Question 1

How do I know if my engineers are actually using AI effectively?

Accepted Answer

Most engineering leaders can't answer this — they can see who has a Copilot or Cursor license, but not whether anyone is using it well. Maestro measures agentic coding sessions directly: how engineers prompt AI tools, whether they verify outputs before accepting, how many turns a session takes, and whether the resulting code gets flagged in review. You get a session quality score per engineer that tells you who is upleveling and who is vibe coding.

Question 2

How do I justify my AI tool spend to the board?

Accepted Answer

Maestro tracks token usage and cost by team, tool, and feature — so you can show your CFO exactly what your AI bill is buying. Compare session quality and output across teams using Claude Code, Cursor, or Codex. Identify which teams are getting a productivity lift and which are spending tokens without shipping better software. One customer found a clear gap between two engineering pods: AI-adopting teams improved measurably over six months while non-adopting teams stayed flat — data that justified a $200K+ annual investment.

Question 3

What is vibe coding and why is it a problem?

Accepted Answer

Vibe coding is when an engineer accepts AI-generated code without understanding or verifying it — pressing accept on whatever the agent produces and moving on. It looks productive on the surface (PRs ship fast) but produces brittle, untested code that breaks in production. Maestro detects vibe coding patterns: blind approvals, sessions with no verification turns, high token usage with low review coverage. It flags risky sessions before they merge so you can coach engineers toward better AI habits.

Question 4

Does Maestro work with Claude Code, Cursor, and Codex?

Accepted Answer

Yes. Maestro measures agentic coding sessions across Claude Code, Cursor, Codex, and other AI coding tools. You can compare session quality, token usage, and output across tools to understand which ones your team uses most effectively — and make smarter decisions about which subscriptions to expand or cut.

Question 5

How is Maestro different from GitHub Copilot metrics or Cursor analytics?

Accepted Answer

Tool-native dashboards show you usage — acceptances, completions, active users. They can't show you whether that usage is producing good engineering. Maestro sits across all your tools and measures session quality: did the engineer give the agent enough context? Did they verify the output? Did the code hold up in review? You get a cross-tool view of how AI is actually changing the way your team works, not just whether they clicked accept.

Question 6

What engineering leaders is Maestro built for?

Accepted Answer

Maestro is built for CTOs, VPs of Engineering, and Engineering Managers at companies with 10–500 engineers who are serious about AI adoption. If you're responsible for an AI tools budget, need to report on engineering productivity to a board or exec team, or are trying to understand why velocity hasn't improved despite AI adoption, Maestro gives you the data to answer those questions.

See how your engineers use AI.Not just who's using it more.

Trusted by engineering leaders at:

Why Engineering Leaders
Choose Maestro

See how your engineers work — not just what they ship.

Alex Rockwell

Overview

Activity

Microservices authentication service deployed

API response time optimization completed

Mentored junior developers on platform architecture

The gap between your best and the rest doesn't have to widen.

Impact by Person

Not a vibe — a standard.

Impact of AI Adoption

Don't ship AI slop.

Team Status Updates

Security isn't just a feature—
it's our foundation

The dashboard is green.
The codebase is not.

Throughput is up. Reviews are paying for it.

They're using it. They don't trust it.

You can't tell who's growing.

Coverage is up. So are deleted tests.

Right now, you can see who's using AI.You can't see how well.