
Bifrost AI provides physically accurate synthetic datasets generated in simulated 3D worlds to accelerate AI and robotics development. Their platform allows users to train and validate AI models without real-world data, offering pixel-perfect labels and rich scenario metadata, including object type, speed, bearing, and environmental conditions. Bifrost enables rapid scenario creation and dataset generation, adapting AI to new environments and objects quickly. The company focuses on bridging the domain gap with realistic synthetic data through customizable sensor emulation and physically accurate 3D assets. Their platform is designed for ease of use, with templates, presets, and automated labeling, allowing teams to improve AI performance systematically by identifying failures and generating data patches. Key use cases include prototyping, bootstrapping, class balancing, performance boosting, automated labeling, and demo optimization across industries like Maritime, Geospatial, and Robotics.

Bifrost AI provides physically accurate synthetic datasets generated in simulated 3D worlds to accelerate AI and robotics development. Their platform allows users to train and validate AI models without real-world data, offering pixel-perfect labels and rich scenario metadata, including object type, speed, bearing, and environmental conditions. Bifrost enables rapid scenario creation and dataset generation, adapting AI to new environments and objects quickly. The company focuses on bridging the domain gap with realistic synthetic data through customizable sensor emulation and physically accurate 3D assets. Their platform is designed for ease of use, with templates, presets, and automated labeling, allowing teams to improve AI performance systematically by identifying failures and generating data patches. Key use cases include prototyping, bootstrapping, class balancing, performance boosting, automated labeling, and demo optimization across industries like Maritime, Geospatial, and Robotics.
What they do: Generate physically accurate 3D synthetic datasets and scenarios for training and testing perception and robotics systems
Founded: 2020
Founders: Charles Wong and Aravind Kandiah
Recent funding: Series A $8M (Oct 30, 2024); total raised ≈ $13M–$13.7M
Employees: ~40
Synthetic data and 3D simulation for perception, robotics and autonomous systems across industries such as maritime, geospatial, aerial, industrial automation and off‑road/off‑world applications.
2020
Synthetic data / Robotics / Computer vision
$8,000,000
Round included participation from Airbus Ventures, Peak XV (Surge), Wavemaker Partners, MD ONE, and Techstars
“Participation from corporate and venture investors including Carbide Ventures and Airbus Ventures”