In a world increasingly shaped by artificial intelligence (AI), organisations are facing new challenges – and opportunities. With our long-standing experience in software quality assurance, we support you in integrating AI into your testing processes in a targeted way and in putting your AI systems through their paces.

Our mission: maximum quality and reliability for your software – enabled by state-of-the-art AI methods.

We use AI to holistically optimise and largely automate your testing processes – from test case design and execution through to evaluation.

In test case design, we generate tests directly from your requirements, for example from systems such as Jira or based on BDD specifications like Cucumber. This ensures that test cases remain consistent, traceable and always up to date.

Through self-healing tests, we automatically detect and analyse changes to applications or user interfaces. Your test cases are updated when adjustments are made – supported manually where needed or fully automated – significantly reducing maintenance effort and the risk of errors.

For test data generation, we create realistic, domain-consistent test data and derive equivalence classes and boundary values. This ensures that both standard scenarios and critical edge cases are systematically covered.

In test planning and prioritisation, AI-driven analyses of test coverage, data-based success measurement and defect analysis help you identify risks early on and understand which test cases provide the greatest value for quality and stability.

Our test visualisation and analytics enable automated preparation of test results and defects. Dashboards and reports show coverage, defect frequency, trends and other key metrics, so that decisions can be made transparently and based on facts.

In terms of tooling, we rely on modern platforms and tools to make AI usable across the entire test process – from integration into existing toolchains to stable operation in production environments.

With smart regression testing, we combine rule-based analysis with targeted re-execution of tests. This means that only those test cases are run that are necessary for maximum efficiency and risk reduction, instead of repeatedly running full regression suites unnecessarily.

AI systems place special demands on quality assurance. We test your AI thoroughly to ensure that it is reliable, safe and ethically sound.

We focus on adaptability and flexibility: we make sure that your system continues to operate robustly and correctly even when conditions and environments change.

We also look at evolution and autonomy, assessing how well the AI learns from experience and analysing its behaviour in autonomous scenarios in detail. This shows whether the system is evolving in line with expectations in production.

Another key focus is transparency and explainability. We examine the traceability of algorithms and datasets and support users in correctly interpreting AI results, thereby strengthening trust and acceptance.

From the perspective of ethics and bias mitigation, we validate your systems against established ethical guidelines, such as the EU guidelines for trustworthy AI, and identify and reduce inappropriate distortions in data and models.

To ensure safety and protection from unintended side effects, we identify potentially harmful impacts at an early stage and conduct tests in virtual environments to minimise risks for real users and live systems.

We also specifically address the prevention of reward hacking through independent tests, ensuring that your AI is not driven to undesirable or harmful behaviour by poorly defined objectives or incentive structures.