Demo · DeployMeter

Example data · Acme Engineering, a fictional team of 12 developers across 3 products. Your dashboard shows your own pull requests.

AI Multiplier

3.4x

across 3 products · P25–P75 2.1–5.6x

estimate · 67% measured

AI Time Saved

39.5 days

≈ 39.5 days (28.5 days–53.4 days)

estimate · 67% measured

DORA metrics

Deployment Frequency

6.10/day

+13.0% better

Elite

183 deploys · 30d

Lead Time

3.1h

−11.4% better

EliteP50 3.1hP75 14hP90 28h

to deploy

Change Failure Rate

5.1%

−10.5% better

High

Failed Deployment Recovery

6.5h

−11.0% better

MediumP50 6.5hP90 22h

Rework Rate

8.0%

−11.1% better

High

derived from: GitHub Deployments, GitHub issues (label: incident)

Trend by product

Direction over the period — per-product daily medians. Click a cell to expand.

DFDeployment Frequency

LTLead Time

CFRChange Failure Rate

FDRTFailed Deployment Recovery

All products

Checkout

Mobile App

Data Platform

Each value is the per-day average over the last 30 days

DORA performance bands

Where each product sits today against industry Elite / High / Medium / Low thresholds.

Focus

Better ←

EliteHighMediumLow

All products

Deployment Frequency (DF)

6.1/dElite

Lead Time (LT)

3.1hElite

Change Failure Rate (CFR)

5.1%Elite

Failed Deployment Recovery (FDRT)

6.5hHigh

Rework Rate (Rework)

8.0%High

Incidents2none open

Uptime99.9%no open SEV1 incident

View incidents →

derived from: GitHub Deployments, GitHub issues (label: incident)

Human vs AI contribution

Agent-authoredHuman, AI-assistedHuman only

PRs merged

75% with AI · 102 / 136

Commits

32% with AI · 259 / 803

Lines changed

69% with AI · 18,224 / 26,384

Agent-authored is a floor: only accounts classified as agents count. A commit is AI-assisted when it carries an AI co-author trailer; a PR, with all its lines, when at least one of its commits does.

Product comparison

Every product side by side across DORA, impact, volume, and quality.

	Impact		Volume		Quality
Name	AI Multiplier	Time saved	PRs (30d)	Developers	Bugs	DX	Review
All products	3.4x	39.5d	136	12	12	72	8.0h
Checkout	4.1x	16.5d	57	4	5	82	1.1h
Mobile App	3.0x	13.2d	45	4	4	71	6.3h
Data Platform	2.2x	9.9d	34	4	3	63	21.7h

Insights

Based on last 30 days of team activity

Updated 2d ago

Checkout leads on AI multiplier

aiMultiplier

Checkout's median multiplier is 4.1x, the highest of the three products, driven by short, well-scoped PRs with measured coding time across most of the team.

Action:

Share Checkout's PR sizing habits with Data Platform, whose PRs average 3x more lines.

Data Platform lead time is 20x Checkout's

leadTime

Median lead time is 62h against 3.1h. The gap is review wait, not coding: first review lands after 21h on average.

Action:

Set a review turnaround goal for Data Platform and route reviews to a second reviewer after 8h.

Failure rate is stable across the period

changeFailureRate

5.1% of deployments failed or were attributed an incident, barely moved from the previous period. Both SEV2 incidents were on Mobile App.

Action:

Keep the current release cadence; watch Mobile App's post-release checks.

Measured time on two thirds of developers

timeSaved

8 of 12 developers send measured coding time, so the time-saved band is narrow. The remaining 4 are on Data Platform.

Action:

Run npx deploymeter init on Data Platform's repositories.

Rework climbing on Mobile App

reworkRate

Rework reached 11% this period from 8%, concentrated in two files touched by every release.

Action:

Schedule a refactor of the two hotspot files before the next release train.