Insight dashboards
Why it matters
Engineering teams need visualizations they can share, revisit, and act on whether it's a board-ready report on engineering health, a team-level delivery dashboard, or an AI adoption ROI breakdown. Building these manually takes hours and the results are outdated by the time they're shared. Modern approaches let you generate and maintain dashboards both inside Port and through AI-powered tools.
How Port helps
Port offers two complementary approaches to insight dashboards:
Built-in dashboards & widgets: Create custom dashboards directly in Port using configurable widgets. Visualize metrics across services, teams, and the entire organization. Dashboards update in real time as data flows in from your integrations no manual refresh needed. Everything lives in one place alongside your software catalog.
Custom widgets: Go beyond built-in widgets with custom widgets that let you build fully tailored visualizations using your own code. Embed bespoke charts, calculators, or interactive components directly into Port dashboards perfect for organization-specific metrics or visualizations that don't fit standard widget types.
AI-generated reports: Use Port's MCP integration with tools like Claude or Lovable to generate polished reports and visualizations through natural language. Ask for a "monthly DORA report for the platform team" and get a structured document with charts, tables, and narrative summaries. Build interactive web dashboards in Lovable that pull live data from your context lake ready to share with leadership.
Example scenarios
Scenario 1: Built-in dashboards and widgets
An engineering leader sets up a DORA metrics dashboard in Port with standard widgets showing deployment frequency, lead time, change failure rate, and MTTR — broken down by team. The dashboard updates in real time as data flows in from GitHub and PagerDuty. Every Monday, team leads open Port and immediately see where their team stands compared to organizational targets.
Scenario 2: Custom widgets
A platform team wants to correlate deployment frequency with infrastructure costs, a metric no standard widget supports. They build a custom widget that pulls cost data from their FinOps tool and overlays it with deployment data from Port's catalog. The widget is embedded directly in the platform team's Port dashboard, showing cost-per-deployment trends per service. Leadership uses it to identify services where optimization would have the biggest impact.
Scenario 3: AI-generated reports via Port MCP
A VP of Engineering needs a board-ready report on engineering health across 12 teams. They ask Claude via Port MCP "Create an engineering health report for Q1 covering DORA metrics, scorecard compliance, and AI adoption by team." Within minutes, they have a formatted report with visualizations built in Lovable as an interactive web page that they share directly with the board.