Unlike other areas of the system, this is not a space for structural creation, but rather a center for observation, tracking, and auditing. It is a fundamental tool for detecting errors, validating results, understanding system behavior, and identifying the need for adjustments in other configuration areas.
Capabilities: What can the user do?
Within this module, you have the necessary tools to audit and control end-to-end service quality:
Visibility and Search: Consult active ongoing conversations and search for previous interactions.
History Auditing: Read incoming and outgoing messages in detail to understand the context of each interaction.
Status Control: Easily identify if conversations are open, closed, successfully resolved, or escalated/routed to another area.
AI Monitoring: Observe exactly how the Artificial Intelligence assistant (AI Employee) responded to different queries.
Flow Analysis: Verify if the service journey and configured rules executed as expected in specific cases.
Strategic Use: When should you review this section?
Constant monitoring is key to maintaining operational health. We recommend actively using this section in the following scenarios:
To control and supervise daily operations.
When an error or anomaly is detected or reported by users or the internal team.
To analyze a real response and evaluate how the AI is interacting in a specific context.
To verify if a new configuration (such as the creation of a new channel, stage, or employee) is functioning correctly in the live production environment.
💡 Corporate Best Practices
To ensure continuous improvement and excellence in customer service, apply these guidelines when auditing your conversations:
Post-implementation Audits: Make it a habit to review real conversations immediately after applying major changes to the platform's configuration to ensure stability.
Balanced Analysis: Dedicate time to analyzing both successful and problematic cases. Understanding why a conversation flowed well is just as important as correcting an error.
Qualitative Evaluation: When reviewing the AI's participation, do not simply confirm whether there was a response. Pay special attention to evaluating whether that response was truly useful, clear, and correct in resolving the user's needs.
Need to go deeper on something specific? Check out How the filters work in the Conversations section to search active or past conversations, Difference between qualified, escalated, and recovered contacts to understand the outcome of an interaction, How to leave feedback in conversations if you spot an error, or Visualization of the Artificial Intelligence Reasoning to understand why the AI responded a certain way.