Analytics

Analytics allows you to monitor AI usage, cost, and operational activity across your project or organization. It provides real-time visibility into token consumption, model usage, cost distribution, and feature level activity, ensuring AI usage remains measurable, transparent, and controlled.

The Analytics dashboard helps you track how these operations impact usage and spending.

Access Analytics :

• Click Analytics from the left navigation panel.

Switch Between Project and Organization View

• Use the Project / Organization toggle at the top-right corner to change the data scope.

• Project View

  • Displays AI usage for the selected project only.

  • Monitor AI calls within the project.

  • Track token usage and cost trends.

  • Review story and test generation activity.

    
• Organization View

  • Displays aggregated data across all projects.

  • Monitor enterprise-wide AI consumption.

  • Compare project-level spending.

  • Track overall adoption.

  • Useful for budgeting and governance discussions.

  

Filter by Date Range (Project & Organization Level)

• Use the date selector to adjust the reporting period for analysis.

• Available options include Last Day, 7 Days, 1 Month, All Time, and Custom Date Range.

• Applies to both project-level and organization-level views.

• All graphs and metrics automatically update based on the selected date range.

Project View Metrics

• Total Stories – Number of stories created.

• Test Cases – Number of test cases generated.

• AI Calls – Total AI engine invocations.

• Total Spend – AI cost and token consumption.

• Each Gap Analysis run, repository scan, story generation, or document processing counts as one AI call.

Analyse Usage Trend (Project Level)

• The Usage Trend graph shows token consumption over time for the selected project.

• Each data point represents usage for a specific day.

• Spikes indicate heavy AI operations, while flat lines indicate low activity.

• Helps identify peak usage days and unusual cost increases.

• Enables monitoring of usage patterns across the project.

• Large Gap Analysis runs or repository scans may cause noticeable spikes.

Monitor Budget Status (Project Level)

• The Budget Status section shows whether a spending limit is configured.

• If unlimited usage is enabled, AI operations continue without restriction.

• If limits are configured, usage may be governed by budget rules.

• This is important for financial control and governance.

Analyse Cost by Model (Project Level)

• The Cost by Model chart displays how AI model usage cost is distributed within the selected project.

• Helps identify which model (e.g., Claude Sonnet) is contributing most to project-level cost.

• Enables teams to evaluate cost efficiency at a project level.

• Supports optimization by adjusting model usage based on cost and performance needs.

Analyse Usage by Operation (Project Level)

• The Usage by Operation chart shows token consumption for different features within the selected project.

• Helps identify which operations (e.g., Phase 2 Story Validation, Phase 3 Code Quality Analysis) are consuming more tokens.

• Enables better tracking of feature-wise usage within the project.

• Helps optimize usage by reviewing high-consumption operations and reducing unnecessary runs.

Monitor Stories by Status (Project Level)

• Displays story distribution across workflow stages such as:

  • Draft

  • Approved

  • In Progress

  • Completed

• Connects AI-generated output with execution progress.

Team Activity (Project Level)

• Displays team member activity within the project

• Shows AI calls, tokens, tests, executions, and last active time

• Helps track contributions and engagement

Feature Adoption (Project Level)

• Shows how different features are used within the project

• Identifies most and least used features

• Helps improve feature utilization

Quality Overview (Project Level)

• Displays Total Executions, Pass Rate, Average Duration, and Defects Found

• Helps assess overall testing quality

• Most Failed Tests (Project Level)

  • Highlights test cases with the highest failures

  • Shows pass %, failures, and total runs

  • Helps identify unstable tests

• Model Performance (Project Level)

  • Displays model response time, usage, and cost

  • Helps evaluate efficiency and performance

  

Dashboard Settings (Project Level)

• Dashboard Widgets

  • Enable or disable widgets for the project dashboard

  • Customize dashboard based on needs

• Export Data

  • Export analytics data as CSV

  • Useful for reporting and sharing

• Preferences

  • Set default date range

  • Options: Last Day, 7 Days, 30 Days, All Time

Organization View Metrics

• Total Projects – Active projects in the organization.

• Total Tokens – Combined token usage.

• AI Calls – Total AI invocations.

• Total Spend – Aggregated AI cost.

• Token usage directly impacts AI spend.

Analyse Usage Trend (Organization Level)

• The Usage Trend graph shows token consumption over time across the organization.

• Each data point represents usage for a specific day.

• Spikes indicate heavy AI operations, while flat lines indicate low activity.

• Helps identify peak usage days and overall cost trends.

• Enables monitoring of usage patterns across multiple projects.

• Large operations such as Gap Analysis or repository scans across projects may cause noticeable spikes.

Review Organization Summary (Organization Level)

• Displays high-level enterprise metrics such as:

  • Total active projects.

  • Total AI spend.

• Useful for executive reporting and strategic oversight.

Analyse Cost by Model (Organization Level)

• The Cost by Model chart shows how spending is distributed across configured AI models.

• Helps you:

  • Identify high-cost models.

  • Evaluate model efficiency.

  • Optimize AI configuration.

  

Analyse Usage by Operation (Organization Level)

• The Usage by Operation chart shows how token consumption is distributed across different features.

• Operations may include Gap Analysis, Analyse Multi Repository, Story Generation, Test Case Generation, Code Generation, Document Processing, and Duplicate Detection.

• Helps identify which operations are consuming more tokens.

• Enables optimization by reviewing the frequency and scope of high-consumption operations.

Review Cost by Project (Organization Level)

• The Cost by Project chart shows how AI spending is distributed across different projects within the organization.

• Helps identify high-spending projects.

• Enables better budget allocation across projects.

• Ensures accountability by tracking project-wise AI usage and cost.

Team Activity (Organization Level)

• Displays activity across all projects

• Tracks AI calls, tokens, tests, executions

• Helps monitor team productivity

Feature Adoption (Organization Level)

• Shows feature usage across projects

• Identifies adoption trends

• Helps improve feature utilization

Dashboard Settings (Organization Level)

• Dashboard Widgets

  • Enable/disable widgets across organization dashboard

• Export Data

  • Export organization analytics data

  • Preferences

  • Set default date range for organization view

  

Why Analytics Matters

The Analytics page centralizes AI visibility in one dashboard. Instead of manually tracking cost or reviewing multiple modules, you can:

• Monitor token consumption.

• Track AI calls.

• Analyse operational distribution.

• Control spending.

• Maintain governance.

It supports both project-level optimization and organization-wide financial oversight, ensuring AI usage remains efficient, transparent, and aligned with business goals.