Traditional AI testing is failing. Jo.E provides a rigorous, multi-layered solution that combines automated efficiency with irreplaceable human expertise to build safer, more trustworthy AI.
+22%
More Adversarial Vulnerabilities Identified
+18%
More Ethical Concerns Detected
-54%
Reduction in Human Expert Time
Automated first-pass analysis flags anomalies.
Specialized agents probe for specific risks like bias.
Experts make final judgments on complex issues.
Feedback loop improves the model.
Monitor in a limited environment before full release.
Jo.E's strength lies in complementarity. Different tiers excel at finding different risks. This chart shows the contribution of each tier in detecting specific types of vulnerabilities, proving that a multi-layered approach is essential for comprehensive coverage.
By automating the initial screening and testing phases, Jo.E focuses scarce human expertise only on the most complex and nuanced issues. This dramatically reduces the time and cost required for a rigorous evaluation.
The framework provides a consistent, multi-dimensional evaluation across different models. This radar chart from Experiment 1 benchmarks leading AI models, revealing distinct performance profiles that go beyond simple accuracy metrics.
Go beyond the static report. Use the Gemini API to explore the concepts of AI evaluation yourself.
Select a risk category and see what a real test prompt looks like.
Enter a term from the paper (e.g., Robustness) to get a simple explanation.