Simmetry.ai is Building Scalable Synthetic Data Platform for AI Training

Simmetry.ai, providing high-quality, annotated training data for your machine learning applications, led by Kai von Szadkowski, Anton Elmiger, and Prof. Dr. Stefan Stiene, has raised €330,000 from NBank, the investment and development bank of the German state of Lower Saxony, as part of the High-Tech Incubator (HTI) accelerator programme.

simmetry.ai generates photorealistic, annotated synthetic data to train computer vision models for robotics, autonomous machines, and inspection systems.

AI adoption is limited by the data bottleneck; over 80% of effort goes into collecting and preparing real-world data, especially in agriculture and manufacturing.

simmetry.ai addresses this by generating photorealistic synthetic data that augments datasets, helping models handle edge cases and become more robust, enabling uses like precision weed control, food quality inspection, and industrial monitoring.

simmetry.ai was founded in 2024 as a spin-off from the German Research Center for Artificial Intelligence by Kai von Szadkowski (CEO), Anton Elmiger (CTO), and Prof. Dr. Stefan Stiene.

After years in computer vision and robotics research, they saw that data, not algorithms, was the real bottleneck. What began as generating synthetic datasets as a workaround evolved into the company’s core technology.

Gartner estimates 60% of data used in AI projects was synthetic in 2024, rising to 95% by 2030. simmetry.ai is positioned within this shift, gaining early traction in agriculture and expanding into adjacent sectors.

The grant will support a scalable platform that lets developers generate photorealistic, fully annotated training data for tasks like segmentation, 3D pose estimation, and regression, reducing the cost and time required to build reliable computer vision models where real-world data is limited.

Anton Elmiger said the company began with agriculture due to its high impact and data complexity, where limited training data makes computer vision difficult. The grant will help turn its technology into a broader platform, enabling faster AI innovation across agriculture, industry, and other real-world applications.