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Model evaluation ensures AI systems are accurate, reliable, and aligned with business goals. Evaluations can include automated metrics, human review, and stress testing for safety risks.
The process of measuring model performance, quality, and safety using defined metrics and test sets.
We help teams go from definitions to deployed workflows—safely and fast.
Teams use offline benchmarks, human evaluation, and production monitoring to verify performance. The right mix depends on the stakes and the domain.
Choose metrics that align with user outcomes. For example, summarize accuracy and citation quality for knowledge work, or precision/recall for classification tasks.
High-impact systems should be evaluated continuously with automated checks and periodic human audits.
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