German synthetic data startup simmetry.ai secures €330k to tackle this key bottleneck holding back AI adoption

simmetry.ai, an Osnabrück-based synthetic data company accelerating AI development across agriculture, food and industrial sectors, today announced it has secured €330k from NBank, the investment and development bank of the German state of Lower Saxony. The company secured this financing as part of the High-Tech Incubator (HTI) accelerator programme.

Founded in 2024 as a spin-off from the German Research Centre for Artificial Intelligence (DFKI) by Kai von Szadkowski (CEO), Anton Elmiger (CTO) and Prof Dr Stefan Stiene, simmetry.ai is a simulation platform that generates photorealistic, fully-annotated synthetic data across multiple sensor modalities for training computer vision models. Its current focus areas are agriculture, food, and industrial CV applications.

Anton Elmiger, CTO and co-founder of simmetry.ai, said, “We started with agriculture because it is both a highly impactful and technically demanding field for AI. Improving how crops are monitored and managed can support more efficient and sustainable farming, but building reliable computer vision systems here is extremely difficult due to the lack of diverse training data.

“With this grant, we are turning our technology into a platform that makes advanced data generation accessible to more teams, helping innovation move faster not only in agriculture, but also in industrial and other real-world applications.”

According to the company, the platform supports tasks such as semantic segmentation, object detection, 3D pose estimation and regression. It caters to computer vision engineers and AI developers working in robotics, autonomous machinery, quality inspection and other domains that rely on visual perception in complex, changing environments.

simmetry.ai aims to address the data bottleneck hindering AI adoption. It claims that more than 80% of the effort in developing an AI model is dedicated to data collection and preparation, especially in industries like agriculture and manufacturing, where capturing all real-world scenarios can be extremely costly. This challenge is the main reason why AI remains economically unfeasible for many applications that could provide substantial value.

The German startup claims to solve this by generating synthetic data that augments real-world datasets. It enables AI models to become more robust and generalisable by producing photorealistic images across a controlled range of conditions, environments and edge cases. Its applications include precision weed control with reduced pesticide use, quality inspection in food production, and AI-based monitoring in industrial settings. 

The company plans to use this grant to fund the development of a scalable platform that enables AI developers to generate photorealistic, fully annotated training data tailored to their specific use cases, including semantic segmentation and 3D pose estimation to regression tasks. The platform is intended to reduce the time and cost currently required to build robust computer vision models, particularly in environments where real-world data is scarce or difficult to collect.

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