> ## Documentation Index > Fetch the complete documentation index at: https://koreai-content-gov.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Usage Insights This document covers the core monitoring stack in Analytics and Insights: the Executive Dashboard for high-level KPIs and trend analysis, the Analytics page for operational drill-downs and session-level debugging, and Billing and Usage for cost governance. ## Dashboard The **Dashboard** page — titled **At a Glance** in the interface — provides a pre-built executive overview of your AI agent program. It aggregates key performance indicators, trend visualizations, outcome breakdowns, and a conversation-level drill-down into a single view, giving stakeholders immediate visibility into agent performance without any configuration. Use it as the starting point for daily operational checks or to prepare data for leadership reviews. Navigation: **Project** → **Insights** → **Dashboard** **Date range selector**: A toggle in the top-right corner lets you select **7d**, **30d** (default), or **90d**. Changing the range refreshes all KPI cards, charts, and conversation data on the page. Dashboard ### KPI metric cards Six metric cards appear at the top of the page. Each card displays a primary value and, where applicable, a sub-label with supporting context. Warning icons appear on cards where the metric falls below expected thresholds. | Metric | Description | | -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Conversations** | Total conversation count in the selected period. The sub-label shows how many conversations pipelines evaluated. | | **Containment Rate** | Percentage of sessions the agent resolved without human escalation. A warning icon appears when the rate drops below the platform-configured threshold. The sub-label shows the resolved count versus the total evaluated. | | **Quality Score** | Aggregated quality score across all evaluated conversations, derived from pipeline evaluations. Displays a dash (–) if no quality pipeline has processed data yet. | | **Avg Sentiment** | Average sentiment score across all conversations in the period. Displays a dash (–) if the sentiment pipeline has not yet run or if the platform lacks sufficient data. | | **Cost Savings** | Estimated cost savings compared to human-handled conversations. A positive value indicates cost-efficiency; a negative value indicates the program has not yet reached cost parity with human support. | | **Escalation Rate** | Percentage of sessions escalated to a human agent. The sub-label shows the escalated count versus the total evaluated. | ### Views Below the KPI cards, four views organize the dashboard's detailed data: | View | What it shows | | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **Overview** | A **Conversation Volume & Containment Rate** trend chart plotting daily conversation count and containment percentage over the selected period. Below the chart, an **Outcome Distribution** horizontal bar breaks down conversations into three categories: Resolved (green), Contained-unresolved (amber: the AI handled the conversation but resolution remains uncertain), and Escalated (red). Each segment shows its count and percentage. | | **Trends** | Longitudinal trend lines for all core KPIs — conversations, containment, quality, sentiment, cost savings, and escalation — over the selected period. Use this view to identify sustained improvements or regressions across multiple metrics simultaneously. | | **ROI** | Return-on-investment metrics comparing agent costs to human-handled baselines. Includes cost-per-conversation, total savings, and efficiency ratios. To set values, navigate to the [Cost Settings](#cost-settings) section. | | **Conversations** | A filterable, sortable list of individual conversations with columns for status, outcome, agent name, duration, and key metrics. Click any row to open the full conversation detail. | ### Cost settings The **ROI** tab estimates the savings from automating conversations by comparing AI cost against the equivalent human cost. Select **Cost Settings** to open the **ROI Cost Settings** dialog, set the cost assumptions the ROI metrics use, then select **Save**. | Field | Description | | ------------------------------------------- | ---------------------------------------------------------------------------------------------- | | **Human Cost per Conversation (\$)** | Cost of handling one conversation with a human agent. | | **Human FTE Monthly Cost (\$)** | Fully loaded monthly cost of one human full-time equivalent. | | **Avg Human Handle Time (minutes)** | Average time a human agent takes to handle one conversation. | | **Estimated AI Cost per Conversation (\$)** | Per-conversation AI cost used as a fallback when actual LLM cost tracking data is unavailable. | *** ## Billing and usage The **Billing and Usage** page provides a consolidated view of your project's billing-unit consumption, enabling finance and operations teams to monitor spend, forecast costs, and ensure the project stays within allocated budgets. The platform calculates billing data from materialized processing batches, so there may be a short delay before the most recent usage appears. **Navigation**: **Project** → **Insights** → **Billing and Usage** Use the time range selector to view usage for the last **7 days**, **30 days**, or **90 days**. The page displays aggregated billing-unit counts, breakdowns by resource type (LLM calls, token consumption, pipeline executions), and trend lines showing usage over the selected period.