Testing AI Products Requires
AI Testing Expertise

What does correct mean for your AI? We define accuracy thresholds, safety requirements, and forbidden output types before any testing begins.

We create a comprehensive dataset of normal, edge case, adversarial, and boundary prompts specific to your use case and user base.

LLM-as-judge scoring, embedding similarity, and custom rubrics evaluate hundreds of outputs automatically and consistently.

Manual adversarial testing for prompt injection, jailbreaks, and safety failures that automated tools cannot reliably catch.

Output distribution analysis across demographic groups and input variations to identify systematic biases in model responses.

Detailed findings with prompt engineering and guardrail recommendations, plus CI/CD integration for ongoing regression testing.