Evaluations are the foundation of AI governance. Before any virtual employee gains autonomy, before any AI-generated output reaches production, evaluations verify that performance meets your standards. The evaluation framework supports automated testing, human review workflows, and statistical analysis of AI behavior over time — creating the evidence base that justifies increasing autonomy.
Define test suites that evaluate AI outputs against expected results. Run them continuously to detect performance degradation before it impacts operations.
Some evaluations require human judgment. Structured review workflows route AI outputs to appropriate reviewers with clear rubrics and scoring criteria.
Track evaluation metrics over time. Identify trends, detect drift, and measure improvement. Data-driven decisions about AI capability expansion.
Industry-specific evaluation benchmarks provide baseline comparisons. Know how your AI performance compares to established standards in your domain.
Evals works in concert with other layers in the intelligence stack — each connection amplifying the capability of both components.
Build the evidence base for AI trust. Systematic evaluations provide the data leadership needs to confidently expand AI autonomy — and the early warning system to pull back when performance doesn't meet standards.
Discover how Evals fits into your enterprise intelligence strategy.
Request a Demo →