These are real questions Aidealy answers - about your engineers and your AI agents, measured as one workforce, from the work they actually ship. You ask in plain English; Aidealy investigates and answers. No dashboards to configure, no queries to write.
Worried this is just another dashboard, or a stack-ranking tool?
It's neither - here's how we think about that.
Analyzed your engineering data · 7/7 steps
Productivity Rank · last 90 days
Productivity score · by engineer
90-day window · signed, uncapped
A negative score means cleanup outweighed shipped work.
Why these questions
Notice what these questions have in common: every one is an aggregate question - a ranking, a trend, a correlation across your whole workforce over time. Not "who touched this file last week" (that's digging anyone can do by hand), but "rank the engineers who caused the most production breakage this quarter" - the kind of question you could never answer by hand. This is what it looks like to actually measure engineering productivity in the AI era, instead of guessing at it.
And for the first time, the list includes your AI agents. They open pull requests, review them, even merge them now - a real part of how your software gets built - so Aidealy measures them as part of one workforce, right alongside your engineers.
Most of these had no good answer until now - not because the data didn't exist, but because no one could connect it and just ask. Browse them by what you want to understand.
A rank is where the investigation starts - not where it ends. The why is always one more question away.
The library
22 prompts shown
One signed score per engineer - the Aidealy Productivity Rank - built from what reaches production and its quality. The ranking is where the investigation starts, not where it ends.
Bugs are the hard case - they surface days or weeks after code reaches production. Aidealy measures how long code lasts, and traces a late-surfacing bug back to the pull request that introduced it - and to both its author and its approver.
A single 0–100 score for your actual source code - the Aidealy Code Quality Score, scored against the full Clean Code discipline - tracked as it moves over time and across every pull request.
Where work is slowing down - drawn from how long pull requests sit in review, get approved, and merge across your Git history. Review is the new AI-era bottleneck: code is written faster than it gets read.
What your team is actually doing with AI - the purpose behind each interaction, how much of the code is AI-assisted, and the new working patterns the AI era created - plus what your AI usage burns in tokens, by model and by engineer, all from the work itself. A token count is a fuel bill, not an achievement.
You've seen the questions. Talk to us and we'll get you set up to ask them of your own R&D: your engineers, your AI agents, and the code they ship together.
Talk to usAidealy is in early access - we're onboarding engineering teams now.