Control Center

The Control Center is an advanced analytics screen for people who want to understand the quality of the bot's work, not just the number of conversations handled. The main question this screen answers is: "What exactly should I do to make the bot help better?".

The five metrics at the top

• Total escalations: how many conversations were handed off to your team in the selected period.
• AI resolution rate: the percentage of conversations the bot closed on its own, without involving an agent.
• Average confidence: how confident the bot was in its answers (the higher it is, the less often it makes things up and the more often it hits the mark).
• Prevention rate: how effectively new knowledge base articles prevent further escalations on the same topic.
• Customer satisfaction: the share of positive ratings out of all rated conversations.

Each tile also shows the trend versus the previous period (green for up, red for down) with the percentage change.

Period filter

In the top right corner of the screen you can choose the number of days the metrics are calculated for: 7, 14, 30, or 90 days.

Charts

Weekly comparison

A bar chart juxtaposing the number of conversations resolved by the bot with the number of escalations across consecutive weeks. It shows whether the ratio is improving or getting worse.

Confidence breakdown

A donut chart that splits conversations into three groups based on the bot's confidence:

• High: the bot answered with high confidence. The ideal state.
• Medium: the bot answered, but wasn't entirely sure. Here it's worth checking whether the answers were accurate.
• Low: the bot barely managed an answer. Here it's usually worth expanding the knowledge base.

Escalation reasons

A table listing the reasons conversations were handed off to the team. Columns:

• Reason: a description of the category (e.g. "question outside the knowledge base", "customer asked for a human", "individual topic").
• Count: how many times this reason occurred.
• Percent: its share of the total number of escalations.
• Trend: the change versus the previous period.
• Average confidence: how confident the bot was when such a case occurred.

This table is the fastest way to identify what's missing in the knowledge base. If the biggest escalation reason is "question outside the knowledge base" and the topics keep repeating, adding a few articles will dramatically cut the number of conversations handled by the team.

AI insights

Below the charts you'll find AI insight cards, that is, automatic recommendations generated from an analysis of recent conversations. They may suggest, for example:

• specific topics worth writing an article about,
• low-performing articles to rewrite,
• recurring types of customer questions.

Each card includes a short description of the problem and a suggested action.

What's next

Once you've identified gaps in the knowledge base, go back to the Knowledge base section and add articles. After a few days, take another look at the Control Center to see how the prevention rate and confidence breakdown have changed.