AI testing
For us, AI testing means three things: testing smarter with AI, assessing the quality of AI applications, and assuring software that was partly built by AI. Especially in healthcare, energy, infrastructure and government, where a single error has immediate consequences, human judgment makes the difference.
AI and software quality: three scenarios
AI supports testing, is increasingly part of applications itself, and helps write code. Three scenarios, each with its own quality approach. The bar stays the same, whoever or whatever built the software: a specialist signs off on the verdict.
AI tests, the specialist decides
Anyone can run a test with AI these days. The difference is in what happens next: understanding why a test fails, seeing the risk behind it and having the confidence to say whether something can go live. That's why every result passes a specialist who knows both your system and your sector before any conclusion is drawn.
What it delivers
The tangible results of AI testing are easiest to measure in our own testing work. Here's what clients see:
How we already use AI today
Three applications running at clients right now, in healthcare, energy and government.
Beyond testing
The gains from AI are easiest to measure in our testing work, but the approach is the same everywhere: AI for the repetitive work, a specialist for the judgment. Here's how that carries through all our areas of expertise.
No experiments on your production systems
Every AI application we use has followed the same path. Our specialists explore new technology in professional communities and share what works and what doesn't. In Cerios Labs, they turn promising ideas into working pilots, together with clients, on real cases. Only what proves itself there is made robust and scalable by our solutions teams. So you know that whatever runs in your environment has already proven itself.
Further reading on AI and testing
Frequently asked questions about AI testing
AI testing is the use of artificial intelligence in the testing process: from generating automated tests based on a plain-language description to comparing screens during a migration. It's also known as AI quality assurance. The goal is faster, sharper testing, while a specialist remains responsible for the verdict.
With the same bar as for all software, but with adapted checks: reviews of the AI-generated code, extra testing depth in the parts AI helped build, and an automated regression suite as a safety net. A model's errors follow different patterns than human errors, and we design our testing around that.
No. AI takes over the repetitive work and makes testing up to a hundred times faster, but deciding what needs testing, interpreting the results and deciding whether something can go live remain human work. At Cerios, every AI result is reviewed by a specialist.
Yes, if you set it up properly. Beforehand, we look at which data is needed, in which environment it's processed and who has access. Our specialists work within the frameworks of the GDPR and the EU AI Act: your data stays yours, models get no more access than necessary and every outcome is traceable.
In our testing work: up to 100x faster test feedback, up to 70% less test automation maintenance and, in migrations, more than half less manual checking. The exact gains depend on your IT landscape, and an assessment makes them concrete in a short time.
Yes. A chatbot or LLM application never gives exactly the same answer twice, so traditional testing falls short. We test these systems for relevance, safety and consistency, using real-world scenarios and review by specialists who know what an acceptable answer looks like.
That depends on the application and the roles in the team. We tailor training and support accordingly: learning to work with AI in practice, recognizing its limitations and learning to assess the quality of its output.