We enable control over AI systems

AI Risk Testing and Certification of AI systems for industry

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.

AI Risk Testing / Certification

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.

Robustness

Robustness determines how well an AI system adheres to its actual purpose. Especially in case of disturbances and unpredictable data situation, malfunctions or risks can be avoided and optimal behavior can be maintained. Reinforcement learning and AI-controlled systems show significant potential in process optimization, aiming to maximise efficiency and minimize unwanted losses. The method developed at DatenVorsprung introduces continuous monitoring of a given system, ensuring a robust and consistent behaviour of the controller, even in response to the slightest changes within the system.

Explainability & Trustworthiness

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.

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.

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 & Robustness Analysis of AI
Integration of AI Products
Customized AI Implementations through Commissioned Work
Analysis & Optimization of Existing AI Systems
Consultancy for Safety & Robustness in AI
and much more...

Our speciality

Robustness Analysis

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.

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.