AI engineering metrics
Turn AI hype into measurable engineering outcomes
Paceflow connects AI assistant activity with git outcomes so engineering leaders can see whether AI tooling investment is accelerating delivery, improving quality, or just burning budget - and act on it.
AI investment
Outcome report
Know whether AI is actually helping
Every engineering team is adopting AI coding tools. Most can't tell you if it's making them faster. Paceflow changes that by connecting AI assistant sessions directly to delivery outcomes - commits, PRs, merges, churn - so you can answer the real question: are we shipping better software because of AI, or just spending more to produce the same?
This isn't about tracking individuals. It's about understanding whether your AI investment is moving the metrics that matter to your business.
Four ways to look at AI's impact
Delivery Acceleration
Are AI-assisted commits reaching PRs and merging faster? See PR reach rate, mainline reach, merge rate, and shipped lines of code.
Use this to answer: is AI helping us ship more, or just generating more activity?
Code Quality
Is AI-assisted code holding up in production? Track churn rate, bug-after-merge rate, and revert rate.
Use this to answer: are we shipping faster but accumulating tech debt?
Session Efficiency
Are AI sessions productive? See prompts per session, debug-loop rate, no-output rate, and time to first change.
Use this to answer: where should we invest in training or better prompting patterns?
Cost ROI
Is the money worth it? Track estimated spend, cost per session, cost per accepted session, cost per shipped line, and cost per mainline commit.
Use this to answer: should we expand licenses, switch tools, or change workflows?
What this data lets you do
Justify AI tooling spend to leadership.
When the CFO asks what you are getting for AI licenses, you have numbers, not developer testimonials.
Make adoption decisions with evidence.
Expand licenses when the data shows delivery acceleration. Pull back when churn or no-output rates signal the tool isn't sticking.
Target training where it matters.
If debug-loop rates are high across the team, that's a prompting workshop. If one cluster struggles with no-output sessions, that's a pairing opportunity.
Improve code review priorities.
Churn and bug-after-merge data tells you where AI-generated code tends to break. Focus review attention where it actually matters.
Benchmark across teams and tools.
Compare delivery velocity, quality, and cost across teams or AI models, then standardize where the evidence is strongest.
Have better 1:1s.
Weekly trends and risk flags give you conversation starters grounded in data, not vague check-ins about how AI tools are going.
How it works
Minutes to data, no workflow disruption
Engineers run a lightweight CLI sync. No code changes, no CI integration, no workflow disruption. AI assistant history and git metadata flow into Paceflow, and the Engineering Analytics dashboard updates within minutes.
Signals that prompt action
The dashboard automatically surfaces patterns worth your attention - not to police, but to act on.
Stale sync
A team stopped reporting. Might mean the sync needs a refresh, or AI adoption stalled.
High debug-loop rate
Sessions stuck in fix-retry cycles. Training or tooling adjustment needed.
High no-output rate
Sessions producing nothing accepted. Wrong tool, wrong workflow, or skill gap?
High churn
AI code later removed. Quality issue worth reviewing before it compounds.
Low PR reach
AI commits not making it to PRs. Workflow bottleneck or integration gap.
Each signal points to a specific action - not just a problem to observe.
Measure what matters. Skip what doesn't.
Get the engineering metrics missing from your AI tooling stack.