The company

AI UI is a Thuringian AI company specializing in the development and implementation of artificial intelligence in various industries.


Our team consists of experienced experts who know the latest technologies and developments in the field of AI. We offer customized solutions for companies that want to optimize their processes and increase their business growth.

We work with customers from various industries, including universities, industry, trading and development. Our customers appreciate our reliability, flexibility and ability to solve complex problems.


If you too are interested in how AI can transform your business, contact us. We’ll help you realize the full potential of your data.


Martin Schiele

CEO | Co-Founder

Stephan Gasterstädt

CTO | Head of Development

Pengcheng Fan

CAIO | AI Specialist

Philipp Claus


Tobias Bauer


Mirko Schliemann

Business Angel | Co-Founder

We are a collective of AI enthusiasts who want to make the world a little better.

The core team consisted of Pengcheng Fan, Martin Schiele and Stephan Gasterstädt. We got support early on from Philipp Claus, who strongly influenced the layout of the application with his ideas at the beginning, but was then unable to continue working for over a year due to time constraints.

In the summer of 2020 Tobias Bauer joined us. When we wanted to start up, we were able to convince Philipp to join us again. Via Sprint Thuringia, we drew the attention of Mirko Schliemann and his business angels to us. They joined us in the fall of 2020.

Our declared goal is to make the complex field of neural networks accessible to laymen and non-programmers. Furthermore, we want to enable the industry to quickly provide a working AI solution with little effort/cost/time.


Hendrik Fischer



AI for forensic trace detection and classification

Boosting Mask R-CNN Performance for long, thin Forensic Traces with Pre-Segmentation and IoU Region Merging Abstract Mask R-CNN has recently achieved great success in the field of instance segmentation. However, weaknesses of the algorithm have been repeatedly pointed out as well, especially in the segmentation of long, sparse objects whose orientation is not exclusively horizontal or vertical. We present here an approach that significantly improves the performance of the algorithm by first pre-segmenting the images with a PSPNet algorithm. To further improve its prediction, we have developed our own cost functions and heuristics in the form of training strategies, which can prevent so-called (early) overfitting and achieve a more targeted convergence. Furthermore, due to the high variance of the images, especially for PSPNet, we aimed to develop strategies for a high robustness and generalization, which are also presented here. Link to article

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AI for live process monitoring in industry 4.0

In-situ monitoring of hybrid friction diffusion bonded EN AW 1050/EN CW 004A lap joints using artificial neural nets Abstract In this work, a dissimilar copper/aluminum lap joint was generated by force-controlled hybrid friction diffusion bonding setup (HFDB). During the welding process, the appearing torque, the welding force as well as the plunge depth are recorded over time. Due to the force-controlled process, tool wear and the use of different materials, the resulting dataseries varies significantly, which makes quality assurance according to classical methods very difficult. Therefore, a Convolutional Neural Network was developed which allows the evaluation of the recorded process data. In thisstudy, data from sound welds as well as data from samples with weld defects were considered. In addition to the different welding qualities, deviations from the ideal conditions due to tool wear and the use of different alloys were also considered. The validity of the developed approach is

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AI for brake related, non-exhaust emissions

Artificial Neural Network regression models for the prediction of brake-related emissions Abstract The growth of electric propulsion systems motivates the automotive industry to transfer the focus from exhaust to non-exhaust emissions, with special attention to brake-related emissions. The literature lacks well-established approaches that describe the particulate emissions through reliable analytical correlations. Moreover, the mechanisms of brake particulate formation entail highly stochastic phenomena, which cannot be captured by means of traditional deterministic modelling tools. Machine learning algorithms have been recently used as an alternative method to seek for a branched correlation between tribological properties (i.e. friction coefficient and wear rate), pad composition, environmental and operating conditions. In this regard, the presented work focuses on the study and identification of sophisticated stochastic meta-models for the prediction of the number of emitted brake particulate and associated uncertainty. Specifically, artificial neural networks are developed and validated against brake emission data collected in real driving

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We will be happy to advise you!

The integration of AI into your work processes offers endless possibilities.
If you are undecided or have any questions, please do not hesitate to contact us. We will be happy to advise you on how and where artificial intelligence could help your company to grow.

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