APE Innovation GmbH - AI Experts with a Scientific Background

#DoingGoodThings

About Us

Apes are innovative, aren't they?

The APE Innovation GmbH is a company that focuses on the good things in life. With our background in science and our expertise in artificial intelligence, we strive to make a positive impact. Our mission is to bring innovation to life.

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Our Portfolio

Feel free to contact us

Feasibility Studies

A feasibility study provides crucial early insights into a project’s viability, empowering you to seize opportunities while minimizing risks.

Project Consultation

Building on the findings of a completed feasibility study, we offer the opportunity for long-term collaboration.

Workshops

We provide customized workshops and courses for all experience levels, from beginners to advanced professionals.

Public Speaking

Looking for an inspiring AI keynote for your event?

Research

We’re passionate about science and excited to support research proposals and collaborate on innovative research projects!

The Gold Guild

coming soon ...

Areas of Expertise

Artificial intelligence, machine learning, scientific research

Generative AI

Stable Diffusion | Image Generation | Prompt Engineering | Streamlining labor-intensive tasks like writing, coding, and reading

Image Processing

Computer Vision | Automated Quality Assurance | Non Destructive Testing | Anomaly Detection | 3D and 2D Image Data Analysis

AI Infrastructure

Opensource and Inhouse AI Software | AI Integrations | Hardware Requirements Research

The Team

We bring innovation to life

Tobias

"live your best life!"

MSc. Machine Learning & Data Analytics
BSc. Software Engineering

Daria

"make a positive impact!"

MSc. Computer Science
BSc. Media Computer Science

We stand for NEW WORK

Previous Research

We do #science @Aalen University

Dec 08,2023

Lazy Labels for Chicken Segmentation

Tobias Schiele, Daria Kern, Ulrich Klauck

Procedia Computer Science Volume 225, 2023, Pages 2664-2673

Mar 01, 2022

3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review

Daria Kern and Andre Mastmeyer

Journal of Image and Graphics, Vol. 10, No. 1, pp. 17-27

Oct 05, 2021

Comparison of deep learning-based image segmentation methods for the detection of voids in X-ray images of microelectronic components

Tobias Schiele et al.

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), pp. 1320-1325

Feb 15, 2021

2D vs. 3D U-Net abdominal organ segmentation in CT data using organ bounds

Daria Kern, Ulrich Klauck, Timo Ropinski, André Mastmeyer

Proc. SPIE 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, 1160114

19 Mar, 2020

Archiving 4.0: Application of Image Processing and Machine Learning for the Robben Island Mayibuye Archives

Daria Kern, Manuel Zweng, Stanley Sello, Antoine Bagula and Ulrich Klauck

2020 International SAUPEC/RobMech/PRASA Conference, pp. 1-6

Our Partners

and References