Trustworthy AI
Starts With Testing

Chatbots & Assistants

LLM-based dialogue systems must do more than provide good answers. To be trustworthy, they must be reliable, secure, consistent and safe, even in edge cases.

Typical risk: Incorrect information, tone failures, weak fallback behaviour, missing AI disclosure

What we assess:

• Answer quality & factual accuracy
• Robustness against reformulations
• Handling of uncertainty & refusal
• Security & manipulation resistance

Knowledge Assistants (RAG)

For knowledge-based systems, not only the answer matters but also its derivation. We assess whether relevant content is found, correctly used and traceable to the right sources.

Typical risk: Wrong sources, outdated content, weak retrieval despite plausible answer, unauthorised access to confidential documents

What we assess:

• Retrieval quality & source fidelity
• Hallucination rate on knowledge questions
• Data leakage from knowledge base
• Document currency

AI Agents

AI agents must be trustworthy not only in what they answer, but in what they do. We test whether they plan, use tools and execute actions reliably, safely and within defined boundaries.

Typical risk: Unintended actions, error propagation across steps, prompt injection via external sources, irreversible actions

What we assess:

• Task completion & efficiency
• Tool usage & scope compliance
• Injection resistance & security boundaries
• Irreversibility of actions

Decision Systems & ML Models

Automated decisions in credit, HR or public administration are regulatorily high-risk. We assess fairness, accuracy and explainability – as the basis for compliance evidence.

Typical risk: Discrimination by protected attributes, model drift, lack of explainability towards affected individuals

What we assess:

• Fairness & bias per group
• Model accuracy & drift detection
• Explainability of individual decisions
• Regulatory compliance

Complex AI Landscapes (Enterprise)

Trustworthy AI at enterprise scale requires a unified quality framework, not a patchwork of isolated tests. We help assess portfolios of AI systems across departments, risks and governance requirements.

Typical risk: Inconsistent quality standards, missing governance across systems

What we assess:

• Portfolio inventory & risk classification
• Unified quality framework
• Governance & compliance evidence
• Continuous monitoring

AI Advisory

Not every organisation needs a test first. Sometimes what is needed first is clarity – about strategy, risks and the right next steps.

Typical risk: Missing AI strategy, unclear responsibilities, regulatory exposure

What we offer:

• AI Act Readiness Assessment
• Governance structure & AI policy
• Regulatory risk mapping
• Management briefing & roadmap

ISTQB Certified Tester - AI Testing

Acquire a basic understanding and skills for testing AI-based software systems and the use of AI technologies in testing.

ISTQB Certified Tester - Testing with Generative AI

A4Q AI Essentials

This e-learning and certification provides an introduction to AI compliance, ethics and risk awareness - no prior technical knowledge is required.

A4Q AI Foundation

Gain a comprehensive understanding of how generative AI can be used responsibly and effectively in accordance with regulatory requirements. You will acquire basic AI skills in accordance with the EU AI Act.

TestSolutions Originals - Basics of AI Testing 

Learn the basic concepts, terms and procedures of testing AI-based systems. It is suitable for anyone who is interested in AI testing and wants a quick and easy introduction to the topic.