The Risks Benchmarks Miss
As AI systems take on high-stakes work, a subtle error or an unhandled edge case costs more. Generic evaluations miss the failure modes that only someone with years in the field would know to look for.
Expert-Led Safety Evaluation
Our domain specialists test for what standard benchmarks don't cover:
Domain Risk Assessment
Experts flag the subtle risks, common misconceptions, and dangerous edge cases specific to their fields.
Red Team Evaluations
Adversarial testing by specialists who understand how systems fail in real-world applications.
Capability Evaluation
Testing how well the model handles expert-level tasks and reasoning problems.
Bias & Fairness Analysis
Subject-matter experts check for bias and fairness problems that a generalist would miss.
Evaluation Framework
Our experts score models on five dimensions:
- Accuracy: Whether outputs are correct for the domain
- Reliability: Consistency across different scenarios and edge cases
- Safety: Harmful outputs or reasoning patterns
- Alignment: Whether outputs follow the field's best practices and ethical standards
- Robustness: Performance under adversarial conditions and distribution shift
Why Expert Evaluation Matters
Benchmarks and automated metrics measure only part of how a model performs in the real world. Expert evaluation finds the subtle failures and edge cases that could do real damage once a model is deployed.
Find the failures before you deploy
Get a safety evaluation from experts who know where the risks in your field are.
Schedule Evaluation