They optimize workflows.
We measure the work.
LinearB routes PRs faster and tracks your DORA metrics. That was the right problem to solve before AI. Today, quality decisions happen in the conversation between engineer and agent — before the PR is ever opened. Optimizing the workflow around a PR you can't see inside is the wrong lever.
Session quality is upstream of workflow efficiency.
LinearB Optimizes the Workflow.
Maestro Measures the Work.
LinearB was built to eliminate friction in the DevOps pipeline — auto-assign reviewers, track DORA metrics, speed up the PR process. That's real value for the workflow around the code.
But AI coding tools changed where quality decisions happen. They happen in the session — the conversation between engineer and agent where problems are framed, proposals evaluated, outputs verified. LinearB's gitStream can route a PR to the right reviewer in seconds. It can't tell you whether the engineer who wrote it accepted 40 suggestions they didn't understand. Maestro was built for exactly that.
The Git Workflow vs. Inside the Work
LinearB sees the Git workflow: PR open and close times, review assignment, cycle time, deployment frequency. It's accurate workflow telemetry. It's just not where the AI-era quality failures originate.
Maestro sees inside the work: how your engineers are using AI tools, whether they're developing craft or stuck in copy-paste loops, where the quality gap is opening between your best and worst AI users.
Two Philosophies
What LinearB Measures
- DORA metrics: deployment frequency, lead time, MTTR
- PR cycle time, review assignment, gitStream automation
- Git-centric activity: commits, branch patterns, throughput
- Workflow efficiency around the PR boundary
Useful. But it stops at the PR boundary.
What Maestro Measures
- How your engineers use AI tools — quality, not just volume
- Who is developing AI craft vs. stuck in copy-paste loops
- Whether your team's skills are improving or plateauing
- What your best engineers do — made visible and teachable
Maestro sees what's inside the work.
Read the full argument.
Eng metrics are dead. →See inside the work
Join engineering leaders who moved past optimizing workflows and started measuring craft. See how Maestro surfaces intelligence across your org — who's using AI well, who needs coaching, and what your standards actually are.
Trusted by engineering leaders at Runway, AimLabs, and more