For many companies working on AI models with applications in the physical world, data presents the biggest opportunity. It’s also the biggest hurdle they face, as nicely labeled and clean real-world data is as readily available as hen’s teeth, and the costs and effort required to gather and clean up data can be immense.

Bifrost, a 3D data generation platform, believes its tech can help robotics and industrial companies solve at least one part of that problem: the time required to train AI models. The startup, based out of San Francisco, says its platform lets companies generate simulated 3D worlds to instruct their AI models and help their robots adjust to new objects, tasks and surroundings within hours instead of months.

The company said on Wednesday that it has raised $8 million in a Series A funding round led by Carbide Ventures.

“Most of our customers need vast amounts of real-world data to train AI models,” co-founder and CEO Charles Wong said in an exclusive interview with TechCrunch. “This often means they would have to deploy fleets of robots across hundreds of locations, collect millions of hours of footage, manually label the data, and implement rigorous quality checks to reduce human errors and bias. This approach is brutal. It costs millions, takes years, and proves nearly impossible to scale.”

Wong co-founded the firm with Aravind Kandiah in 2020. Wong previously worked on AI perception models for self-driving cars at Nutonomy, an MIT spin-out that worked on self-driving vehicles and autonomous mobile robots. Meanwhile, Kandiah previously built a medical AI system that detects early signs of blindness and diabetic retinopathy.

“It didn’t take long for us to realize something fundamental: AI and robotics need enormous amounts of high-quality data to function well. And that data is essential,” Kandiah told TechCrunch. “It can make or break the performance and potential of these systems. So we joined forces to start Bifrost with a single goal: solve the data problem, so AI and robotics can finally tackle the complex challenges of the physical world.”

Bifrost claims it is different from its competitors because its platform doesn’t require a team versed in creating 3D simulations to generate such data. This, Wong said, gives AI engineers a significant advantage, allowing them to develop AI systems for tasks like patrolling contested waters with autonomous boats without needing to hire a 3D team.

“Nvidia’s Omniverse tools, by contrast, require a dedicated 3D team just to operate,” Kandiah said, adding that Bifrost enables engineers in various heavy industries to teach AI systems new skills and accomplish more faster.

Bifrost’s product is currently in a closed beta with select heavy industry partners. The startup will use the fresh cash to fund the platform’s public launch in the coming months, as well as to hire more staff to speed up product development.

Wong said that the company’s primary market is the U.S., but it is also gaining momentum in Japan thanks to the country’s significant industrial sector. The startup generates revenue via an annual subscription model.

The platform’s primary users include AI developers who specialize in creating robotics systems, computer vision and perception models for applications in industries such as robotics, aerospace, defense, maritime, geospatial and industrial automation. Its target customers are big industrial companies, government organizations, and startups in the growth to late stages, all of which would have teams focused on developing physical AI solutions in their respective fields.

“We are initially focused on mission-critical, heavy industrial applications. By 2025, we aim to expand platform availability […] Looking ahead, we plan to support a broad spectrum of commercial robotics use cases, especially as robotics applications have been rapidly emerging across nearly every major sector and industry,” Kandiah said.

The Series A brings Bifrost’s total capital raised to $13.7 million. The round also saw participation from Airbus Ventures, Peak XV’s Surge, Wavemaker Partners, MD One and Techstars. The outfit has 22 staff in the U.S. and Singapore.