Future made in Austria
verifiable AI algorithms and AI testing for industry – developed by DatenVorsprung.
Gain an advantage through expertise.
Approximately 60-80% of AI projects fail or are discarded before they are market-ready, because they are not efficient, robust, or secure enough in real-world applications.
DatenVorspung 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 much more. The highly specialized team, consisting of data technicians, mathematicians, and data scientists, has set itself the goal of formally verifying unexplainable processes within a learning AI (black box), so that both ethical and safety-critical concerns in the use of artificial intelligence can be eliminated. This enables a safe, trustworthy, and non-discriminatory application.
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.
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.
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.
Robustness of AI refers to the extent to which the output of a model changes when new data is used compared to the training data. Ideally, the performance should not deviate significantly and the output of the system should remain within the desired range. Robustness is important for several reasons. First, confidence in any tool depends on reliable output. Trust can diminish if an ML system performs in unpredictable ways that are difficult to understand. Second, a deviation from the expected ouput may indicate important issues that require attention.
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.
Wiener Mozart Orchester
Analysis based on data from past concerts, followed by evaluation of customer traffic. Recommendations for improving operational performance.
DNS asset GmbH
Real estate developer
An assessment of the situation and modeling of future price developments based on commuter behavior. An interactive visualization of project data.
Creation of a target audience analysis based on tourism data, social media posts, and proprietary booking data. Real-time price evaluation based on social trends.
Forecasts and recommendations in the field of marketing, with the integration of a visualization into the company's existing software.
For more information, please contact us.
+43 1 513 11 11 85