Every, a company focused on AI evaluation, is developing personalized benchmarks for its employees to assess AI model performance on specific job tasks. CEO Dan Shipper explained that these custom evaluations, designed by individuals like editor Kate Lee and head of evals Mike Taylor, go beyond general benchmarks to measure how well models handle tasks such as copy-editing or creating presentations according to personal standards. This approach aims to create a feedback loop where model failures inform improvements to the evaluation criteria, ultimately helping users identify the best AI models for their specific needs. AI
IMPACT Personalized benchmarks could improve AI model selection for specific enterprise workflows.
RANK_REASON Company is developing a new internal tool/methodology for evaluating AI models.
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