AI and software quality
AI is transforming software testing. It makes work faster, sharper and more consistent. But only if you know exactly what you want to achieve and how to use it. At Cerios, we use AI to enhance quality. In test processes, in tooling and in teams.
Our AI approach
At Cerios, we first test AI in our own projects before using it for clients. In our community, we share knowledge and ask the tough questions. Our consultants use AI to analyze data, refine user stories and generate test cases. What works, we take forward. What doesn’t work, we improve. This is how you get the best results from AI in your software.
AI applications
AI can be applied in many ways to support software quality. Your approach depends on your situation and goals.
AI in daily testing
We use AI to analyze user stories, refine requirements and prepare test cases. This saves time and frees your team to focus on work that truly adds value: craftsmanship, collaboration and strategic decision-making.
Validating AI systems
AI behaves differently from traditional software and requires a unique testing approach. We detect bias, ensure explainability and verify compliance with the EU AI Act. From algorithm to real-world impact, you get complete insight.
Making teams AI-savvy
Knowledge of AI is becoming essential, yet not everyone knows where to start. Through training, learning journeys and communities, we build skills for your team and our own consultants. Learning together works best.
Secure AI Deployment
Without clear governance, AI can create more problems than it solves. We establish frameworks, ensure transparent processes and protect privacy. This gives you AI that you can trust and control.
Assessing AI Readiness
Many organizations do not know where they stand with AI. Where are the opportunities? What are the risks? Which steps will deliver the most value? We create a realistic roadmap tailored to your organization.
Why organizations choose us
We are not the first to talk about AI, but we are one of the few to systematically test, apply and further develop it. Our consultants work with it every day. In real projects, with real customers, with real challenges.
This gives us insight into what works and what doesn't. What pitfalls there are and how to avoid them. We are happy to share that knowledge through training courses, communities and concrete projects. Because only if everyone understands AI can we benefit from it together.
Frequently asked questions about software-quality AI
AI often raises the same questions. What does it really deliver, what about safety and when is it useful to use? We'll answer the most common questions so you can quickly know where you stand.
That depends on your current test processes and challenges. If you spend a lot of time on repetitive tasks, have trouble with consistency, or want faster feedback, AI can be valuable. Or maybe test automation without AI is the best choice. We always start with an analysis of your situation.
Safety comes first. We work with platforms within your own environment, such as Microsoft Azure, and set clear governance rules. You always have control over your data and processes.
No, AI empowers testers. It takes over repetitive tasks and provides quick insight into large amounts of data, so that your team can focus on analysis, collaboration, and strategic choices. Human expertise is becoming more important, not less.
For daily use of AI tools for software quality, you can often see a difference within a few weeks. For more complex implementations or building AI skills, we usually count on a few months.
This varies by situation. Some AI tools are relatively easy to add to existing processes, while others require more investment. We always make a realistic cost-benefit analysis first. Should our people take AI training first? Training helps, but it's not always necessary to get started. Learning by doing is just as important. We're gradually building AI skills, tailored to your pace and needs. Some tools can be used immediately. We have various training courses for all levels of knowledge that we can offer at the right time.
By systematically testing, detecting bias and ensuring explainability. We monitor performance and adjust where necessary. Transparency and auditability are key.