Port AI Builder: the first vibe coding built for platform engineering

Build a live dashboard for Cursor and Copilot adoption

See who actually uses Cursor and Copilot, what it costs, and how it changes delivery, with a live adoption dashboard in Port.

John Crowley
John Crowley
August 5, 2026
John Crowley
Pavan Belagatti
John Crowley&Pavan Belagatti
August 3, 2026
John Crowley
Pavan Belagatti
John Crowley&Pavan Belagatti&
August 3, 2026
Build a live dashboard for Cursor and Copilot adoption

Pavan builds a live AI adoption and impact dashboard in Port, then wires up an automation that tags every AI-created PR on its own.

Buying Cursor and Copilot seats is the easy part. Six months in, most engineering leaders can’t say who actually codes with them, which tool moves delivery, or how many paid seats go untouched each month.

Pavan’s walkthrough closes that gap.

The dashboard answers three questions

Once the data model is in place, the dashboard pulls real numbers into a single view.

Who’s using what: Cursor adoption across users, active Cursor and Copilot users, and licensed-versus-active counts that surface the seats you’re paying for but no one touches.

What it costs: total spends, daily cost trends, and AI model usage distribution.

What it changes: PR and lines of code created by each tool, usage trends over time, PR cycle time, and monthly impact on operations.

Every widget reads off your own catalog data, so a manager opens one page and sees adoption, costs, and delivery side by side.

Building it takes a data model and a resync

The setup in Port is quick. 

  • Connect GitHub through Port’s Ocean integration alongside Cursor and Copilot
  • Extend the GitHub pull request and Jira issue blueprints with a few AI-tracking properties
  • Update the integration mappings
  • Resync

From there you spin up a dashboard and drop in widgets. Pavan walks through the full step-by-step guide so you can build it in your own Port account.

An automation keeps the data honest

Here’s the part that makes the dashboard worth trusting: a workflow called auto-tag created PR. Every time a pull request lands, it detects which AI tool wrote it, tags the PR as Cursor or Copilot, and flips that developer’s record to active. No one tags PRs by hand, so the numbers stay accurate.

That closes the usual trap where a dashboard looks sharp on launch day and no one trusts it a month later. 

Try it on your own stack

Check out the full walkthrough video – it ends with a working dashboard and a live automation. Then build your own.

Find out whether your Cursor or Copilot spend is paying off. Sign up for Port, free.

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