Customer interest is supposed to tell a deep-tech founder that the market is opening. For Seamless XR, it revealed a more uncomfortable possibility. Companies were hiring for capabilities similar to its surgical haptics, hospital departments had concrete requirements, and the underlying technology worked. And yet, every serious conversation added another layer of anatomy, tissue behavior, procedure-specific content, visuals, or haptics. The stronger the evidence of demand became, the harder it was to see how substantially the same product could serve the next customer.
Seamless XR Found Demand Before It Found a Repeatable Product
Seamless XR was co-founded by Dr. Ajinkya Bhat, a robotics researcher and engineer whose work spans more than a decade across soft robotics, wearable interfaces, haptics, and assistive technologies. Bhat holds a PhD from the National University of Singapore and previously studied robotics at Carnegie Mellon University and the University of Plymouth. His research background became the technical foundation for Seamless XR, which he now leads as co-founder and CEO.
Bhat and co-founder Jonathan Ambrose developed six patents spanning wearable technology, haptics, and soft actuation before building Seamless around the commercial potential of that research, according to the company’s history. One early application was surgical simulation, where their haptic technology could help recreate tactile sensations for training.
The underlying problem gave them good reasons to pursue the market. Access to cadaver-based training can be constrained by cost, availability, and infrastructure, while simulation offers surgeons additional opportunities to practice. Yet Bhat told ngopihangat that Seamless eventually encountered a problem that had little to do with the validity of its haptic technology.
“The technology worked, but the commercial unit was too customized and the surrounding stack was too complex.”
The warning emerged as Seamless looked more closely at the organizations it hoped to serve. Bhat said the team used LinkedIn job notifications as an informal market test and found companies recruiting for capabilities similar to what Seamless was developing. Discussions with department leads across Singapore’s public hospital clusters reinforced the existence of demand, but they also showed how differently that demand translated into product requirements.
A surgeon’s specialty could change enough of the simulation environment that a new customer was not necessarily asking for another deployment of the same product.
“Depending on what kind of surgery a person specialized in, they were asking for requirements that would need significant modification of the device,”
Bhat said.
Customer discovery had therefore produced two signals at once. Organizations had reasons to want the technology, but the requirements emerging behind that interest were making it harder for Seamless to define a product that could remain substantially the same as the customer base expanded.
Surgical Simulation Turned One Technology Into Many Product Requirements
A credible surgical simulator needs more than haptic hardware. Bhat said the stack could require realistic tissue behavior, anatomical models, procedure-specific content, visual simulation, and haptics calibrated to the task. Different specialties changed enough of those requirements that the commercial unit stopped looking standard.
The 2025 systematic review Research trends in virtual reality surgical simulation for education, led by researchers at Korea’s National Cancer Center, identified 395 papers on VR surgical education and included 92 in its final analysis. Laparoscopic and endoscopic training were the most studied procedures, and the researchers concluded that the field’s future potential depends partly on improvements in realism, cost efficiency, and integration into surgical curricula.
Haptics itself remains technically relevant. The 2024 review Findings Favor Haptics Feedback in Virtual Simulation Surgical Education examined 51 studies and found that more reported performance results favored haptic feedback than non-haptic conditions, although outcomes varied.
A 2025 systematic review and meta-analysis, Comparing Learning Outcomes of Virtual Reality Simulators Using Haptic Feedback Versus Box Trainer in Laparoscopic Training, covered seven randomized trials and 125 participants. It found that haptic VR and physical box trainers could both support surgical-skill transfer, while physical trainers retained advantages in the learning curve and natural tactile feedback.
That evidence helps explain why Seamless’s first thesis was plausible. The problem existed, haptics had a credible role, and potential users expressed needs. The weakness appeared when those needs had to be translated into one product that could be sold repeatedly without rebuilding major parts of the system.
More Customer Requests Could Have Increased Fragmentation
For Seamless, the accumulating requirements led to a commercial choice that Bhat described in unusually direct terms:
“Over-engineer the product to cover every requirement, which makes it expensive. Now you have to charge more to maintain margins, which automatically narrows the pool of people willing to pay. Develop customizations for each sub-segment, which is also expensive on the R&D side and only really works as a project-based model.”
The problem was therefore not a simple lack of demand. One route risked increasing product complexity and price until the available customer pool became smaller. The other could satisfy specialized customers but required repeated engineering work, pulling the company toward a project-based model instead of a product that could be deployed repeatedly with limited modification.
Separate manufacturing research points to the broader cost of excessive variety. The 2025 study From mass customization to circular customization: measuring and managing variety in product service systems drew on a three-year project involving 39 industry experts and found that product-service variety can create lifecycle complexity, higher costs, and inefficiency even when customization creates customer value.
For deep-tech founders, repeated feature requests are therefore an incomplete market signal. A stronger test is if customer discovery makes the product architecture converge or keeps creating branches that require additional engineering.
One Clinician Question Changed the Commercial Job of the Technology
The pivot emerged during clinician discovery. Bhat asked an occupational therapist what problem she would solve if she had a “magic wand.” Her answer moved the discussion away from simulating an external procedure and toward measuring a recurring clinical problem.
“Her answer was that she could not reliably tell whether a patient’s sensation was recovering.”
Seamless began exploring how its existing micro-haptic technology could deliver controlled tactile stimuli for sensory assessment. Bhat said the company built the first Artemis prototype within five months, preserving the core engineering lineage while changing what the technology was being asked to accomplish commercially.
That change also altered the meaning of technical performance. Surgical simulation rewarded high fidelity and dense tactile experiences. Sensory assessment needed controlled, localized stimulation across clinically relevant areas, so maximizing actuator density was no longer the central product objective.
The Core Technology Survived, but the Product Architecture Did Not
This pivot did not require Seamless XR to discard the engineering capability it had already developed. Bhat said the technical foundation behind its earlier XR work remained directly useful even though the job assigned to that technology changed.
“The transferable core was our ability to create soft, localized and controllable tactile stimuli. Pneumatic micro-haptic actuators, embedded controls, and our understanding of human–machine interaction all survived the pivot.”
What counted as technical progress changed with the application. Surgical simulation had pushed the team toward richer tactile experiences and denser actuator configurations, but those attributes were no longer the main objective once the technology was being applied to sensory assessment.
“What stopped mattering was the high density and fidelity.”
And that continuity at the technology level did not spare Seamless from rebuilding the product around it. The new clinical use case required a different definition of what the system should do, how people should interact with it, and what would eventually be required to commercialize it.
“We had to rebuild almost everything commercial and clinical: the intended use, protocol, patient interface, scoring logic, automated workflow, reporting, quality system, regulatory strategy, manufacturing plan and evidence programme.”
The experience separates the survival of a research asset from the survival of the startup thesis built around it. Bhat described the transition in terms that capture why Seamless did not treat the move as a technological restart.
“The pivot was therefore not from a failed technology to a new technology. It was from a commercially infeasible use case to a scalable one.”
For deep-tech founders, that creates a more useful way to think about a pivot. Valuable IP can remain intact even when the product, customer problem, and commercial structure surrounding it need to be reconsidered almost completely.
Korea Is Putting More Capital Behind Technology Commercialization
The question is timely for Korea as public support for technology commercialization expands.
In its September budget proposal, the Ministry of SMEs and Startups allocated KRW 54 billion to its technology-commercialization package for 2027, up from KRW 24 billion in the 2026 budget, and proposed KRW 156.3 billion for the Super Gap Startup Project.
The ministry also opened a September recruitment process for organizations that will support technology commercialization and investment attraction under the 2027 Super Gap Startup Project. For Korean deep-tech startups moving research assets toward commercial markets, technical performance and customer interest may be important, but neither still proves that substantially the same product can be sold repeatedly.
That is why if every new buyer requires new engineering, new content, or a substantially different surrounding system, growing interest may be exposing a fragmented commercial architecture. Founders, accelerators, corporate partners, and investors can therefore track how much the product changes after each serious customer conversation.
The Best Customer Signal May Be Convergence
Customer discovery is often treated as a search for confirmation that a problem is real. And yet, deep-tech founders may need to watch for another signal at the same time: successive conversations should make the product definition sharper instead of steadily making the product larger and more bespoke.
Seamless XR‘s experience suggests that preserving valuable technology does not require preserving the first business built around it. A stronger commercialization threshold may arrive when the next customer is likely to buy substantially the same product as the last one.
Key Takeaway
- Seamless XR found real interest in surgical haptics, but customer demand exposed product fragmentation. Ajinkya Bhat said different surgical specialties required enough product modification to undermine repeatability.
- Demand validation and deep-tech product scalability are separate tests. A real problem, functioning technology, and interested customers do not prove that one commercial product can serve the market repeatedly.
- Surgical simulation technology can require a broad surrounding stack. Procedure-specific anatomy, tissue behavior, visual simulation, content, and haptic requirements can increase customization beyond the core technology.
- Seamless preserved its soft-robotics and pneumatic haptics foundation while rebuilding the commercial system around it. Artemis changed the intended use and required a new protocol, interface, scoring, workflow, regulatory strategy, manufacturing plan, and evidence program.
- Korea is increasing support for deep-tech commercialization. MSS proposed KRW 54 billion for its 2027 technology-commercialization package and KRW 156.3 billion for the Super Gap Startup Project.
- A useful commercialization signal is product convergence. Founders should ask if each serious customer makes the product definition more repeatable or creates another branch of custom engineering.
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