On April 2, U.S. president Donald Trump announced the launch of large-scale “reciprocal tariffs.” Once the news came out, corporate pricing, inventories, procurement rhythms, and market expansion plans were thrown into disarray. The U.S. and China quickly entered a cycle of retaliation and counter-retaliation, straining cross-border supply chains and plunging the entire world into unprecedented uncertainty.

Half a year later, countries were stuck at the negotiating table, while businesses were struggling to adjust their survival strategies under mounting cash flow pressure. In such an environment, any system that can reduce costs and improve cash flow flexibility has become more important than ever.

Precisely at this time, a mechanism that had been overlooked for more than 200 years was once again discussed: “duty drawback.”

“Many companies don’t even know that the tariffs they pay are refundable,” said Penny Chen, founder of Pax, a startup that uses AI to help companies reclaim taxes. In her interviews with clients, she sees the same thing over and over again: companies pay tens of billions of dollars in tariffs to the government every year, and only about 20 percent of that money is actually refunded. The remaining 80%—amounting to nearly $15 billion—just sits there unclaimed. “It’s free money left on the table!” she added with a sense of helplessness.

This huge disconnect unexpectedly became an entry point for Pax. With AI at its core, Pax uses algorithms to help companies identify 15% more refundable tariffs than traditional service providers, streamlining processes that would otherwise take more than six months to just over ten working days. For the first time, organizations are realizing that AI tools can transform tariff refunds from a burdensome process into an immediate source of cash flow.

Companies Don’t Have to Overpay Tariffs! But Who Is Eligible for a Refund?

A “duty drawback” is a refund administered by U.S. Customs and Border Protection (CBP) that allows businesses to recover import duties they have already paid; it is distinct from the more familiar income tax refund. Under U.S. law, any business that has paid duties on goods imported into the United States can recover part or all of the duties if the goods are subsequently re-exported, re-exported after processing, or destroyed within the United States, among other qualifying conditions.

The most common case comes from the manufacturing industry: if a company imports raw materials from overseas, processes them in the United States, and then exports the finished products, the tariffs previously paid can then be refunded in accordance with U.S. law. Another typical example comes from

Another typical example involves retailers and distributors: if a company imports merchandise from overseas and re-exports it without selling or using it in the United States, the duties previously paid can also be refunded under U.S. law.

Cross-border e-commerce companies and large retailers, which have grown rapidly in recent years, are also frequently eligible for tax refunds. Shipments moved from within the U.S. to overseas warehouses are considered exports. If a consumer returns shipped goods that are then destroyed in the U.S., duties on those goods can also be recovered.

In other words, many routine logistics operations, such as moving warehouses, returning goods, or destroying products—which on the surface may not seem directly related to revenue—can in fact be a source of substantial tax rebates. As long as companies can clearly track the flow of their goods, they can reclaim funds that already belong to them.

Complicated Processes and Outdated Tools: Why Traditional Tariff Refunds Are So Difficult

Although the system itself is not difficult to understand, the high complexity of implementation is the most challenging aspect for companies. Penny Chen explained that, in addition to different commodities and various import and export scenarios, a bigger headache is that the information required for tariff refunds is often scattered across PDFs, Excel files, and ERP systems. Companies are tasked with organizing data from piles of invoices, customs declarations, and logistic records—which use different formats and layouts—into the specific format required by government agencies, an extremely time-consuming process.

Due to this complexity, many companies choose to outsource to specialized service providers, but this does not necessarily make the problem any simpler. Traditional providers continue to rely on software that has been in use for more than 20 years, and the process still depends heavily on manual labor. Each case has to be manually entered and compared line by line, and a company applying for a refund for the first time may need to wait an entire year from the time it submits the relevant documents to when it actually receives the refund.

Because the process is so cumbersome and slow, many service providers are only willing to take on large clients with potential refunds of $100,000 or more per year. As a result, even though SMEs are eligible for tariff reimbursement, they are often unable to find anyone who is willing to handle their cases.

“This Is a Fun Math Problem!” How Pax Uses Algorithms to Help Companies Recover 20% More in Tariff Refunds

Chen’s earliest exposure to tariff refunds came when she worked as a researcher at Flexport. She quickly realized that the process involved extensive data cleansing and rule comparison. Through exchanges with industry experts, she sought to understand the market’s real pain points. “I found that everyone’s dilemma was almost exactly the same,” she said. “They were eligible to recover the tariffs they had already paid, but because they didn’t understand the system, they didn’t have the tools, or they couldn’t find providers who were willing to support them, they ended up with nothing.”

Penny Chen holds a Ph.D. from the Massachusetts Institute of Technology (MIT), where she specialized in algorithmic design. “From my point of view, tariff refunds are actually a very interesting mathematical problem—it’s just that no one has ever approached them algorithmically!”

Simply put, Pax is like TurboTax for corporate tariff refunds. TurboTax, the most widely used tax filing software in the United States, breaks down complicated rules into a standardized process that allows taxpayers to file returns with a single click. Pax aims to re-create the same experience for corporate tariff refunds, helping businesses reclaim tariffs without needing to understand the rules, organize the data, or waste time and money on a long, drawn-out process.

However, the first and most difficult step in achieving this is addressing a fundamental pain point: “fragmented data.”

Currently, service providers handling tariff refunds spend most of their time manually preparing data. To proceed to the next steps, companies often have to submit all necessary documents up front, requiring them to be organized in a uniform format. This stage alone demands a significant amount of time and manpower, and it’s the reason why most companies find their first experience with tariff refunds so frustrating.

Pax’s approach eliminates this “manual review” step. Companies no longer need to prepare any documents in advance; they simply provide the raw data to Pax, and the system automatically reads and extracts the relevant information, transforming unstructured data into structured, calculable formats, thus saving countless hours.

The next step is the algorithm. Chen and her team design their own algorithms that enable the system to identify which permutations will maximize tariff refunds. Many businesses, after submitting their cases to Pax for calculation, have been able to recover an additional 15% to 20% compared with the amounts previously determined through manual review.

The final step is actually submitting the information. After calculating the refundable amount using its algorithm, Pax has its in-house tax experts verify the results and then submits the documents directly to the government. Because Pax is authorized for U.S. submissions, the entire process eliminates the need for the traditional iterative submission process.
Under this model, processes that used to take six months to a year for companies can now often be completed in just 10–15 working days, resulting in a significant efficiency boost.

A Niche but Overlooked Market, Reimagined by AI

Tariff refunds have long been regarded as a fringe aspect of the trade system, but in reality, they are part of a system that has existed for over 200 years, involving the entire cross-border supply chain of import, processing, and export. Any company engaged in import and export activities may be eligible for refunds, meaning that the range of industries covered is far broader than generally imagined.

That’s why it’s a mature but fragmented market: even though more than a dozen service providers in the U.S. have been operating for years, the process is still highly dependent on manual labor, leaving a large volume of eligible refunds untouched for long periods—like forgotten “sleeping cash” waiting to be reawakened.

Pax tackled this problem by combining algorithms with experienced domain experts. Within just over a year of its founding, it was selected for Y Combinator, a leading Silicon Valley accelerator, received $4.5 million in early stage investment, and processed tariff refunds totaling around $10 million. Following Trump’s tariff announcement this year, demand from businesses surged, and Pax’s revenue tripled as a result.

With a team of fewer than 10 people at its founding, Pax achieved these results not only through the strength of its product but also thanks to favorable policy conditions. The evidence is clear: there is a huge and vastly undervalued market for “duty drawback,” and Pax is leading the way in addressing this gap.

Looking ahead, Penny Chen points out that the United States has adjusted the relevant laws and regulations numerous times. This system, which has existed since the founding of the nation, has been continuously revised over the past two centuries, becoming increasingly complicated as a result. “I think tariffs will only continue to increase,” Chen said. Amid supply chain restructuring and geopolitical tensions, it is difficult for companies to go back to the past, and the demand for tariff refunds is only likely to grow stronger.

In a rapidly changing world, “duty drawback” deserves to be better understood and more effectively utilized than ever before. Chen expects that through AI and automation, Pax can help businesses of all sizes transform what was once a cost burden into cash flow resilience, giving them greater control in the new economic landscape.

Taiwan-based TherapiAI, founded by a group of AI experts and medical specialists, has built an AI platform that helps biotech companies, pharmaceutical manufacturers, and CDMOs (Contract Development and Manufacturing Organizations) save raw materials and speed up production lines—allowing pharmaceutical production to run at ten times the speed, no longer dragged down by cumbersome traditional processes.

The structural bottlenecks that have accumulated in the biopharma industry over the years are clear: fragmented experimental data, slow R&D processes, shortages of specialized manpower, and complicated, time-consuming regulatory documentation cycles. TherapiAI aims to solve these four major pain points. They have built AI infrastructure focused on CDMO and CMC (Chemistry, Manufacturing, and Controls) needs, enabling faster R&D, optimized process parameters, and accelerated documentation and compliance workflows across the board.

Just as smartphones require an operating system to allow apps to work together, TherapiAI functions as an “AI operating platform” for pharmaceutical companies: connecting fragmented data, automating complex workflows, and transforming expert knowledge into reusable AI agents. Work that once relied on specific doctoral experiences to move forward can now be accelerated and scaled.

Crossing from the Courts into Pharmaceuticals: Building Foundational Cross-Domain Capability

Therapi AI, formerly known as Akousist, was founded by Michael Han in 2018. Interestingly, its early work had nothing to do with biopharma. “We previously handled AI-driven automation of electronic case files for 36 courts across Taiwan,” Han explained. Using AI to help court professionals automate large volumes of routine documents was the difficult challenge they were focused on at the time.

This seemingly unrelated experience turned out to be a key capability that allowed Therapi AI to enter the biopharmaceutical industry. The challenges faced by pharmaceutical companies are, in fact, very similar to those faced by the courts: data scattered across ERP, equipment, and laboratory systems; departments seeing different sets of information; and clinical and experimental documents containing sensitive information that cannot leave secure environments.

Through its work with the courts, the Akousist team developed a rigorous approach and deep expertise that enabled AI to “act as an agent” for professionals—performing routine tasks under strict data-security and interoperability constraints. These are precisely the foundational structures most lacking on pharmaceutical production lines, and thus became one of TherapiAI’s core strengths.

However, to understand why this AI infrastructure matters, we must first return to the question: what are the pain points of CDMOs?

The Four CDMO Pain Points: Data, Process, Manpower, and Regulations

First is the fundamental issue of data silos. On pharmaceutical manufacturing floors, critical experimental data is often scattered across instruments, paper records, and systems used by different departments. Formats are incompatible, and it is difficult to compare everything on a single platform.

Second, the high variability of cell and drug processes makes scale-up especially challenging. These processes are extremely sensitive to parameters and environments, where even tiny deviations can cause multi-million-dollar experiments to fail. While cells exist in relatively simple conditions in laboratory settings, once they enter large reactors, every parameter must be recalibrated. Pharmaceutical companies often rely on repeated trial and error and additional batches of raw materials to identify a stable process for production.

The third pain point is the manpower bottleneck created by heavy reliance on experts.

CDMO projects are complex and lengthy, taking an average of 18 months just to sign a contract. Much of the workload falls on a handful of senior VPs, BDs, and PMs. Take PMs as an example. CDMOs commonly face shortages of experienced PMs, high turnover rates, and overwhelming workloads. Even more challenging, key process know-how often sits only in the minds of PhDs, so when a core PhD leaves, the entire line has to be rebuilt from the ground up.

The final and most difficult pain point is data sensitivity and regulatory pressure.

In CDMO operations, much of the data is highly confidential and inherently unsuitable for the public cloud. A deeper issue is that “the whole industry shares a common misconception: that the first step to adopting AI is to centralize all the data,” Han explained. “But centralized data processing is slow, expensive, easily costing millions, and it may not even be effective.” As a result, although most pharmaceutical companies understand the potential benefits of AI, many prefer to maintain the status quo rather than take risks.

What Does TherapiAI Do? Building the AI Operating Platform for the Biopharmaceutical Industry

TherapiAI’s technical architecture consists of two core layers: the underlying “Knowledge Layer Model” and the front-end “AI agents.” At the knowledge layer, the team integrates publicly available global datasets with CDMO internal production data, enabling AI to genuinely understand highly specialized pharmaceutical knowledge. Such adjustments turn the models into a credible base for pharmaceutical companies and lay the groundwork for subsequent automation and application capabilities.

On top of this foundation, TherapiAI goes on to build AI agents that can “actually do the work.” These agents are not designed to serve a single function but rather to address the three interlocking stages of the pharmaceutical process: research, exploration, and exploitation.

First of all, in the research stage, AI must follow a “better none than wrong” principle. This working environment demands high precision and has nearly zero tolerance for error. If the model lacks sufficient supporting data, it will simply respond with “I don’t know,” avoiding incorrect inferences. This allows researchers to treat AI as a trustworthy information partner rather than a black box requiring constant verification.

Next is the exploration stage, during which researchers seek not just answers but AI-assisted reasoning. At this point, the AI agent uses its built-in knowledge and cross-system data to propose possible parameter ranges, hypothesis paths, or potential causes of anomalies, helping researchers shorten experimental iteration cycles. This marks the key transition from “looking up information” to “thinking together.”

Finally, in the exploitation stage, AI formally “enters the production line,” transforming research outcomes into operational workflows—such as performing cell-line screening; automatically generating contracts, process documents, and GMP reports; and querying FDA regulations—all directly addressing CDMO pain points.

For pharmaceutical companies, research directions converge faster, manufacturing can identify stable parameters with fewer raw materials, and regulatory teams can reduce document turnaround and revision cycles. Tasks that once required weeks of cross-checking by senior PMs or PhD-level personnel can now produce a preliminary version in minutes, ready for expert review.

Han notes that TherapiAI’s core is stripping away all non-domain noise from large language models, leaving only pharmaceutical-relevant knowledge. Combined with its three-stage agent workflow, the model not only “answers questions” but can “actually get work done” on R&D and production floors. That is why TherapiAI offers not just standalone tools but AI infrastructure that compresses the entire development cycle to a fraction of its previous length.

Practical Applications of AI Agents: Accelerating “Magic Bullet” ADC Drug Development and Tracking Regulatory Changes

TherapiAI’s AI agents are already being applied in a number of highly specialized domains. Among them, the most representative case involves the development of ADCs (Antibody-Drug Conjugates), a field that has drawn much attention in recent years. Because ADCs can precisely deliver payloads without damaging normal cells, they are regarded as a major breakthrough in cancer therapy and nicknamed “magic bullets,” with licensing deals often reaching billions of dollars.

In this trial-intensive, highly complex domain, TherapiAI has built an AI agent specifically for ADC development. After researchers pose questions in natural language, the system automatically integrates cross-system and cross-literature data—covering antibodies, linkers, payloads, and other core components—and organizes design factors that influence efficacy and toxicity. This enables teams to evaluate the feasibility of different strategies early on, without repeatedly cross-checking literature and databases.

More importantly, the ADC AI agent highlights parameters likely to require adjustment later, helping teams to quickly narrow down their direction. For pharmaceutical companies and CDMOs, this reduces the number of unnecessary experiment cycles, dramatically shortens the traditional 2–3 year early exploration phase, and enables earlier entry into development stages with commercial value.

In the highly sensitive regulatory domain, TherapiAI uses its GMP AI agent to challenge manual workflows and help companies stay current with global regulatory changes.

The system can instantly search, compare, and interpret international regulatory texts such as FDA and ICH guidelines, and automatically detect inconsistencies or gaps across department documents, ensuring alignment and preventing delays caused by version errors.

The GMP AI agent can also simulate questions likely to be raised by reviewers using its built-in risk-prediction model, marking high-risk sections on a “risk map” so documents undergo a round of pre-review before submission.

TherapiAI has already been deployed at multiple pharmaceutical companies and CDMOs across Taiwan, Japan, and other places, supporting use cases such as early ADC design, cell-line screening, automated process documentation, and GMP regulatory comparison. Some customers have integrated AI agents directly into their production workflows for cross-department collaboration and document generation; others, after adopting the system, have proactively requested new features, hoping to shift more critical process steps from manual work to AI automation. These collaborations have allowed TherapiAI to evolve into an essential operating layer in the pharmaceutical production chain.

Are You Selling a “Solution” or a “Tool”?

Many deeptech start-ups encounter the same early-stage blind spot: strong technology does not automatically translate into perceived value, and having many tools does not mean that customers want to assemble them themselves. TherapiAI faced the same issue.

“We started with the technology, expecting customers to operate everything on their own,” Han said. However, most pharmaceutical teams are overwhelmed with work. They simply don’t have the time, and therefore won’t pay for “tools.” Whether a tool is powerful or not is a separate issue from whether it solves real on-site problems.

The team later realized that “experts don’t need a powerful tool, yet they need their pain point solved directly.” Han explained, “Within two weeks, we pulled back all the separate tools, stopped selling technology, and transformed it into AI agents that could directly complete work.” For example, their previously standalone OCR module was integrated into a GMP document AI agent. Once the “tool” became an “agent” capable of delivering outcomes, its value became immediately clear: customers were willing to pay and more willing to adopt.

Customers do not want separate tools; they want foundational capabilities that truly integrate R&D, manufacturing, documentation, and regulatory workflows. TherapiAI is not merely creating tools to solve individual problems; it is building an AI operating platform capable of reshaping the entire biopharma sector.

Not every AI revolution begins in Silicon Valley. TherapiAI, a team from Taiwan, offers a glimpse of another future possibility: the next major breakthrough will not depend on geographical coordinates but on who can bring AI into the world’s most complex and critical operational sites.

“Engineers often have to spend over 100 hours going through as many as 2,000 pages of datasheets, just to verify that the hardware components are compatible,” said Niyam AI co-founder and CEO Samarth Shyam. “In this industry, every day of delay can cost hundreds of thousands of dollars, and for brands, a month’s delay in launching a product can result in a loss of 20% to 30% of their lifecycle profits. Across a portfolio, those slips compound to millions, if not billions.”

The electronics manufacturing industry is the backbone of global innovation, yet it has long been hampered by inefficient design tools. The first-pass success rate of prototyping is less than 1%, and engineers not only have to check the compatibility of components one by one but also deal with downstream  issues such as parts obsolescence and supply chain instability.

An even bigger problem is that electronic design involves multiple teams but lacks a unified platform to integrate the entire process, resulting in many problems only discovered after the prototype has been completed. Existing tools lack real-time feedback and proactive analysis, leaving companies often forced to “put out fires” only after mistakes have been made.

And this is the very pain point that Niyam AI is trying to solve.

How can two atypical hardware professionals disrupt the electronics manufacturing industry?

The two founders of Niyam AI are not typical “hardware folks,” but they have recognized the industry’s long-standing pain points.

CEO Samarth Shyam is a serial entrepreneur and angel investor with a track record of shipping and exits and plenty of battle scars; his previous company sold tokenized loyalty platforms to enterprises and was acquired. CTO Agrim Singh was Citibank’s first Hacker in Residence and built market analytics that surfaced price-moving events minutes before Bloomberg or Reuters.

The two met in the Entrepreneur First program in Singapore and have since worked on a number of projects, but the real turning point came after watching a founder friend nearly lose his startup because of a single wrong MCU choice. It was an upstream failure. Kickstarter is a graveyard of similar stories where tiny part data mistakes snowball into missed launches.They found that hardware prototypes often required multiple revisions due to component compatibility or specification errors, and engineers lacked tools that could “proactively” identify problems and were forced to make revisions only after the errors had occurred. This was a pure data (ETL) problem and they knew it!

This led them to the decision to found Niyam AI, which catches component risks early by surfacing compliance, sourcing, and design errors before they turn into costly surprises. The duo combines commercial GTM firepower with deep AI execution, the mix needed to win a fast moving, niche technical market.

“The AI spellcheck for the World of Hardware” makes design ten times faster! What does Niyam AI do?

“Niyam is the AI spellcheck for the world of hardware.” Shyam explained that engineers, who used to spend hundreds of hours comparing data and copying specs out of PDFs, now have Niyam sitting inside ECAD and PLM. The agent pulls design files, BOMs, and linked datasheets automatically, then parses and checks them in minutes.

Shyam further explained that at the beginning of the process, the agent extracts and normalizes specs from each datasheet and design file, builds structured part data, and runs instant compatibility checks. It then looks for risks like insufficient inventory, long lead times, and expiring certifications.

All test results are consolidated into a single dashboard, where engineers can simply click on the suggested options and have an updated bill of materials automatically generated and imported back into the existing Product Lifecycle Management (PLM) system, with little or no change to their work habits.

On the supply-chain side, Niyam has significantly reduced the cost of manual cross-checking. On the supply chain side, the agent removes most manual cross checking. In the past, teams entered part numbers across thousands of suppliers, verified inventory and certifications, and picked replacements by hand.

Now, Niyam’s agentic AI will start from the bill of materials and proactively provide “contextualized recommendations”not only telling you “what parts are available” but also filtering out solutions that “really work,” based on criteria. This proactive, real-time feedback makes design over ten times faster, revolutionizing the reactive, time-consuming model of the past.

How can Niyam AI enter the EDA market dominated by giants?

Over the past 30 years, the EDA market has been monopolized by giants such as Synopsys, Cadence, and Siemens, with tools primarily focused on design. Although they do have verification features, these are limited to basic checks. As hardware design becomes more complex, traditional tools struggle to perform cross-component analysis at an early stage and lack data integration with the supply chain. EDA vendors make money from simulation seats, and PLM vendors make money from change-order churn, so proactive upstream verification has not been a priority.

In the wave of AI, many startups such as Flux, Celus, and JITX have tried to enter the market, but most focus on design efficiency but they leave the core problem untouched: part data still moves by hand and compatibility gets verified late. 

Niyam, on the other hand, targets the upstream failure. Their agent sits inside the ECAD and PLM, pulls schematics, BOMs, and linked datasheets automatically, then runs continuous risk checks for compatibility, lifecycle, sourcing, and compliance. No uploads, no one-off scans, just background guardrails that surface issues early and propose fixes. Compressing what would otherwise take hundreds of hours into minutes.

This positioning has allowed Niyam to quickly establish an advantage: on one hand, it has accumulated expertise in component lifecycle, supply-chain dynamics, and design rules; on the other, it has secured validation from major EMS’s and ODMs within a year of its founding, continuously strengthening its data, processes, and trust in real-world scenarios to build a high barrier to entry.

“In enterprise SaaS, one of the hardest things is figuring out who the real users are, and where in the value chain you fit in”

In November 2024, the team rebuilt the product to run as an agent inside the customer’s environment. 

Upload-based workflows hit a wall in practice. “A BOM or schematic can have over 300 permutations in the lifespan of a product. No one is going to upload a new file each time. If you rely on uploads, most risks stay invisible,” Shyam said. Rather than trying to change habits that are decades old, the team made the workflow agentic. “You meet engineers where they work. The agent watches ECAD and PLM for changes, pulls the right datasheets, runs the checks, and proposes fixes automatically,” he added.

Adoption improved dramatically once those extra steps disappeared. Early versions asked teams to upload files to a dashboard, which worked once in a while, not every day. The new flow plugs directly into ECAD and PLM and serves results in place. The system still supports Excel and CSV for teams that need them, but the default is no manual uploads.

The team also learned who the real day-to-day users are. “At first we thought the daily users would be design engineers,” said Shyam. “In practice, librarians, component engineering, PLM, sourcing, and compliance live in these tools. They care most about accuracy and traceability. If it takes hours, they will still choose correctness.” Time pressure sits with downstream brands and program owners. “For an international brand like Apple, a delay can miss a market window. A month can cut lifecycle profit by 20 to 30 percent,” Shyam said.

Those lessons shaped the product. Meet the bar for accuracy that front line teams demand, run inside their systems so adoption is painless, and deliver time savings leadership can feel. “That is how Niyam becomes part of the supply chain instead of another dashboard people open once and forget,” Shyam said.

Niyam: Creating a “New Order” for the Manufacturing Industry

After a run of pilots and validations, Niyam drew interest from major EMS and ODM partners and was selected for the Wistron Accelerator in 2025. The program let the team test and iterate in real factory conditions and signaled meaningful validation from industry leaders. That selection led to a full sitewide pilot with Wistron, one of the largest manufacturers in the world, and pilots in the pipeline with three of the ten biggest manufacturers globally.

Rather than just catching errors, Niyam reshapes the engineer’s workflow. The agent sits inside ECAD and PLM, surfaces risks early, proposes fixes, and writes clean revisions back into the system of record. “In this industry, even a two to three percent improvement in efficiency can translate into billions of dollars in revenue,” said Shyam.

For Shyam, this is the starting line. The vision is clear: in five to seven years, Niyam will be the agentic AI backbone of the two trillion dollar electronics supply chain. “Every tier 1 EMS, ODM, and OEM runs designs through us, from datasheets and BOMs to alternates, certifications, even placement. What used to be manual, siloed, and error prone becomes self healing and automated. Hardware finally iterates as fast as software, and if you are building electronics anywhere in the world, Niyam is quietly in the background making sure nothing slips,” said Shyam. 

The name Niyam comes from Sanskrit for rules and a way of life, and that is the aim: turn the rules that keep hardware safe into everyday practice inside ECAD and PLM so discipline becomes the default across the supply chain.

Such scenes are daily occurrences in Japan’s convenience stores, restaurants, train stations, and other public places. Each year, as many as 36 million international tourists visit the country. The language barrier is not just a minor inconvenience during travel; it’s gradually become a major pain point in the high-pressure travel industry—affecting not only the tourist experience but also Japan’s broader global competitiveness

This pain point is precisely what the Japanese startup Shisa.ai is determined to solve. Founder Jia Shen and his core team of three people are using AI to change the way people communicate. Together, they’ve created a real-time voice translation product, Chotto.chat. Users simply open their phones and speak naturally in their native language.

Whether they’re in a drugstore, a restaurant, or a taxi, Chotto instantly translates their speech into fluent Japanese, ensuring the conversation feels natural and emotionally resonant. “Speech is better than text at conveying human emotions and tone. Only when communication sounds natural will people feel comfortable speaking up,” says Jia Shen.

Shisa.ai’s large self-trained Japanese language model, Shisa V2-405B, supports Chotto behind the scenes. Boasting an impressive 405 billion parameters, the model has been released under a community license, making it available for research and noncommercial use, and has become one of the best-performing Japanese language models currently available.

According to the team’s evaluation results, Shisa V2-405B demonstrates capabilities comparable to OpenAI’s GPT-4o and China’s DeepSeek-V3 across multiple Japanese language tasks—including instruction understanding, role-playing conversations, Japanese-English translation, semantic reasoning, and text generation.

Surprisingly, this achievement, which rivals those of tech giants, was created by a “nano team” of just three people, and within one month of the model’s launch, it accumulated over a million downloads on Hugging Face, attracting global attention.

Why Does Japan Need Its Own Large Language Model?

For Jia Shen, these efforts seek to address a structural challenge spanning three dimensions: culture, economy, and national security.

But it all begins with the language itself. Japanese relies heavily on “context.” Tone, relationships, and social cues carry various “subtexts”— implied meanings that Japanese people do not express directly. Jia Shen provides an example: “In Japan, refusals are often not explicitly stated. For instance, when the someone says, ‘That day is not very convenient,’ it sounds polite, but it’s actually a clear rejection.”

Similarly, a phrase like “Aishiteru” has no clear subject or object. Depending on the context, it could mean “I love you,” “I love her,” or even “She loves him.” If large language models cannot understand the broader context, they cannot correctly infer meaning, and this linguistic characteristic poses a significant challenge for AI.

It also makes cross-cultural communication difficult—especially in service industry settings.
In April 2025 alone, Japan welcomed 3.9 million tourists, setting a new monthly record. However, the retail and restaurant industries face unprecedented labor shortages, and the language barrier is causing Japan to miss out on numerous business opportunities.

Looking further ahead, generative AI is rapidly evolving from a business tool into a strategic geopolitical asset. If the United States or China were to restrict the export of GPT models, countries that heavily depend on foundation models developed overseas may face risks they cannot independently address. In this context, having a proprietary model that can understand local language and culture is not just about improving efficiency—it’s about digital sovereignty and national resilience.

Shisa.ai have decided to release their models under a community license, allowing them to be used for research and non-commercial purposes. They believe that language models should not be monopolized by a few tech giants but should serve as basic infrastructure that everyone can share. This is not just a technological breakthrough, but also a declaration of intent: to give more developers and businesses committed to local AI development the opportunity to participate in and strengthen AI that truly understands the Japanese language and culture.

Overcoming Cultural Barriers Through “Voice”

Shisa.ai’s first product is a real-time voice translation system that starts with “voice”—Chotto.chat.

“I’m sure you’ve noticed by now that I speak very quickly!” Jia Shen says with a laugh. He chose to begin with real-time voice translation, creating a system that could keep up with speaking speed and adapt to the natural rhythm of conversation. “Chotto.chat is designed for people like me who speak quickly and need real-time communication.”

Unlike general translation tools, Chotto.chat is specifically optimized for Japanese speaking habits. Whether you speak rapidly, habitually lower your voice in public, or speak softly into your phone, it can recognize and respond naturally in real time, ensuring that conversations aren’t hindered by technology.

“We don’t just do simple translation; we make AI understand tone and context so that it can speak naturally and with emotion like a real person,” Jia Shen explained. “It’s like casting voice actors for a drama. It’s not just about reciting the lines correctly, but about speaking with emotion and depth.”

Although Chotto.chat has not yet officially launched its app, it already attracts over 6,000 users daily, with an average session time of 27 minutes per person. It’s not only useful for shopping and ordering food; it can also be used between couples and friends from different cultures, allowing them to communicate naturally in their native languages.

From Restaurants to Stations: Shisa.ai Becomes the “Invisible Translator” in Business Settings

Beyond everyday conversations, Shisa.ai’s voice technology has quietly entered physical spaces in Japan, becoming an important assistant for welcoming foreign customers.

In restaurants, Shisa.ai serves as a language assistance tool for staff, helping them respond to foreign customers’ inquiries about allergens and supporting new employees as they learn on the job. In retail stores, Shisa.ai handles common issues such as tax exemption rules, size conversions, and return/exchange policies, allowing frontline staff to respond smoothly without interrupting their work.

This kind of real-time support also extends to public spaces. Inside Yokohama Station, Shisa.ai has installed AI kiosks supporting 17 languages, allowing travelers to inquire in real time about restrooms, tickets, and store locations. These kiosks act as “talking information boards,” bridging language and information gaps.

Next, Shisa.ai is planning to extend its voice applications by creating virtual pets that combine chat, reminder, and companionship functions—offering a range of future solutions for Japan’s aging society.

Giving Every Culture the Right to Be Heard

Shisa.ai was founded by three immigrants who chose to build in Japan. They believe AI sovereignty must start with local language and culture—not just to preserve diversity, but to ensure data privacy and national resilience. 

CEO Jia Shen co-founded the company with CTO Leonard Lin, who led the development of the Shisa model. Their third team member, Adam Lensenmayer, is a well-known anime translator whose credits include Attack on Titan, Chibi Maruko-chan, Space Brothers, Detective Conan (Movie) and Kaiju No. 8. His attention to tone and linguistic nuance helped the model align more deeply with the subtleties of Japanese communication.

For Jia Shen, the core of Shisa.ai isn’t just about creating “the most powerful AI,” but rather about answering a deeper question: can technology bridge gaps between us and our families, and between us and our culture?

This idea was born out of deep concern for his family.

“My father passed away ten years ago, and my daughter is five years old, so she never met her grandfather,” explains Jia Shen. “But through language models, I can preserve his voice, tone, and life philosophy. In the future, my daughter might be able to talk with her grandfather and listen to the family jokes that have been passed down.”

It’s not just about the preservation of memories but the continuation of culture, or as he calls it, “cultural capture”: recording the voices and emotions that don’t automatically remain on the internet. These include elders’ daily conversations, local dialects, the tonal shifts Gen Z uses on dates, and the cultural memories quietly flowing among groups who don’t go online or post on social media. These subtle yet profound words, if unheard or unpreserved, could vanish in an instant.

Shisa.ai also plans to reach more users and extend voice translation to physical spaces so that everyday environments like train stations, shopping malls, and restaurants can provide “someone who understands what you’re saying.”

After all, the most moving technology isn’t always the most conspicuous. It exists in the moment when a sentence is understood or an emotion is comprehended, quietly exerting its power—and bringing people a little closer together.

This isn’t alarmist rhetoric; it’s an insight drawn from Liu’s firsthand experience at the forefront of Silicon Valley’s tech industry. A veteran of the technology and media worlds, Liu knows exactly how data is stored, transmitted, and exchanged. He has led product experiments at Spotify, served as CEO of the once-popular social platform Digg, and held roles at Google and AOL.

In 2016, Liu made a dramatic shift to become CEO of the South China Morning Post, where he spearheaded the century-old newspaper’s digital transformation. These experiences revealed a troubling truth: while users generate massive amounts of data, they have almost no control over where it goes or how it’s used.That realization sparked the idea for Terminal 3. In just one year, this Hong Kong–based startup has enabled more than 10 million users to store and manage their personal data through its enterprise services. It recently completed an $8 million seed funding round backed by several world-class investment firms.

What exactly does Terminal 3 do?

At the core of Terminal 3’s architecture lies a platform called “Oracle.” But this Oracle isn’t the same as the blockchain oracle used to bridge on-chain and off-chain data. Instead, it’s a decentralized data hub where users retain ownership of their data while still being able to provide trusted authentication to businesses when needed.

Liu explained that Oracle’s key functions include helping users securely store data in a decentralized manner, enabling encrypted computation and processing through privacy-enhancing technologies (PETs), and giving users control over how applications query and use their data—all without ever exposing the original information. This allows organizations to verify identities, ensure compliance, and even conduct advanced data analysis without accessing personal data.

Terminal 3’s foundation is built on blockchain, while its data processing core relies on PETs such as Zero-Knowledge Proofs (ZKP), Multi-Party Computation (MPC), and Trusted Execution Environments (TEE). These technologies enable enterprises using Terminal 3 to perform identity verification and compliance checks without ever holding users’ data themselves. This approach not only greatly reduces the risk of data breaches and compliance costs, but also makes data access more secure and efficient.

Sounds complicated? “What we’re doing is enabling businesses to legally and securely verify information without actually owning the data,” Liu explained. “For example, a bar only needs to know if you’re over 21; it doesn’t need your full birthdate. That used to be very difficult, but with Terminal 3, organizations can now achieve it through zero-knowledge verification—ensuring both compliance and privacy protection.”

“Or consider banks,” he continued. “If they want to verify whether someone is a qualified investor, they typically have to request account details and proof of assets—an approach that’s cumbersome, time-consuming, and labor-intensive. 

But with Terminal 3, financial institutions can receive a verified certificate that simply confirms ‘this person is eligible,’ without ever accessing sensitive data.” This, he added, has major applications in the financial sector for KYC and anti–money laundering (AML) compliance.

Finally, Liu described a potential cross-border use case: “When you travel abroad, you carry your passport not just for the airport, but also for hotel check-ins, tax refunds, and even boarding trains. Now we can convert passport information into an anonymous, verifiable certificate, store it securely on your phone, and present it via a QR code to prove your identity—without handing over a stack of personal data. These are the kinds of solutions companies can achieve by adopting Terminal 3.”

The Rise of Data Sovereignty

This shift toward data sovereignty didn’t happen overnight. In recent years, decentralized technologies have steadily moved into the mainstream, and more businesses and governments are beginning to see blockchain as a critical piece of modern infrastructure. People are increasingly recognizing that data shouldn’t be centrally controlled; it should be owned and authorized by individuals themselves.

Regulations have also emerged as a major driving force behind this trend. From the European Union to the United States and across many Asian countries, governments are tightening personal data and privacy protection policies. Companies are now required to implement clear authorization and traceability mechanisms across data collection, storage, and access. For businesses, this is no longer just an ethical choice—it’s a legal mandate.

Meanwhile, user expectations of digital experiences are shifting. In the past, a service was considered satisfactory if you could simply log in without issues. Today, people expect a cross-platform, seamlessly integrated identity system: Can an identity created on one platform be trusted on another? This demand is placing new pressures on the core infrastructure of enterprises.

Finally, perspectives on national security are evolving. More governments now recognize that data lies at the heart of digital sovereignty—and that controlling it is essential for safeguarding citizens’ information. Terminal 3 isn’t just an enterprise solution; it’s a direct response to the urgent global need for data control and trust.

A New Data Governance Infrastructure

To enable enterprises to handle authentication, authorization, and governance without directly accessing user data, Terminal 3 has divided its technical capabilities into three product lines—Identity, Verify, and Agent Auth—each designed for a specific data application scenario.

Identity is a decentralized identity system that helps organizations manage user accounts, registrations, single sign-on (SSO), and data permissions. It also features a built-in lightweight CRM for user segmentation and communication.

Unlike traditional IAM (Identity and Access Management) systems like Okta or Ping, which store data centrally on enterprise servers, Terminal 3 uses a decentralized framework in which user data is held by individuals and authorized through encrypted certificates. This approach allows enterprises to complete identity verification and compliance checks without accessing raw data, reducing both security and regulatory risks.

Additionally, Terminal 3 integrates zero-knowledge encryption with decentralized storage to offer enhanced security, seamless cross-platform interoperability, and high flexibility—positioning it not just as a replacement for conventional IAM systems, but as the foundation for next-generation identity governance.

Verify expands the possibilities for identity applications. Enterprises can use this module to verify a variety of conditions—such as membership status, specific qualifications, or even ownership of digital assets—all through zero-knowledge verification. This is particularly attractive to government agencies and highly regulated industries like finance.

Agent Auth is Terminal 3’s solution for the AI era. As AI agents increasingly take on tasks traditionally handled by humans—like searching, shopping, and signing contracts—both businesses and users need new ways to confirm these actions are properly authorized.

“In the future, we won’t be physically present in every digital space, but identity and authorization will still need to be verified,” Liu explained. Agent Auth lets users explicitly authorize AI to access personal data and execute transactions on their behalf through secure mechanisms that transmit information, such as payment details, directly to the platform instead of leaving it to the AI. This ensures every action happens under controlled, traceable authorization.

$8M Seed Round Secured

Terminal 3’s three product lines are strategically aligned with distinct target audiences: T3 Identity serves Web3-native teams, T3 Verify targets governments and highly regulated sectors like finance, and Agent Auth supports AI ecosystems and platforms that require agent behavior verification. This clear market segmentation not only sharpens Terminal 3’s focus but also speeds alignment with real-world needs.

Yet realizing the vision of “verification without data” has proven far more challenging than it sounds. Liu admits the team had to build an almost unprecedented infrastructure entirely from scratch, spending 18 months refining the technology and educating the market to gradually earn trust. The journey was demanding, but as the product matured and enterprise users came on board, new opportunities began to emerge.

Since its launch just over a year ago, Terminal 3 has formed partnerships with multiple blockchain platforms, Web3 companies, and enterprises, collectively serving more than 10 million users. Backed by leading investors—including Illuminate Financial, CMCC Global (Titan Fund), Animoca Brands, IDG Blockchain, 500 Global, and Cherubic Ventures—the team has laid a solid foundation for future expansion.

Creating a Foundation of Trust for a New Era

“What’s not going to change in the next 10 years?” Amazon founder Jeff Bezos once reminded entrepreneurs that the best bets are on essential needs that stay constant, no matter how the world evolves.

For Liu, these “unchanging truths” are clear: data must be verified, identities must be validated, and compliance must be enforced. That’s precisely what Terminal 3 is building—a trustworthy foundation for data processing using the most rigorous technologies available.

When asked about his long-term vision, Liu expressed admiration for three largely unseen yet essential infrastructure companies: Stripe, Plaid, and Twilio. While consumers rarely think about them, these companies power nearly every online payment and video call.

“I want Terminal 3 to be like these companies—quietly upholding the foundation of trust in the data world,” he said. “Whenever an application needs to access user data—whether for age verification, KYC, or identity checks—we’re there behind the scenes to ensure privacy and legitimacy.” It may not be a name that consumers mention often, but it has the potential to become one of the most trusted infrastructures of our time.

This article has been contributed to Asia Tech Daily.

‘’Tesla, SpaceX, and Palantir were unnatural. They went against the grain. Electric cars weren’t and wouldn’t have been a thing. Innovation in space travel wasn’t and had no reason to be a thing. ‘’   —Jonathan Lonsdale, Unnatural Progress

Ender, a real estate tech start-up, is one such entity.

Before founding Ender, Jonathan Lonsdale spent years in venture capital, advising and investing in entrepreneurs. He often described his investor career as a “cheerleader on the sidelines,” supporting innovation but not leading it.

That changed unexpectedly.

One evening, as he prepared for hosting an event at his loft, a broken air conditioner flooded his New York City apartment. In his attempt to reach property management, he encountered operations that were fragmented, outdated, and highly inefficient.

The ensuing repairs and maintenance took months. He discovered the problem itself was preventable with maintenance. Standing in his water-damaged living room, Lonsdale realized: “Why is it so hard to solve such a basic problem even on the high-end? Shouldn’t we have an automated system that works seamlessly for everyone?”

That’s where Ender started.

Rebuilding Real Estate Management from the Ground Up

Ender is not addressing a single pain point; it is tackling the systemic inefficiencies that have long plagued the real estate management industry.

Unlike most startups, Ender did not pursue a rapid minimum viable product (MVP) launch.

“Our MVP,” Lonsdale says, “was building a full-scale, enterprise-grade ERP system from scratch.”

While that ambition might sound improbable, it was intentional. Lonsdale spent more than five years leading a team to reengineer the real estate management stack—designing a modern, fully integrated platform aimed specifically at U.S. institutional investors in the single-family rental (SFR) market.

Ender’s platform connects the full operational lifecycle: property appraisal, investment review, renovation and inspections, applications and leasing, move-in / move-out, rent collection, renewals, arrears management, work orders, task management, communication, accounting, and financial reporting—all within a single system.

Rather than serving as a point solution, Ender positions itself as a foundational operating system for institutional landlords, seeking to become the core infrastructure powering large-scale property portfolios.

Technically categorized as a Property Management System (PMS), Ender’s architecture departs sharply from legacy models—rebuilding the category around modern data structures and true process integration.

PMS: A Broken Core Hidden in Plain Sight

At the heart of real estate management lies the Property Management System (PMS)—the platform intended to handle everything from leasing workflows to rent collection, maintenance dispatch, accounting, and financial reporting.

In theory, PMS should be the industry’s most trusted operational backbone. In practice, it is often its greatest liability.

Through deep industry research, Lonsdale identified the root cause: poor data quality.

When daily operations, reporting, and analytics are built on fragmented or inaccurate data, inefficiencies compound—wasting resources and undermining decision-making at scale.

Adding to the problem, traditional PMS solutions tend to address issues in isolation.

Legacy PMSs evolved by stacking specialized modules—leasing, payments, work orders, accounting—on top of one another, often developed by different teams or acquired through M&A. The result is a “Frankenstein” architecture: systems that appear integrated on the surface but remain deeply fragmented underneath.

Ender’s strategy is to eliminate these structural flaws at the root level—offering a truly unified system where data integrity and operational workflows are seamlessly connected from end to end.

Small Landlords vs Institutional Operators: The Two Worlds of PMS

These problems didn’t just happen today, and Ender is certainly not the first team to try to disrupt the PMS space. Looking at the market, current PMS service providers can be broadly categorized into two groups:

The first group serves small and mid-sized landlords—often referred to as “mom-and-pop” owners—through platforms like AppFolio and RentRedi. These property managers typically service between one and hundreds of units, handling tenant sourcing, rent collection, and maintenance directly.

The second group focuses on institutional landlords: corporate owners such as real estate investment trusts (REITs), private equity firms, insurance companies, and large asset managers. Managing portfolios that have thousands of properties, these operators rely on dedicated property management teams—and increasingly, they are expanding into the fast-growing asset class of Single-Family Rentals (SFR).

Traditionally, single-family homes—standalone houses—were owned and managed by individuals or families. However, following the 2008 financial crisis, institutional investors began entering the market at scale.

According to Nine Four Ventures, as of 2022, approximately 700,000 SFR units in the U.S. were institutionally owned—a figure projected to rise to 7 million by 2030.

The Gap Legacy Providers Can’t Bridge

Institutional ownership has brought a new level of operational complexity to the sector. Managing geographically dispersed assets at scale demands systems with full functionality, deep integration, and flexibility—requirements that traditional PMS platforms, initially built for simpler portfolios, struggle to meet.

Institutional-focused PMS providers—such as Yardi, RealPage, MRI Software, and Entrata—are established names in the space. However, most were founded in the 1980s or early 2000s, and their legacy architectures remain deeply entrenched. Modernizing these platforms would require costly, near-impossible rewrites of core systems—an undertaking few incumbents are willing to pursue.

As a result, no player has fully committed to Ender’s approach: Building a new system from the ground up, designed specifically for institutional SFR operations, with truly integrated workflows and scalable infrastructure—serving a high-growth segment that remains significantly underserved.

Building from Scratch to Solve the Industry’s Deepest Pain Points

“The biggest pain points—and the greatest opportunities for innovation—are on the institutional side,” says Lonsdale.

While Ender did not initially target institutional landlords, the team quickly pivoted after early product launches attracted significant inbound interest from institutional groups. This shift was a natural extension of the team’s DNA: Ender’s core members hail from major U.S. technology companies, bringing expertise in building highly stable, scalable systems with a focus on automation and data integration—foundations critical to developing an enterprise-grade ERP.

In its early days, the team even took on property management duties themselves to experience operational pain points firsthand. Managing small and mid-sized portfolios briefly, they soon realized that their real strength lays in addressing the complex, highly customized demands of institutional owners.

That technical depth has translated into measurable impact.

Clients report that by adopting Ender’s rent arrears management module, labor demands for collections have dropped by as much as 80%, freeing operational teams to redeploy resources toward strategic initiatives.

These gains are not the result of isolated feature improvements, but of Ender’s deeply integrated system architecture—designed to streamline processes end-to-end.

This depth of product and systems thinking has drawn the attention of many investors to its potential. Venture capitalists such as Global Founders Capital, Tuesday Capital, LeFrak, HOF Capital, Circle Ventures, and Cherubic Ventures have invested early on to support the company’s efforts to drive innovation from the architectural level in a market that is massive yet digitally underserved.

Ender’s Next Frontier: AI-Powered Automation

With a modern ERP foundation and clean operational data, Ender is now positioned to lead the next wave of disruption in real estate: AI-driven workflow automation.

Unlike legacy platforms retrofitting AI onto outdated systems, Ender’s architecture allows for seamless integration of intelligent agents. In traditional billing, for example, invoices pass through OCR translation, manual classification, and human review.

Ender’s AI systems can automate classification midstream, leaving only the final approval to human teams—dramatically accelerating processing speed.

In leasing operations, AI tools can pre-screen applicants against predefined criteria, freeing staff from repetitive, rules-based tasks and enabling them to focus on high-value decisions.

In 2025, Ender plans to roll out a suite of AI-powered automation tools focused on arrears management, financial reporting, and other high-friction operational processes—efforts that Lonsdale believes could meaningfully transform not just property management operations, but the broader real estate industry itself.

A Flood Sparks the Restructuring of an Entire Industry

Lonsdale believes the property management industry is on the cusp of a profound AI-driven transformation over the next five to ten years—one that will fundamentally rewrite labor-intensive workflows.

He envisions a future where, if something breaks in a home, there will be no need for tenants to file reports or make calls. An AI agent will autonomously detect anomalies, engage vendors, verify insurance coverage, schedule repairs, and eventually dispatch robots to execute on-site fixes. Every step will be recorded, audited, and optimized—without requiring human intervention.

“The operational workflows of the future won’t need the manpower they do today,” Lonsdale says.

“You’ll likely need only one-fifth, or even one-tenth, of the current workforce. Operations will be more efficient, more compliant, less error-prone—and deliver a far better experience.”

From a minor apartment flood to a sweeping industry vision, Ender isn’t just building a product. It is attempting to redefine what property management should look like in the era of intelligent automation.

This article has been contributed to Asia Tech Daily.

Like many avid gamers in Taiwan, Linmic was no stranger to spending heavily on digital content. But unlike most, his curiosity about the price discrepancy led him deep into the world of payments and taxes.

What he uncovered was the impact of the hefty 30% commission fees charged by Apple and Google—a discovery that would eventually lead him to found Tokenz. As the first startup in Asia to fully adopt the Merchant of Record (MoR) model, Tokenz helps developers bypass platform fees, retain more of their earnings, and seamlessly manage the complex web of tax compliance and regulations.

The 30% Apple Fee and the Hidden Costs of Going Indie

For years, the 30% cut taken by Apple and Google has been a thorn in the side of game studios and app developers. To avoid it, many developers set up independent web stores, directing users to make purchases outside of app marketplaces. While this strategy helps reduce platform costs, it also exposes developers to a new world of complexity—one filled with risks related to cross-border payments, tax compliance, and legal regulations. Failure to handle these properly can result in hefty fines or legal trouble.

Laying the Infrastructure for Asian Digital Creators—Backed by ¥200 Million in Seed Funding

This is precisely where Tokenz steps in: as the infrastructure provider for developers charting their own course. Once creators choose to bypass traditional platforms, Tokenz becomes the road beneath their wheels.

Fast forward to 2025, and Tokenz has secured ¥200 million (approximately NT$60 million) in seed funding. The round was led by Japan’s Coral Capital, with participation from Silicon Valley giant NEA, Japan’s Global Brains, Plug and Play Japan, and Cherubic Ventures.

Tokenz now supports a wide range of digital content providers—from gaming and anime to e-books, livestreaming, and SaaS—helping them streamline cross-border payments and tax processes.

“For developers in Asia, the biggest challenge in going global isn’t building great products—it’s the invisible wall of financial and legal complexities behind the scenes,” Linmic explains. 

Tokenz was built to remove these barriers, handling the back-office headaches so creators can stay focused on what they do best—building engaging games, software, online courses, or content platforms.

With Tokenz’s one-stop solution, the cost and complexity of entering overseas markets drops dramatically—opening up new revenue streams for creators across Asia.

A New Revenue Pipeline for Creators

In the mobile gaming industry alone, over 70% of the world’s top 20 publishers have established their own direct-to-consumer web stores. Data shows that when managed well, these stores can drive 20–30% revenue growth. Tokenz empowers more developers to follow this path, unlocking global markets with ease and helping replicate the success of major players.

What Is a MoR (Merchant of Record), and Why Has Asia Been Left Behind?

“To give you an example,” Linmic explains, “when you buy an eBook on Amazon, you might assume Amazon is selling it directly to you. But in many cases, it’s a MoR company like us handling the transaction. We purchase the item under our own name, then resell it to you. That’s the only legal way we can collect payments and handle taxes across different regions.”

A Merchant of Record, or MoR, is a legally recognized entity that acts as the official seller in a transaction. MoR providers are authorized to operate as resellers on behalf of digital product creators, taking on responsibilities such as payment processing, fund transfers and refunds, fraud prevention, risk management, and global tax compliance.

This becomes especially critical when selling digital products internationally. Without a MoR partner, developers face the daunting challenge of navigating each country’s unique tax laws. From calculating precise sales tax on every transaction to knowing which jurisdiction the revenue falls under—and registering accordingly in each market—international digital sales can quickly turn into a legal and logistical nightmare.

By working with a MoR provider, developers can offload these burdens and focus on creating great products, while the MoR handles the operational and regulatory complexities behind the scenes.

What many overlook is that Apple’s App Store and Google Play are themselves functioning as MoRs, quietly managing cross-border taxes, currency exchange, and payments for developers. This is a key reason why the system feels deceptively seamless—but also why, when developers seek to build their own web-based stores, they quickly realize how essential MoR infrastructure is to stay compliant and competitive.

With antitrust laws gaining traction around the world, it’s more important than ever for developers to build outside the app store ecosystem. But doing so without a MoR solution in place can lead to regulatory risks that outweigh the benefits.

Why Tokenz Isn’t Just Another MoR—And How It Differs from the Big Four

In the West, the MoR model is nothing new. In the U.S., the complexity of varying state tax laws has long mirrored the challenges of international sales. Companies like Paddle have emerged to serve this need, building robust MoR solutions for SaaS businesses and operating successfully for years.

But Asia is a different story. The region’s diversity of tax regimes, language barriers, and cultural nuances—combined with a general lack of awareness around cross-border compliance—have made the MoR model almost nonexistent locally. Especially in digital content sectors like comics, eBooks, livestreaming, and virtual goods, regional MoR solutions are still practically unheard of. While some services exist for physical goods, their models don’t translate well to the digital realm.

“In the past, companies looking to expand overseas would immediately turn to the Big Four accounting firms,” says Linmic. “But they can only help you file taxes—they can’t help you sell, collect payments, process refunds, or manage risk.”

The Big Four offer consultancy services. They help ensure regulatory processes are followed, but they don’t assume any legal role in the transaction. In contrast, the core value of the MoR model lies in becoming the legal seller of record—stepping directly into the transaction and absorbing its financial and legal risks.

“And let’s be honest,” Linmic adds, “working with the Big Four often means coordinating with multiple offices across every target market. Online commerce has no borders. In theory, each transaction could touch hundreds of jurisdictions. Making that compliant with consultants alone is not only expensive—it’s painfully slow and doesn’t solve the practical problems of payment and tax execution.”

That’s exactly where the MoR model shines. It’s not just about giving advice—it’s about hands-on execution. MoR partners do the heavy lifting, allowing developers to focus on product and growth instead of getting buried in back-office chaos.

But even globally, true MoR providers are rare—and most come with limitations. Take Paddle, for instance: it’s largely focused on SaaS. Its strength lies in that niche, but that also means it’s ill-suited for Asia’s fast-evolving digital content ecosystem, which spans manga, eBooks, livestreaming, stickers, virtual items, and online courses.

The Risks Behind the MoR Model: What Most Don’t See

Being a Merchant of Record (MoR) also comes with its share of responsibilities—and risks. At the heart of the MoR model is a simple but weighty premise: the provider acts as the legal seller on record, taking over all backend responsibilities related to payments, taxation, and compliance. This means that from the moment a customer clicks “purchase,” the MoR provider assumes full accountability for everything that follows.

Take payments, for instance. If a customer disputes a transaction or initiates a chargeback, the MoR is the one dealing with frozen funds or clawbacks from the bank. On the tax side, the complexity is often vastly underestimated. Different countries have different tax attribution rules—some based on IP address, others on the issuing bank, and some requiring local tax registration before a sale is even allowed. Mishandling any of these variables can easily lead to substantial fines.

On top of that, regulatory frameworks for payments and transactions vary widely by country. Even minor errors in compliance can have legal repercussions. Then there’s consumer-side risk: failed deliveries, accidental charges, or disputes over refunds all require the MoR to step in, collect evidence, and resolve the issue.

A Local Team with Global Experience

That’s why choosing the right MoR provider is critical—not just one that can handle the risks, but one that deeply understands the nuances of local regulations and cultural context. “Most Western MoR solutions simply weren’t built for Asia,” says Linmic. “They don’t have local teams, they don’t understand regional tax rules or business etiquette—and if you send them a question in Japanese or Chinese, it can take weeks or even a month to get a reply.”

Recognizing that no existing solution truly served Asia, Linmic set out to build one himself—Tokenz.

Having been part of the founding team at Japanese fintech unicorn Paidy, Linmic was deeply involved in the company’s journey—from MVP development to its acquisition by PayPal. That experience gave him a unique perspective on the regulatory and technical complexities of building compliant payment systems across borders. “That journey taught me how to build the right product, assemble the right team, and tackle the hard problems,” he says.

“You can only handle all of this if you’re the actual seller in the transaction,” Linmic emphasizes. “That gives you both the right and the responsibility to act on behalf of your customers.”

As of March 2025, Tokenz supports businesses in over 170 regions, offers more than 200 local payment options, and handles most major global currencies. With offices in Japan, Singapore, Lithuania, Taiwan, and the U.S., the company is steadily building a new outbound infrastructure—one built in Asia, for Asia.

Built for Growth, Priced for Flexibility

One of Tokenz’s strongest advantages is pricing flexibility. Linmic acknowledges that, as a growing startup, Tokenz doesn’t carry the same organizational overhead as its Western counterparts. This allows them to offer more adaptable, cost-effective solutions—a huge win for small and mid-sized developers.

The team’s composition also sets Tokenz apart. Its core members hail from companies like Paidy, Tencent Games, Stripe, and ByteDance, with deep operational experience across Japan, China, and the United States. This gives the team not just technical expertise, but also a cultural and regulatory fluency that’s critical for operating across Asia.

“We’re a team born and raised in Asia,” Linmic says. “We know how to talk to people in these markets, and we have the deep industry know-how and relationships to move things forward. That’s not something a foreign competitor can solve with a translation team.”

With its localized foundation, flexible pricing, and regulatory depth, Tokenz is not just riding the wave of Asia’s digital commerce boom—it’s laying the infrastructure for it.

Regulatory Pressure Is Mounting—”Compliant and Confident Global Expansion” Is No Longer Optional

In recent years, global regulatory bodies have tightened oversight on digital platforms, particularly around virtual goods and in-game transactions. In 2024, the U.S. Consumer Financial Protection Bureau (CFPB) issued a landmark clarification: if a gaming platform allows users to purchase virtual currency with real money and facilitates transactions, it is classified as a financial service—and is therefore subject to the Electronic Fund Transfer Act (EFTA).

Originally designed to protect consumers in electronic payments—covering areas such as refunds, dispute resolution, and fraud prevention—the EFTA now formally applies to gaming ecosystems, effectively pulling platforms into the domain of financial regulation.

Meanwhile, on the international front, the OECD has introduced a global framework for VAT and GST collection on cross-border digital transactions. The goal is simple: to ensure that foreign digital goods providers pay their fair share of taxes, creating a level playing field for local businesses.

Under OECD guidelines, platforms or non-resident vendors bear the responsibility for collecting and remitting taxes. This means marketplaces like the App Store and Google Play are now required to withhold and pay tax at the point of sale—a move designed not only to bolster national tax revenues but also to create fairer market competition for domestic companies.

These shifts aren’t limited to the West. Japan has stepped up enforcement of tax compliance for foreign firms generating domestic revenue. Epic Games, for example, was recently ordered to pay ¥3.5 billion (approximately NT$770 million) in back taxes and penalties for failing to declare in-game purchases from Japanese users on its hit title Fortnite. Similarly, Hong Kong-based Yotta Games was found non-compliant and hit with a tax and penalty bill of ¥1.8 billion (around NT$388 million).

Japan’s National Tax Agency further tightened the rules in 2024, announcing that, starting in April 2025, all non-Japanese companies providing services to Japanese consumers via platforms like Apple or Google will be subject to a 10% consumption tax, to be withheld and remitted by the platform itself. For businesses relying on these platforms, it’s yet another layer of pressure added to an already complex regulatory landscape.

These new compliance demands are also looming large for developers and content creators in Taiwan. “A lot of developers in Taiwan are highly competitive in terms of product quality,” says Linmic, “but when it comes to going global, they often get stuck navigating regulatory requirements, tax treatment, or payment flows.”

As more countries ramp up their enforcement, companies without a clear handle on accounting and compliance may find themselves among the first to be penalized. And in this environment, ignorance is no longer an excuse—it’s a liability.

“Is Accepting Credit Cards Enough?” Overlooking Local Payment Methods Could Cost You Up to 50% in Cross-Border Revenue

“Many companies still believe that as long as they can accept credit card payments, they’re ready to sell digital content internationally,” says Linmic. “But that’s one of the most common—and most costly—misconceptions.”

Global market data shows a quiet but significant shift in consumer behavior when it comes to downloadable digital content: the share of credit card payments is declining, while local payment methods are rapidly gaining ground. Take Japan, for example. “According to our data,” Linmic explains, “credit cards account for only about half of all digital content purchases. And in industries like online gaming, webtoons, or e-learning, the ratio is even lower.”

The problem? Gaining access to local payment options is notoriously difficult for foreign businesses. Between language barriers, strict vetting requirements, and the need to establish a local presence, many companies simply can’t make it past the front door. The compliance costs are steep, and building a local team demands serious capital—making it nearly impossible for companies that just want to test the waters.

Building the Highway for Asia’s Digital Creators—From Day One

In a world where regulatory scrutiny is intensifying, platform fees are rising, and tax risk is ever-present, the ability to launch globally—legally and confidently—is no longer a nice-to-have. It’s a necessity.

Tokenz is setting out to build that highway—an infrastructure that allows Asian content creators to take their ideas global from day one. “If developers can sell to the world from the very first day they launch,” Linmic says, “then the creative talent, technology, and content coming out of Asia will finally have the reach and recognition it deserves.”

With Tokenz, it’s no longer about whether a company can go global—it’s about how fast and sustainably they can scale across borders, without tripping over compliance hurdles or leaving money on the table.

Let me know if you’d like me to turn this into a cohesive full-length feature, or prepare a short version for LinkedIn, blog, or media syndication. Happy to help you polish the final package!

“Is Accepting Credit Cards Enough?” Overlooking Local Payment Methods Could Cost You Up to 50% in Cross-Border Revenue

“Many companies still believe that as long as they can accept credit card payments, they’re ready to sell digital content internationally,” says Linmic. “But that’s one of the most common—and most costly—misconceptions.”

Global market data shows a quiet but significant shift in consumer behavior when it comes to downloadable digital content: the share of credit card payments is declining, while local payment methods are rapidly gaining ground. Take Japan, for example. “According to our data,” Linmic explains, “credit cards account for only about half of all digital content purchases. And in industries like online gaming, webtoons, or e-learning, the ratio is even lower.”

The problem? Gaining access to local payment options is notoriously difficult for foreign businesses. Between language barriers, strict vetting requirements, and the need to establish a local presence, many companies simply can’t make it past the front door. The compliance costs are steep, and building a local team demands serious capital—making it nearly impossible for companies that just want to test the waters.

Building the Highway for Asia’s Digital Creators—From Day One

In a world where regulatory scrutiny is intensifying, platform fees are rising, and tax risk is ever-present, the ability to launch globally—legally and confidently—is no longer a nice-to-have. It’s a necessity.

Tokenz is setting out to build that highway—an infrastructure that allows Asian content creators to take their ideas global from day one. “If developers can sell to the world from the very first day they launch,” Linmic says, “then the creative talent, technology, and content coming out of Asia will finally have the reach and recognition it deserves.”

With Tokenz, it’s no longer about whether a company can go global—it’s about how fast and sustainably they can scale across borders, without tripping over compliance hurdles or leaving money on the table.

This article has been contributed to Asia Tech Daily.

These professions, once confined to specific venues, are now breaking free from physical boundaries thanks to online courses. This shift isn’t just about sharing their passions; it’s opened up new avenues for side hustles, entrepreneurship, and personal branding.

For many creators, this shift isn’t just about sharing their passions; it’s a new path toward side hustles, entrepreneurship, and building personal brands.

A key enabler behind this transformation is Teachify. It’s more than just a platform for launching courses—it’s a system that empowers creators to take full control of their brand and manage their business independently.

Teachify is Lawrence Lin’s third startup venture. In Taiwan’s startup ecosystem, Lawrence is no stranger. He previously founded the tech media Inside and the recipe-sharing community iCook. 

Now, with Teachify, he’s built a platform tailored for knowledge creators—individuals, businesses, and educational institutions—to easily launch online courses and grow their brands. To date, the platform boasts over 840,000 members.

To draw a parallel with e-commerce, Hahow is more akin to B2C platforms where teachers upload courses and students shop from a marketplace. Teachify, on the other hand, is more like Shopify. Rather than being a course marketplace, it provides a set of tools that help creators build their own branded sites, offering an all-in-one solution covering everything from storefront creation and cloud content management to secure content delivery. Creators can build branded course websites, foster learning communities, manage cloud-based content, and deliver seamless streaming to students, regardless of file size or format.

This model significantly lowers the technical and operational barriers for creators, enabling them to truly own their business rather than just participating on someone else’s platform.

Learning Has Changed: Knowledge Is Now an Income Stream, Not Just an Input

The way people learn today is radically different from before. Previously, picking up a new skill meant buying a book, signing up for a physical class, or attending a seminar. But now, the spread of knowledge has gone fully digital.

“People haven’t stopped learning—they’ve just changed how they do it,” Lawrence observes.
“YouTube videos, podcasts, newsletters, social media, and even short-form videos are now dominant learning methods. Online courses are a particularly promising piece of that puzzle.’’

And this shift isn’t just impacting learners—it’s disrupting the entire knowledge economy. In the past, publishing a book required passing through layers of gatekeeping: publishers decided if your ideas deserved to reach the public, how many copies to print, and where to distribute. Now, with the rise of personal branding, creators can bypass these intermediaries and go directly to market, choosing their own themes, formats, and audiences.

As the knowledge economy accelerates, Teachify isn’t just mining for gold—it’s selling the shovels. By equipping creators with the right tools, it helps them monetize their expertise.

This model reflects a broader shift: more people are seeking flexible, diverse income streams. From side hustles to full-on career pivots, financial freedom is top of mind. Unlike physical stores, stocks, or real estate, digital content creation is lower-risk, more flexible, and easy to start. Whether it’s a custom PDF, a mini live class, or a full course, anyone can turn knowledge into income.

And it’s not just individuals riding this wave—businesses are getting on board too. Teachify+—the company’s enterprise solution—offers customizable systems for digital learning. Companies don’t need to build an online store. Instead, they can use Teachify’s infrastructure to manage internal training, content delivery, and talent development.

From Carts to Courses: Teachify Brings E-Commerce Precision to the Creator World

Carving out a place in a competitive market is never easy—and Teachify’s journey was no exception. Throughout its product development process, the team encountered more than a few challenges.

In the early days of Teachify, with limited resources on hand, founder Lawrence Lin and his team focused on a single goal: lower the barrier to entry so more people could start using the product. But after spending time in deep conversation with their users, a more crucial insight surfaced—creators don’t stay just because something is easy to use. They stay if they can make money. That realization became a turning point in the evolution of Teachify’s product strategy.

What does it mean to apply e-commerce thinking? Lawrence explains that the heart of e-commerce isn’t just about completing a transaction—it’s about improving conversion rates, increasing average order value, and boosting customer retention.

These ideas are second nature in the world of online retail, but rarely applied to content monetization. Teachify began rethinking its entire product structure, not only enabling creators to upload and sell courses, but also designing a seamless shopping and learning experience—ensuring that students could continuously use the content they purchased, which in turn, would encourage repurchases and long-term engagement.

“In traditional e-commerce, once a customer receives the product in good condition, the seller’s job is mostly done. But what we’re selling is content,” Lawrence says. “If someone buys a course to prepare for a major national exam, their learning journey might span months or even a full year.”

“From purchase to test day, every step of the journey is on us. Any disruption along the way—we fix it. The learning experience must stay smooth, end to end.”

This means Teachify must manage every stage of the user journey—before the transaction (attracting creators to the platform), during the transaction (ensuring a frictionless purchase and learning experience), and after the transaction (maintaining long-term access to content and encouraging learners to return). Compared to a typical e-commerce platform, it’s a much more complex operation.

“‘To be honest, even I underestimated its complexity,’ Lawrence admits.”

Teachify isn’t just offering a website for creators to host courses—it’s integrating three distinct systems into one product: website building, video streaming, and payment processing. Each of these functions could be a standalone startup on its own. Yet Teachify chose to develop all of them in-house, keeping full control over the core technology to ensure a stable, high-quality user experience.

Cracking the Payment Puzzle: Unlocking the Final Mile of Knowledge Monetization

One of the biggest hurdles Teachify faced was payment processing. After launching their courses, creators often find themselves burdened with administrative tasks like issuing invoices, filing taxes, and dealing with financial compliance. Meanwhile, payment providers are typically wary of “deferred goods” like online courses, which aren’t immediately delivered at the time of purchase.

To address this issue, Teachify implemented a Merchant of Record (MOR) model.

In this setup, Teachify acts as the official seller of record, taking on the financial risks and compliance obligations on behalf of creators. This gives payment processors peace of mind and allows creators to focus solely on course development and community building—without worrying about reserve funds, invoicing, or tax filings.

While this level of system integration brought immense technical challenges, it also gave Teachify a distinct competitive advantage. Some competitors might excel at storefront tools but fall short on payment infrastructure. Others might have the financial side figured out but lack the robust video technology needed to support high-traffic content delivery.

Teachify chose a different path: doing it all. After three years of methodical, ground-up building, the company has accumulated the domain knowledge and technical foundation to support rapid scaling. That period of quiet preparation is now paying off in a big way. In just three years, Teachify’s user base skyrocketed from 50,000 to 840,000. The number of courses published on the platform soared from 480 to over 7,600—an astonishing 15-fold increase.

As more and more knowledge creators enter the space, Teachify has evolved from a simple online course platform into a powerful engine for the knowledge economy. This year, the company’s gross merchandise value (GMV) is projected to surpass NT$1 billion.

From a Boy Who Built Websites to the Architect of a Creator’s Stage

Lawrence Lin’s entrepreneurial journey may have been written in the stars as early as age twelve.

While other kids were glued to their Game Boys, Lawrence was learning how to build websites. “Back in middle school, I was already helping my school and friends set up their own sites so they could publish their content,” he recalls. Even as a child, the idea of being a “webmaster” sparked something in him—not just because of the technical thrill, but because he saw websites as spaces where people could gather, share, and interact.

“Building a website is like being an architect—you’re constructing a skyscraper like Taipei 101. But being a webmaster is more like running Taipei 101. You manage the utilities, the lighting, the flow of people, the shops—it’s about creating a vibrant space full of traffic, commerce, and connection.” That philosophy would later become the cornerstone of Lawrence’s entrepreneurial vision.

His first official startup was Inside, a tech media platform designed to give voice to the tech community and support the growth of Taiwan’s startup scene. He later went on to found iCook, a recipe-sharing platform that empowered home cooks to showcase their culinary creativity. “Inside gave techies a place to speak out. iCook lets food lovers share inspiration. And now, Teachify is helping anyone—literally anyone—share their expertise with the world. We’ve already seen it spread across languages: Japanese, English, Spanish.”

And yet, despite the different domains, the heart of each platform remains the same: to build a stage where creators can shine.

A Stage for the Creator Economy

Teachify isn’t just a platform—it’s a stage where voices rise. On this stage, hobbyists have become professional instructors. Side hustles have transformed into full-time careers, unlocking financial freedom. Even companies are using Teachify to build internal learning systems, helping teams grow smarter and more competitive.

“At Teachify, anyone can launch a course,” Lawrence says. “Whether your audience is five people or five thousand, there’s a space for you in the market.” He’s seen countless creators start from scratch, building their personal brands through Teachify, watching their income and influence rise in tandem. “That journey—watching someone go from unknown to unstoppable—that’s the part that feels most valuable and meaningful to me.”

The boy who once built websites has grown into a new kind of digital architect—one who is shaping the future of education and empowering others to do the same. And through it all, his mission has never changed: to build a real stage, where more people can express their talents and be seen by the world.

This grand production of the knowledge economy? It’s only just begun.

This article has been contributed to Asia Tech Daily.

Amid this harsh market winter, while many companies retreated or scaled back their operations, one startup—SignalPlus—displayed remarkable resilience and growth.

Rising in the Bear Market: Achieving Leadership in Cryptocurrency Options and Capturing 20% Market Share Within Two Years

Founded at the peak of the bull market in 2021, SignalPlus specializes in cryptocurrency derivatives infrastructure and automated trading solutions. Shortly after its establishment, the company confronted the severe bear market. Rather than pulling back, SignalPlus leveraged the downturn as an opportunity to refine its products, advance its technology, and achieve significant breakthroughs.

Co-founders Chris Yu, a former Goldman Sachs trader, and James Shan, a serial entrepreneur, lead SignalPlus alongside a core team of experts from Wall Street investment banks and technology giants such as Alibaba and ByteDance. Their extensive experience in trading practices and product development has gradually earned client trust, even as market enthusiasm dwindled to historic lows.

By 2023, SignalPlus had garnered recognition from several prominent investment institutions and established collaborations with well-known cryptocurrency exchanges like Binance, Bybit, Deribit, and OKX. Within just two years, SignalPlus managed to capture approximately 15-20% of the total options market trading volume, driving significant revenue growth.

At the end of 2024, SignalPlus again attracted recognition from the capital market, successfully securing an $11 million Series B financing round. This funding was led by AppWorks and OKX Ventures, with participation from reputable institutions such as the Avenir Group and HashKey, equipping SignalPlus with resources to accelerate its global expansion and innovation in institutional-grade trading tools.

What enabled SignalPlus to distinguish itself during the prolonged market downturn? Simply put, it provides sophisticated cryptocurrency options trading tools tailored specifically for institutional clients, including institutional-grade trading dashboards, automated market-making robots, and structured product automation solutions. These tools streamline derivatives trading, making it more efficient, convenient, and cost-effective. Additionally, SignalPlus offers users advanced, real-time risk management capabilities, eliminating the need for constant monitoring by traders and significantly reducing the expenses associated with maintaining large trading teams.

Finding the Potential: SignalPlus’s Reverse Discovery in Cryptocurrency Options Trading

Cryptocurrency options trading is booming today, but the landscape was significantly different in 2021 when SignalPlus was founded. Co-founder Chris Yu recalls that, initially, their goal was to establish a hedge fund specializing in cryptocurrency derivatives. However, they quickly realized the entire cryptocurrency options market at the time accounted for less than 0.5% of perpetual contract trading volume. This surprisingly small size sparked their curiosity: “Why was the cryptocurrency options market so underdeveloped?”

Chris explored various derivatives trading instruments and platforms, uncovering three primary pain points holding the market back. First, the cryptocurrency options market lacked a professional and stable infrastructure. Unlike traditional financial institutions, where traders have sophisticated systems for trade analysis and real-time risk management, the cryptocurrency market largely relied on intuition and experience, making precise risk assessment nearly impossible.

Second, liquidity was severely limited. Few market makers existed in cryptocurrency options, resulting in significantly lower liquidity and stability compared to traditional markets. Establishing a comprehensive market-making system required substantial capital, skilled personnel, and advanced technical resources—something most cryptocurrency institutions found prohibitively expensive. This created a cycle where insufficient liquidity deterred new players from entering the market.

Lastly, the overall market suffered from inadequate deal flow. Limited market makers and capital reduced market activity, leaving cryptocurrency options like a dormant field awaiting activation.

So, what triggered the “awakening” of the cryptocurrency options market within just over two years?

Chris identifies two main factors behind this rapid transformation. First, market volatility declined substantially, reducing the attractiveness of pure leveraged trading. In 2021, Bitcoin’s implied volatility peaked around 120%, but by 2024, it stabilized between 40% and 50%. Lower volatility squeezed profits from leveraged futures and perpetual contracts, making flexible instruments like options increasingly valuable for managing risk and constructing yield strategies. A similar evolution had occurred previously in traditional finance.

Second, institutional entry significantly boosted professional demand. Over the past two years, traditional financial institutions increasingly entered the cryptocurrency space, bringing extensive experience using options for hedging and leveraging in foreign exchange, commodities, and other traditional markets. Their entry naturally amplified the demand for cryptocurrency options.

Chris has witnessed similar transitions before. He recalls the period between 1995 and 2000 when foreign exchange options were also obscure and limited in scale. With improved infrastructure and professional tools, the foreign exchange options market evolved dramatically, becoming mainstream by 2010, on par with stock futures.

This historical perspective strengthens SignalPlus’s conviction: the cryptocurrency options market today parallels the early stage of foreign exchange options twenty years ago. With continued development of infrastructure and ecosystems, they believe the cryptocurrency options market has the potential to expand tens or even hundreds of times in the coming years.

From Widespread Promotion to In-Depth Cooperation: How SignalPlus Precisely Empowers Professional Organizations

Transitioning from traditional financial markets to cryptocurrency derivatives is far from a simple process for SignalPlus; rather, it is akin to fighting a technological battle from scratch. “Automation in options trading is extremely complex due to numerous factors that must be accounted for,” recalls Chris. “When we initially designed the product, we considered real trading scenarios and discovered that each trade could branch into tens of thousands of potential pathways.” To create a product truly adapted to cryptocurrency markets, SignalPlus meticulously restructured and rebuilt the mathematical models underlying automated trading, ensuring the solution resonated with market dynamics and user needs.

After developing the product, SignalPlus faced another challenging task: effectively promoting and implementing it.

At the end of 2023, SignalPlus officially launched its automated trading robots, initially opting for broad promotional strategies to rapidly gather feedback and optimize the technology. This phase revealed significant differences in user needs regarding automated trading tools.

“Our system involves as many as two to three hundred parameters,” explains Chris. “Mastering these settings and utilizing them effectively requires considerable expertise. Even seasoned traders from traditional finance typically require about two weeks of training to efficiently use our system. For users new to options trading, the learning curve is naturally steeper.”

Furthermore, SignalPlus recognized that different organizations have unique requirements for adapting and leveraging the platform. Some clients needed intensive guidance and support, prompting SignalPlus to continuously refine its service model. The goal was clear: enhance client efficiency and allocate service resources appropriately.

Starting in the second half of 2023, SignalPlus shifted its approach from widespread marketing to forging deeper cooperative relationships with institutional partners. This allowed each organization to find tailored solutions, aligning more closely with market demands and collectively advancing the industry.

“Numerous institutions expressed interest,” Chris said. “To ensure maximum effectiveness, we prioritized partnerships with teams already deeply familiar with options trading, allowing rapid adoption and efficient trading.” This strategic pivot quickly demonstrated its value. One trader from a large professional trading institution mastered the SignalPlus system within just one month, generating hundreds of thousands of dollars in profits.

This refined strategy enabled SignalPlus to concentrate on delivering high-value trading solutions specifically tailored to professional institutions, enhancing overall efficiency and resource allocation. 

Importantly, Chris also observed a critical shortage of qualified options professionals. “Compared to traditional finance, the cryptocurrency market has a significant shortage of professionals with deep expertise in options trading,” he noted. “However, this gap will gradually narrow as the market matures.”

SignalPlus’s ultimate vision extends beyond providing cutting-edge trading technology. They aim to convert specialized options trading expertise into foundational market infrastructure. According to Chris, the cryptocurrency options market holds the potential to truly flourish when every participant gains the capability to engage more professionally and efficiently in options trading.

SignalPlus: The Next Unicorn in Cryptocurrency Derivatives

SignalPlus’s innovative product philosophy wasn’t created overnight; rather, it emerged from the founding team’s extensive professional experiences and deep-rooted beliefs. The partnership between Chris and James dates back to 2007 when they met during an investment competition at Shanghai Jiao Tong University. 

Since then, their friendship and professional bond have grown stronger, shaped by their respective careers in traditional finance and technological entrepreneurship. Reunited by a shared vision and deep understanding of market dynamics, they formed SignalPlus to tackle real-world trading challenges head-on. Chris brings profound insights into trading market operations and pain points, while James specializes in product design and technology execution. This combination uniquely positions SignalPlus to clearly understand what needs to be done, why it must be done, and how to execute better than any competitor from day one.

This pragmatic approach is central to SignalPlus’s philosophy. From the outset, the team decided they were not interested in creating a short-term money-making tool. Instead, they sought to develop robust infrastructure that genuinely addresses market pain points and earns the trust of professional traders. Adhering to this principle has allowed SignalPlus to establish itself even during challenging bear markets, traditionally a difficult period for product promotion and growth.

As the cryptocurrency options market expands and institutional investors begin to participate more actively, SignalPlus’s future path is becoming clearer.

“We envision our automated trading infrastructure becoming the next unicorn in the cryptocurrency derivatives market,” Chris explained. “Ultimately, we even hope to bring our innovations into traditional financial markets, significantly enhancing their automation and operational efficiency. We firmly believe the market will recognize our value as long as we maintain our strategic focus and commitment to excellence.”

This article has been contributed to Asia Tech Daily.

Behind these successes, however, lie challenges that the 3D content industry cannot escape:development costs reaching hundreds of millions of dollars, extremely long production cycles, and massive labor demands.There’s no denying that the rising cost of AAA game development has become the industry’s biggest bottleneck.

Game Development: An Escalating Battle of Costs and Complexity

The cost of game development has been rising in recent years. In an interview, former PlayStation Worldwide Studios chairman Shawn Layden stated that developing a game cost around $1 million during the PS1 era, doubled to $2 million with the PS2, and increased to $4 million by the time of the PS3. When the PS4 launched, this figure had skyrocketed to $16 million, and with the arrival of the PS5, development costs have surged to a staggering $300–$400 million, exhibiting an explosive growth trend.

In addition, the complexity of game development has increased dramatically. Creating a AAA masterpiece requires not only a massive capital investment but also the coordination of hundreds of professionals—screenwriters, artists, designers, software engineers, and sound effects teams working in close collaboration. From 3D modeling to world design, from narrative to soundtrack composition, every aspect must be meticulously crafted to deliver an unparalleled immersive experience.

With production on the scale of a Hollywood blockbuster, AAA game development poses major challenges for the industry—especially for small and mid-sized studios.

Yet even with massive budgets, success is never guaranteed. Take Sony’s Concord—despite a budget of hundreds of millions, it was pulled from stores just 12 days after launch. As development costs and complexity soar, the risks are higher than ever.

Reducing costs and complexity to minimize development risks has become a major challenge for the industry. The gaming sector is in need of a transformation, and Silicon Valley startup Cybever is leading the charge.

Cybever’s AI Revolution: Transforming Game Development with Speed and Efficiency

“Traditional 3D content creation is like hand-wrapping dumplings,” said Jie Yang, cofounder and CTO of Cybever. “If you’re serving a small number of users, traditional methods work fine, but if you want to scale, you need better tools and processes. Cybever’s mission is to leverage AI to revolutionize the prototyping and pre-production phases of game development, which are the most time-consuming and labor-intensive parts of the process.”

Jie Yang and Cybever cofounder and CEO Cecilia Shen previously served as a research lead and software engineer at Google X, the Moonshot Factory, where they specialized in inventing potentially world-changing technologies. Their experience at Google X inspired them to launch Cybever, a platform that combines generative AI with 3D technology to provide practical and scalable tools for the game industry.

Traditionally, a 3D game requires various natural landscapes and urban environments, along with corresponding 3D assets that match the storyline. For instance, if the setting is a small American town in the 1990s, it would need nostalgic elements such as brick buildings, neon signs, vintage cars, and public phone booths. If the story takes place in medieval Europe, the environment might feature vast fields, wooden mills, thatched-roof cottages, stone bridges, horse-drawn carriages, and towering castles.

Meanwhile, game developers have two choices: create their own 3D assets or purchase them. The former is time-consuming, requiring powerful GPUs, CPUs, and expensive specialized software. The latter, while feasible, sacrifices originality and uniqueness, often resulting in inconsistencies in artistic style and quality that fail to meet the game’s requirements.

With the rise of generative AI, many companies have attempted to use AI to generate 3D environments and assets directly. However, with current technology, the goal of generating entire game scenes with AI remains aspirational but underwhelming in practice. Despite the efficiency and low cost advantages of GenAI-generated 3D assets, they still significantly lack in refinement, accuracy, standardization, and controllability. Additionally, generated content may involve copyright risks. Overall, the current stage of GenAI 3D technology falls short of meeting the high-quality and highly customized demands of professional game or film productions.

Why Cybever Took an Alternative Approach to Direct AI Generation

Because of this, Cybever has adopted a more market-driven and practical strategy to directly address the pain points of game developers. Instead of relying on AI to generate 3D assets from scratch, Cybever collaborates with existing asset libraries and 3D studios to streamline the production process. AI is used to retrieve the most suitable objects, accurately place them within the 3D environment, and fine-tune them—ultimately enabling high-quality 3D scene creation at an unprecedented speed.

Unlike competitors that rely on AI to generate 3D assets from scratch, Cybever leverages AI for asset retrieval, placement, and world-building—allowing for faster and higher-quality results. Additionally, Cybever supports cloud collaboration, significantly reducing development costs and complexity. It can directly generate project files for game engines like Unreal Engine 5 and Unity, enabling developers to seamlessly integrate Cybever’s output into their game development workflow without manual conversion.

This approach is not only more aligned with industry needs but also directly addresses one of the biggest challenges in game development: ensuring consistency and quality while drastically reducing production time.

“Let’s say a game requires a desolate, post-apocalyptic setting. In a traditional 3D design process, developers might spend weeks manually modeling rivers, hills, buildings, and other assets,” Yang said, “or purchasing pre-made buildings and props from 3D asset libraries.” Even after acquiring these assets, integrating them into the 3D environment and making final adjustments is still a time-consuming process.

With Cybever’s solution, developers simply enter a few prompts—such as “desolate Gobi Desert‘’ or “mountains surrounding a lake”—and the system automatically generates a 3D scene while simultaneously retrieving the most relevant assets from partner libraries and placing them in the environment. In just a few minutes, a detailed 3D world takes shape, ready for fine-tuning, including terrain and weather adjustments.

This AI tool has cut scene design time from one to two weeks down to a single day, saving game developers more than 70% of their time. Currently, Cybever has nearly 4,000 users on the waitlist. They have also invited dozens of game studios to participate in internal testing, receiving extensive positive feedback. “We’re grinding away to build features that’ll be a game-changer for creators—making game development faster, smarter, and way more affordable,” Yang said with a spark of entrepreneurial enthusiasm.

Cybever’s AI solution not only understands user intent but also incorporates historical, cultural, and geographic context to create scene layouts that align with specific styles and eras. For example, when placing assets, the AI considers historical accuracy and spatial logic, such as the proportion of farmland in a medieval village or the appropriate width of a street in a modern city. 

Cybever’s AI ‘Iron Man Suit’ Is Changing 3D Game Development

However, Cybever did not take this path from the outset. “At the beginning of our journey, we explored many different directions, including generative AI chatbots, intelligent NPCs in games, 3D avatars, and more,” admits Jie Yang.

“It took us a while to sit down, rethink, and return to our original goal: creating high-quality 3D content,” says Yang. It was during this period that the Cybever team decided to pivot away from other AI applications to focus on 3D environment generation and asset placement technology. “If you try to do everything, you end up doing nothing well. We had to let go of some seemingly promising opportunities and focus on what truly matters.”

For game studios, Cybever’s solutions can significantly reduce development time and labor costs, while for 3D asset companies, partnering with Cybever opens a vibrant new chapter in their business journey. Through AI, assets can be automatically searched and placed, making it easier for creators to find the materials they need while providing 3D artists with a broader audience of potential buyers. As a result, the once-separate realms of “creation” and “transaction” are now intricately linked, enhancing efficiency and fostering collaboration across the industry.

This is why many well-known 3D asset platforms, such as Daz 3D, along with numerous AAA-level 3D studios, are partnering with Cybever to drive innovation and development in 3D content.

Not only has Cybever’s technology been embraced by game developers, but it’s also making waves in film and entertainment. Recently, Cybever teamed up with The Day After Tomorrow director Roland Emmerich, whose upcoming sci-fi TV series Space Nation relies on AI to generate 3D environments and previsualization (previs) . This allowed the production team to build scenes faster and make quick visual adjustments.

“AI is revolutionizing content creation,” says Jie Yang. “With Cybever’s AI, everyone—whether professionals or amateur 3D enthusiasts—will be able to create high-quality 3D worlds with ease. Think of AI as an Iron Man’s armor for 3D creators—once you put it on, you’re no longer bound by traditional limitations. It enhances your abilities, amplifies your creativity, and enables you to build worlds that were once impossible.”

This article has been contributed to Asia Tech Daily.