Testing User-Exposed LLMs
Go live with confidence – without reputational risk
"Verify that your app fulfils the customer's need." Our testing service for user-exposed LLM applications ensures that brand image and functionality are uncompromisingly maintained. We prevent reputational damage caused by incorrect AI responses by evaluating your system from the actual customer perspective and rigorously putting it through its paces – across multiple modalities.
What we deliver
- Agent-supported testing operations: Highly automated selection, execution, and analysis of tests by AI agents ("agentization").
- Detailed insights: We objectively show what works from the customer's point of view and what doesn't – including root-cause analysis of malfunctions.
- Comprehensive safeguarding: Your chatbots and AI agents make no statements that could harm your brand, user trust, or your company.
- Multi-turn dialogue evaluation: Assessment in your specific product context across multiple dialogue turns.
- Multimodal tests: Functionality and safety not only for text, but across various modalities.
What sets us apart
| Typical automated tests | Our approach |
|---|---|
| Standardized security/compliance metrics | Realistic, product-specific scenarios |
| Single prompts | Complex multi-turn conversational flows |
| Technical benchmarks | Verification of the real customer need |
| Limited coverage | Up to 10x test coverage through agentization |
A result from practice: For one customer, agentizing the testing operation increased test coverage and throughput tenfold. ROI was achieved within a year; over the project duration, costs in the low seven-figure range were saved.
Frequently asked questions
"We already test with standard benchmarks." These usually check only basic security based on single prompts. Our evaluation uncovers problems that only arise in real conversational flows.
"Our developers have written their own prompt tests." Internal tests rarely capture the full spectrum of unpredictable customer behavior. We bring in the external customer perspective and objectively show where the application reaches its limits.
"We'd rather launch as a beta and learn from users." With user-exposed LLMs, even small glitches can cause viral reputational damage. With us, you go live with confidence – without putting your brand image at risk.