Prove your AI ROI.

Measure engineering before and after AI, and read the gain as capacity you did not have to hire. Then check it against the roadmap, and rank it against the companies setting the pace.

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NThe ROI statementLast 30 days · 350 engineers
Engineering capacity · one mark is 10 engineers
AI spend$226,900last 30 daysAcross 7 AI tools
Capacity added+326engineersNo new people
Capacity vs pre-AI1.93×per developerGraded per commit in ETV, not AI usage
On the payroll350 engineers
Ships like676 engineer-equivalents
Real peopleCapacity, not people
+326 engineers nobody had to hire

350 engineers on payroll. $226,900 bought 326 more. The same spend, split three ways:

Went to features$117,534· 51.8% of spend
New value
No stated outcome$65,120· 28.7% of spend
$110,274 (48.6%) went to a named objective
What it cost$696per added engineer

$226,900 ÷ +326 of capacity

Monthly, at today’s spend
01The math

How much capacity did AI add?

We measure every developer’s work in ETV. The gain shows up as engineers, team by team.

Real peopleCapacity, not people1 mark = 5 engineers
Capacity vs pre-AI
  • Platform team2.39× per developer
    105+146
  • Mobile team1.78× per developer
    82+64
  • Web app team1.68× per developer
    93+63
  • Data & ML team1.76× per developer
    70+53

Faster at what?

Split the work, or the number lies. Good adoption grows features while fixes and maintenance stay flat.

  • Features51.8%$117,534
  • Maintenance22.3%$50,599
  • Tests11.9%$27,001
  • Docs5.1%$11,572
  • Fixes8.9%$20,194

What did the capacity cost?

Divide the AI bill by the engineers it added. That’s your cost per engineer.

$226,900
spent on AI last month
7 AI tools, one month
+326
engineers of capacity it added
scored per commit in ETV
$696
per AI-added engineer
a month, at last month’s spend

Compare that with a hire. But cheap capacity is only a bargain if it went to the roadmap.

02Roadmap alignment

Was any of it on the roadmap?

The part everyone skips, in dollars. Connect Jira and every dollar lands in one of three buckets.

AI spend · last 30 days$226,900
  • Roadmap aligned$110,27448.6%
  • Aligned, off roadmap$51,50622.7%
  • Unaligned$65,12028.7%
Cut the number itself with smart model routing
03Benchmark

Is our AI transformation good enough?

One measurement for everyone. Your repositories, public or private, ranked against the companies setting the pace.

Effective engineering capacity

What 100 engineers at each organization now deliver, on their public repositories. The company on this page is measured the same way, and ranked with them.

Apr 2025 → Sep 2026
Real engineersAdded capacity
  • #1OpenAI19.3× since Apr 2025+1,828per 100 real
  • #2Microsoft4.9× since Apr 2025+390per 100 real
  • #3Cloudflare4.1× since Apr 2025+309per 100 real
  • #4Vercel3.3× since Apr 2025+231per 100 real
  • #5Google3.0× since Apr 2025+196per 100 real
  • #6Meta2.8× since Apr 2025+175per 100 real
  • #7The company on this page1.9× · 350 engineers+93per 100 real
  • #8UiPath1.2× since Apr 2025+20per 100 real

We recalculate the index every day, across the top 500 open source projects. The live data is at 500.navigara.com. To see why you rank where you do, and what moves you up, see AI Transformation →

04What’s next

Now make your team even better.

Every developer gets personal guidance on using AI better, built from their own work.

AI AdoptionSee how it works
AI ROI · in 5 days

Prove your AI ROI in five days.

Give us read-only access. Five days later you see what AI added in capacity, what it cost, whether the work landed on your roadmap, and where you rank.

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