Tech Explained: Here’s a simplified explanation of the latest technology update around Tech Explained: simmetry.ai expands AI training platform following €330K funding in Simple Termsand what it means for users..
simmetry.ai, a
synthetic data company working across agriculture, food and industrial sectors,
has secured €330,000 from NBank, the investment and development bank of the
German state of Lower Saxony. The funding was provided through the High-Tech
Incubator (HTI) accelerator programme.
simmetry.ai was
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. The company develops a simulation platform that generates
photorealistic, fully annotated synthetic data across multiple sensor
modalities for training computer vision models. Its current focus includes
agriculture, food and industrial computer vision applications.
The platform
supports tasks such as semantic segmentation, object detection, 3D pose
estimation and regression. It is aimed at computer vision engineers and AI
developers working in areas such as robotics, autonomous machinery, quality
inspection and other environments that rely on visual perception under complex
and changing conditions.
simmetry.ai aims to
address what it describes as a key data bottleneck in AI development. According
to the company, a significant portion of effort in building AI models is spent
on data collection and preparation, particularly in industries where capturing
diverse real-world scenarios is costly or difficult. Its synthetic data
approach is intended to augment real-world datasets and improve model
robustness by generating photorealistic images across controlled conditions,
environments and edge cases.
The technology is
being applied to use cases including precision weed control, quality inspection
in food production, and AI-based monitoring in industrial environments.
Commenting on the
company’s focus, Anton Elmiger, said that agriculture was chosen as an initial
sector due to its technical complexity and potential impact. He explained that
improving crop monitoring and management requires reliable computer vision
systems, which are often limited by a lack of diverse training data.
With new funding,
the company plans to develop a scalable platform that enables AI developers to
generate photorealistic, fully annotated training data tailored to specific use
cases, with the aim of reducing the time and cost required to build robust computer
vision models in data-constrained environments.