Bifrost pitches 3-D AI training platform to Korean manufacturers

Charles Wong, co-founder and CEO of San Francisco-based startup Bifrost, speaks during an interview with the Korea JoongAng Daily at Coex in Gangnam District, southern Seoul, on May 20. [AWS] [INTERVIEW] Bifrost, a San Francisco-based startup, leverages computer vision and 3-D generation technologies to train so-called physical AI systems, a service that co-founder and CEO Charles Wong believes could meet the growing needs of Korean manufacturers and tech companies. "There's a huge manufacturing base in Korea, and you build your own products ranging from semiconductors, electronics and motors — there's a nice big ecosystem of all these pieces coming together, which not many other countries have strong points in all of them," said Charles Wong, co-founder and CEO of Bifrost, in a recent interview with the Korea JoongAng Daily during the AWS Summit Seoul 2026, held between Wednesday and Thursday at Coex in Gangnam District, southern Seoul. Bifrost has recently been eyeing expanding into the Korean market. Its current sole Korean customer is an autonomous maritime startup, Seadronix, to generate the synthetic training and testing data that the company uses to develop its autonomous ship navigation AI. Bifrost is now building a similar platform centered on robotics, a push that has accelerated since Nvidia CEO Jensen Huang declared last year that physical AI and robotics represent the next major frontier. Wong sees Korea as the ideal market for that expansion. "We just completed a successful trial with a major Korean company for home robotics use cases," he said. "This company is doing development for future generations of robotics products, and they want to use our tools to speed up development and enable new capabilities." Beyond that trial, Bifrost is in talks with several Korean conglomerates about partnerships, which Wong said he looks forward to announcing when they are finalized. Wong co-founded Bifrost in 2020 after working on AI perception models for self-driving cars at NuTonomy, an MIT spinout that ran the world's first autonomous taxi trial in Singapore in 2016. Even then, before AI had become part of daily life, he was convinced that autonomous technology would eventually reach every industry — from homes and factories to ships and aircraft. Charles Wong, co-founder and CEO of San Francisco-based startup Bifrost, speaks during an interview with the Korea JoongAng Daily at southern Seoul's Coex on May 20. [AWS] "It was just a matter of when," he said. "So we thought about what the bottleneck was stopping AI from coming into the real world, and that was data. We had to find a better way to create huge amounts of organized data for physical AI to learn from, and that's how we landed on synthetic data." Bifrost's core product is Stardust, a synthetic data platform that allows an AI developer with no simulation experience and no dedicated 3-D team to produce high-quality simulated environments for training AI models. Bifrost's goal is not to replace real-world data entirely, but to give developers a way to identify weaknesses in their AI systems early and cheaply — so by the time they move to expensive real-world testing, they already know exactly what to look for. "A developer can go from zero to a ready dataset in about five minutes," Wong said. Despite having a team of around 30 employees, Bifrost's customers span multiple sectors — from maritime and aerial to defense and government — including the U.S. Air Force, NASA and defense technology firm Anduril. The startup has raised a total of $13.7 million across four funding rounds, with the latest $8 million secured from Carbide Ventures, Airbus Ventures, Peak XV's Surge and several more. A screen capture of Bifrost website [BIFROST] Underpinning all of this is Bifrost's deep reliance on AWS infrastructure, which provides the computing power, storage and AI tooling that allow the company's simulation software to operate at scale. "AWS has some great features that are really important to us," Wong said. "SageMaker lets us run inference workloads very quickly, which means our customers can validate their perception systems — basically test whether their AI can actually see and understand the world correctly — in a very short time. "The other big one is Bedrock. Right now, our users write Python code to build scenarios, but a lot of them are already using tools like Claude Code or Copilot to do that very quickly by connecting those tools to our documentation. It's very natural that at some point we bring that functionality into the app itself, through something like Bedrock — so instead of writing code by hand, users can just describe what they want and an AI agent builds the scenario for them." BY LEE JAE-LIM [lee.jaelim@joongang.co.kr]