We help developers create powerful artificial intelligence systems with ease

Examples

Newcomers

Starting with Deep Learning means starting to code. Getting runnable code is a very tedious process that involves lots of trial and error. Why not start with your idea first and see how it is done later on?
Managers

In data science there are no established standards for implementing projects which makes project transfers between data scientists very difficult. Our auto-generated projects combine best practices from software engineering and data science.
Data Scientists

Writing boilerplate code for prototyping ideas is both exhausting and time-consuming. Focus on the parts of the code that are important for your idea and let us take care of the rest.
Mentors

Conveying principles of Deep Learning to newcomers is tremendously slowed down by implementation details that stop them from getting receptive for more ambitious ideas. Would it not be great to focus on concepts first and let newcomers explore implementation details on their own later on?
Consultants

Machine learning solutions are highly tailored pieces of software that are incrementally developed from scratch. At the end, cleaning up a messy project for delivery can be cumbersome. Save your time by generating clean and documented code that represents the final increment of your solution.

CREAIDAI // Blueprint Engine BETA
- Model code
- Training code
- Deployment code
Develop powerful end-to-end Deep-Learning-based AI systems on a pure concept level using an intuitive GUI without writing a single line of code. Fully documented and style-compliant source code is then generated for training, evaluation, and deployment of the designed AI system, using well-known libraries and frameworks such as TensorFlow or Keras. All generated source code can be edited at any level of granularity and executed effortlessly, offering you ultimate flexibility and a significant productivity boost.

Try our BETA
CREAIDAI // Environments COMING SOON
- Optimized packages
- GUI-assisted setup
- VM / Cloud images
Especially the first steps of AI development are cumbersome as developers are faced with an ever-growing universe of software to choose from, to install, to optimize, and to manage. We guide you through these initial steps with GUI-based interactive setup instructions. In addition, you can choose from a selection of pre-configured development environments which are available as virtual machine images for popular virtualization tools such as VirtualBox or Docker and for cloud providers such as AWS. Never waste time again browsing the web for setup guides and reading endless articles about complicated setup steps but rather get started immediately.

Dr. Sigurd Spieckermann

Sigurd is an expert in machine learning with a specialization in Deep Learning for time series data and Natural Language Processing and more than 7 years' experience in AI R&D. Sigurd has been a research scientist at Siemens Corporate Technology for more than 3 years. He received a PhD in multi-task and transfer learning with recurrent neural networks from Technical University of Munich and a master's degree from Stanford University. Sigurd's master's thesis in distributed computing for machine learning was advised by Prof. Andrew Ng.
Dr. Denis Krompaß

Denis is an expert in machine learning with a specialization in Deep Learning and (Deep) Reinforcement Learning and more than 7 years' experience in AI R&D. Denis has been at Siemens Corporate Technology for more than 3 years as a Senior Key Expert for Deep Learning and Research Lead of the Siemens AI Lab. He received a PhD in representation learning on knowledge graphs from Ludwig-Maximilian University of Munich, supervised by Prof. Volker Tresp, and a master's degree in Computational Biology from the Ludwig-Maximilian and Technical University of Munich.

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