have provided tailored AI solutions that not only meet the market’s practical demands but also encompass applications from business innovation to R&D, helping Thai businesses navigate future challenges. Additionally, Porntip Iyimapun, CEO of PacRim Group, added that digital transformation, leadership transformation, and corporate culture transformation are integral parts of the same plan. She emphasized that AI implementation is intertwined with these transformations, and Thai businesses need to make comprehensive adjustments in these areas to successfully integrate AI into their business strategies.

Facilitating Talent Training and Upgrading: Laying the Foundation for Digital Transformation

The success of digital transformation hinges on the skill enhancement of the workforce. PacRim Group, with 30 years of experience, will focus on providing professional training to equip Thai employees with AI skills and foster an AI-ready culture. This will lay a solid foundation for the long-term development of Thai businesses, ensuring that the entire workforce possesses the capabilities required for the AI era. The digital transformation and AI applications of local businesses will set new benchmarks.

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