Digital Media 50+ engineers

#1 Online Media Company in Turkey

How a leading media company measured the real impact of AI coding assistants on their engineering output.

AI Engineering Intelligence
Q1 2026
Cycle time -61%
before 4.1d
after 1.6d
AI code ratio +100%
before 0%
after 38%
Rework rate -50%
before 18%
after 9%
12-week trend trending up
"iftrue gave us the AI adoption metrics we were missing. We can finally tell which teams are getting real lift from Copilot and Cursor, and which ones are just shipping more churn."

Engineering Director

#1 Online Media Company in Turkey

The Challenge

As this media leader's engineering team grew, AI coding assistants spread across squads faster than anyone could measure. Leadership could not tell which teams were getting real lift from Copilot and Cursor and which were just shipping more churn. Gut-feel debates replaced data.

The Solution

With iftrue's AI code attribution and delivery metrics, every pull request is tagged with its AI origin and measured against cycle time, rework, and bug ratio. Engineering leaders now see per-team AI lift next to quality, and the Slack assistant surfaces anomalies before they become sprint problems.

The Results

Six months after rollout, compared to the pre-iftrue baseline.

Cycle time

-61%
Before 4.1d
After iftrue 1.6d

Median time from first commit to merge, across all tracked repos.

AI code ratio

+100%
Before 0%
After iftrue 38%

Share of merged code attributed to AI assistants. Unknown before iftrue.

Rework rate

-50%
Before 18%
After iftrue 9%

PRs requiring significant rework within 7 days of merge.

Ready to transform your engineering organization?

Start your journey to data-driven engineering management. Book a demo to see how iftrue can help your team.