Future made in Austria

Understand and effectively utilize artificial intelligence – with verifiable and explainable AI algorithms developed by DatenVorsprung.

Gain an advantage through expertise.

Expertise

Tailormade AI Models

Improper usage of machine learning technologies often leads to suboptimal results and inadequate problem-solving. DatenVorsprung customizes state-of-the-art AI systems to fit your specific problems and needs, ensuring that you receive an optimal tool for data processing.

Verifiable Algorithms

In most cases, self-learning systems are used as "black box models". This means that there is no assurance of a "correct" output, for example, in a rule-based system that uses AI. However, in most applications, there are areas that a system should not reach or should not access. The technology developed by DatenVorsprung creates a predictable and defined solution space, in which the output of an AI system can be guaranteed to some extent.

Explainability & Reliability

Neural networks are typically highly complex, making it difficult to estimate or calculate how a network will behave in conventional systems. However, through specially developed AI architectures, DatenVorsprung creates the opportunity to make artificial intelligence explainable. By introducing explainable processes within an AI, trust is established at the system level - a unique advantage in the application of AI.

DatenVorsprung GmbH is a high-tech startup in the field of artificial intelligence. As the only IT service provider of its kind in Austria, DatenVorsprung offers verifiable and explainable AI systems for industrial processes, marketing, finance, culture, and more. The highly specialized team, consisting of data technicians, mathematicians, and data scientists, aims to formalize inexplicable processes within a self-learning AI (black box) so that ethical and security-critical concerns related to the use of artificial intelligence can be eliminated, and a safe, trustworthy, and non-discriminatory use of AI becomes possible.

Services

DatenVorsprung is your specialized service partner for artificial intelligence. We specialize in developing solutions for the formal verification of AI algorithms for both research and industry applications. Furthermore, our expertise lies in tackling optimization tasks, creating controllers, providing forecasts or recommendations, and visually representing your data and outcomes.

Research

AI algorithms are steadily making their way into scientific research, aiming to uncover new and unknown connections, states, and mechanisms. In this context, the reachtubes developed by DatenVorsprung serve as the foundation for efficiently detecting new and unfamiliar states within AI systems.

We offer:

Industry

For autonomous driving, energy management, controllers, ethics, and other related areas, it is crucial to ensure that only known, safe, and desired states (outcomes) are adhered to by machine-driven systems.

We offer:

Reachtube analyses
Integration of AI products
Customized AI implementations through commissioned work
Analysis and optimization of existing AI systems
Commissioned research and development (R&D)
and much more.

Our speciality

Neural Networks

Artificial neural networks belong to the category of “Machine Learning”. In order to enable the most complex learning possible, we use various types of neural networks that are currently being used in research. An artificial neural network is modeled after the neural network in the human brain and is designed to learn complex tasks through its structure. By using our own verifiable networks, it can be guaranteed, after a neural network has already learned machine-based, in which decision or solution area the output will be. This guarantee protects against unforeseen events and outputs.

Meta Learning

In meta-learning or meta-analysis, machine learning algorithms are trained to make predictions based on metadata. Metadata, in turn, refers to abstract information that does not directly relate to a specific property or feature, but rather leads to a conclusion only through its quantity and in relation to other data. This type of learning leads to a drastic increase in efficiency, compared to achieving the same goals through market analysis or sampling.