Understanding the science behind EmpathyC's AI quality metrics
EmpathyC uses state-of-the-art AI evaluation techniques to analyze every customer support message your AI sends. Our methodology is based on peer-reviewed research and proven frameworks from leading AI safety organizations and customer experience experts.
Unlike traditional customer support metrics that only measure outcomes (like “was the issue resolved?”), we measure how customers feel during and after interactions. Research shows that emotional connection drives customer loyalty more than resolution speed.
Key Insight:
Studies show that 2/3 of customers who feel emotionally understood become repeat customers, regardless of whether their problem was immediately solved. This is why we focus on measuring both technical quality AND emotional impact.
EmpathyC uses GPT-4o-mini as an expert evaluator, following the “LLM-as-a-Judge” framework developed by leading AI research organizations. This approach has been validated in academic research and is used by major AI companies for quality assessment.
Every metric is based on peer-reviewed research and validated frameworks from leading AI safety organizations.
Works with any AI system (RAG, knowledge bases, etc.) because we evaluate based on conversation alone – no external documentation needed.
Our red flags are based on actual LLM failures from 2024-2025, including Character.AI, Amazon Alexa, and Microsoft Bing incidents.
We measure what actually matters: how customers feel. Traditional metrics miss the emotional dimension that drives loyalty.
