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Easily consolidate all explicit feedback from various channels in one place, while seamlessly capturing rich implicit feedback based on user behavior, intent, and interactions.
An advanced NLP algorithm analyzes user queries to accurately detect intent while also providing valuable insights into the emotional tone behind each interaction.
The satisfaction score evaluates how well a user’s intent has been fulfilled during interactions.
The Issues feature detects and classifies negative signals, highlighting instances where users' expectations aren't fulfilled, such as inaccurate or incomplete responses. It analyzes each issue to find the root cause and scores the impact to prioritize resolutions.
The Impact Score helps AI teams prioritize issues by assigning a 1-10 value based on problem frequency and root causes, with higher scores indicating more urgent issues.
The Knowledge Hole feature detects missing information in query-response-context triplets, assigning a 0-1 score. A value closer to 1 indicates a higher chance of missing data. Vector database detection is coming soon.
This feature places users in the centre of evaluation and optimization. By capturing both implicit and explicit feedback, it generates data that can are used to improve the model as well as enhance the evaluation set.
