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Solutions for your AI products
Synthetic data has the potential to democratize the data-based business of the future
Our Services
Synthetic Data Engineering
Boost your innovation and increase your productivity with synthetic data and system simulations. We can help to start your AI projects earlier and identify needs and issues before running expensive data acquisition campaigns. Whether you want to create pre-trained models or extend your existing datasets with edge cases, we can help to boost your model performance and give you more insights into your problem.
AI & Computer Vision Engineering
Our team of experts with years of experience in the fields can support your projects in many ways. We have been designing, developing, and deploying custom computer vision and AI solutions for years. Our services range from analyzing requirements to getting tailored solutions. We can successfully bring a project from the conceptualization stage to launch, using sophisticated algorithms and advanced analytics.
Why Synthetic Data?
Synthetic data holds great potential in various areas of AI and machine vision applications. With the help of well-understood synthetic data that can be generated in near real-time and unlimited quantities, the lead that large technology companies have in this area no longer seems unassailable. Whether you want to create pre-trained models, extend your existing datasets or increase your edge case coverage, you need data to get your development projects off the ground early or need secure ground truth for your unit tests, with the help of synthetic data you can reduce development time, costs and boost your model performance. Synthetic data can also help you to get insights into your datasets because they can be designed to fit the distribution your dataset.
Show Case: Bin Picking Scenario
Reusable data generation pipelines are ideal for industrial scenarios. By using procedural scenery elements, physically based simulations, and advanced rendering and post-processing techniques, we can generate unlimited and super realistic training data for your use cases. You can get arbitrary variance in any domain your model needs to generalize, as well as simulations of edge cases to boost your model performance.
Show Case: Real Footage & Synthetic Data Fusion
With the use of AI, modern Computer Graphics, and VFX techniques also used in the Film Industry, nowadays datasets can be created or extended with near-endless variation and realism for a fraction of the costs of multiple rounds of acquisition and annotation campaigns and with the benefit of reuse- and modifiability. This video shows the fusion of synthetic data with real video footage to demonstrate a possible training data generation use case.
About Us
Dr. Sven Wanner
Sven received his Ph.D. in Physics from Heidelberg University in 2014. His research at the Heidelberg Collaboratory for Image Processing was in the field of 4D light field analysis. His focus was on developing acquisition methods for light fields, as well as 3D- and surface property reconstruction methods in the context of industrial applications and in close collaborations with Bosch and Sony. During his 3 years as postdoc and 6 years working in the industry, he was able to gain experience in product development, as a team leader and worked on multiple different projects, always with a focus on computer vision products, AI, and synthetic data generation.
Dr. Peter Schillen
Peter received his Ph.D. in Scientific Computing and Mathematical Modeling from the University of Mannheim in 2015 after studying Mathematics and Physics at TU Kaiserslautern. He has over 10 years of experience in Image Processing and Deep Learning and has worked on various software and research projects in these areas. As a lead data scientist, he made his contribution to the field of biometric face authentication by creating data pipelines that include data collection hardware, data processing, and continuous metric evaluation. Peter’s passion for technology has helped him contribute to the advancement of emerging technologies in the digital world.