Cycle time you can act on
See where work actually stalls - coding, review, or deploy - so your 1:1s target the real bottleneck instead of a gut feeling.
For Engineering Managers
Find review bottlenecks, rework, and quality drift so you can unblock the team with evidence while keeping individual metrics in the right context.
Start with one repository. Keep your tools, workflow, and code ownership.
What you will see
Connect one repository to see where work waits, where changes come back, and whether AI-assisted output is improving durable delivery.
See where work actually stalls - coding, review, or deploy - so your 1:1s target the real bottleneck instead of a gut feeling.
Spot the reviewer carrying the whole team and the PRs waiting days for a first look, then rebalance before it becomes burnout.
Track revert rate, change failures and rework instead of raw commit counts, so shipping fast never means shipping fragile.
Built for your operating questions
See where work actually stalls - coding, review, or deploy - so your 1:1s target the real bottleneck instead of a gut feeling.
Spot the reviewer carrying the whole team and the PRs waiting days for a first look, then rebalance before it becomes burnout.
Track revert rate, change failures and rework instead of raw commit counts, so shipping fast never means shipping fragile.
Understand how much of your throughput is AI-generated and whether it holds the same quality bar as hand-written code.
Bring concrete, fair patterns to every 1:1 - not to rank people, but to unblock them and grow the ones who are quietly stuck.
Set team objectives against DORA and delivery metrics that leadership already trusts, then watch them move week over week.
From repo to baseline
Select one GitHub repository with read-only access. No workflow migration or developer instrumentation is required.
Backfilled pull request history reveals coding time, review latency, rework, and quality patterns without waiting months for data.
Use the baseline to rebalance reviews, reduce oversized changes, or coach through a recurring blocker with shared evidence.
Developer velocity
FeatureFactory treats developer velocity as a flow diagnostic. It combines complexity, review, quality, and rework context so managers can improve the system without creating incentives to game raw output.
Explore developer velocityThe hardest part of managing engineers is knowing where to lean in. You feel that reviews are slow and that one project keeps slipping, but you can rarely point to where the time goes. FeatureFactory turns that fog into a picture: which stage of the workflow stalls, which PRs sit unreviewed, and which changes come back as reverts.
That lets your 1:1s change shape. Instead of "how are things going?", you arrive with a specific pattern and a question. Our cycle time analytics break delivery into coding, review and deploy so you know whether to unblock a person, a process, or a pipeline.
None of this is about ranking. It is about giving every engineer - including the quiet ones - a fair, concrete path to unstick their work. See how we think about this in the code review best practices guide.
Review debt is invisible until someone leaves. One senior engineer quietly absorbs most of the team’s reviews, first-response times creep up, and large PRs pile up waiting for a slot. By the time it shows up in retro, the damage is done.
FeatureFactory surfaces review distribution and latency directly, so you can rebalance ownership and set a healthy PR-size norm. Pair it with our PR quality analytics to confirm faster reviews are not costing you quality.
As AI generates more of your diffs, review load only grows. Knowing how much throughput is AI-authored - and whether it meets your bar - keeps velocity honest. Explore the full picture on Measure or start with our guide to reducing PR review time.
Start free with real delivery history and leave with a concrete view of where the team can recover durable velocity.
Analyze one repo freeOne repository. No credit card. Read-only access.