German startup simmetry.ai raises €330,000 to scale its synthetic data platform for agriculture, food and industrial AI applications.
A German startup developing synthetic data for computer vision systems has secured €330,000 in early-stage funding as it seeks to address one of artificial intelligence’s most persistent bottlenecks: access to high-quality training data.
simmetry.ai, founded in 2024 as a spin-off from the German Research Centre for Artificial Intelligence (DFKI), received the investment from NBank, the investment and development bank of the German state of Lower Saxony. The funding was awarded through the High-Tech Incubator accelerator program.
The company was established by Kai von Szadkowski, its chief executive; Anton Elmiger, its chief technology officer; and Prof. Dr. Stefan Stiene. Their platform generates photorealistic, fully annotated synthetic data across multiple sensor modalities to train computer vision models. Initial applications focus on agriculture, food production and industrial environments — sectors where collecting diverse real-world imagery can be costly, slow, or impractical.
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Computer vision systems in these industries must perform reliably under variable lighting, weather and operational conditions. Yet assembling sufficiently varied datasets often consumes the bulk of development time. Simmetry.ai argues that synthetic data — generated in simulated but highly realistic environments — can supplement real-world datasets and improve model robustness, particularly in edge cases that are difficult to capture in the field.
The platform supports tasks including semantic segmentation, object detection, 3D pose estimation and regression. It is aimed at AI developers building systems for robotics, autonomous machinery, quality inspection and industrial monitoring.
Among its early use cases are precision weed control in agriculture, quality inspection in food production and AI-driven monitoring in manufacturing facilities.
Elmiger said the company chose agriculture as its initial focus because of its technical complexity and real-world impact. Reliable crop monitoring and management systems, he noted, depend on robust visual models — and those models are often constrained by limited and inconsistent training data.
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With the new funding, simmetry.ai plans to expand its platform, enabling developers to generate customized, photorealistic training datasets tailored to specific operational scenarios. The goal is to reduce both the time and cost required to build dependable computer vision systems in environments where data is scarce but accuracy is critical.
As industries increasingly turn to automation and machine perception, the ability to simulate reality — convincingly and at scale — may prove as valuable as capturing it.
