One R&D workforce - humans and AI agents. Measured, ranked, finally clear.
Aidealy scores what your engineers and AI agents actually ship - developer productivity, code quality, and the bugs that follow - from real engineering activity, not tickets. No dashboards, no queries. You just ask.
Example questions
Ask anything about your R&D. In plain English.
You don't configure dashboards. You don't write queries. You ask - and you get an answer, not another chart to interpret.
Which engineers caused the most critical bugs in production last quarter - and who approved them?
Bugs & stabilityWhere is delivery slowing down - which reviewers keep pull requests waiting longest?
DeliveryWhich repos improved or declined in code quality this quarter?
Code qualityHow many hands-on hours went into each PR we merged this week - and does effort fall as AI share rises?
AI usage
Ask once - for one person, a team, or the whole R&D organization.
Blind spots
AI changed how software gets built. Your visibility didn't keep up.
When the board asks what AI is doing for engineering, the real answer lives in three places you can't see today: what a pull request costs you in AI tokens, what AI-assisted code is doing to your quality, and where delivery now actually slows. Code ships faster than ever, and more of it is opened, reviewed and merged by AI agents. The old way of measuring R&D never kept pace:
What are we actually spending on AI - by model and by engineer - and what did each merged PR cost?
Why are we seeing more bugs since AI - and which PR authors and approvers are behind them?
Where is delivery slowing down now - and which teams or reviewers are behind the wait?
Over the last quarter, which engineers caused the most production breakage - and who kept approving it?
Is AI actually making us more productive - or just faster at producing work that has to be fixed later?
What did that feature really cost to build - in hands-on hours, AI share, and AI spend?
Until now, the honest answer to most of these was a guess.
And when we count tokens, we count them as a fuel bill - cost is context, never the measure of anyone's worth.
The rank
A rank is where the investigation starts - not where it ends.
Aidealy gives every engineer a single, signed score -
the Aidealy Productivity Rank. But the number is an invitation to look closer, never a verdict. It rewards real contribution and subtracts for the bugs and rework left behind - so it can even go negative when someone's output costs more than it adds. See it one engineer at a time, or as an average across each team.
Analyzed your engineering data · 7/7 steps
Productivity Rank by team · last 90 days
Platform leads on average. Backend has your top engineer, Sarah Wong, yet the lowest team average - the place to ask why.
Productivity score by team
90-day window · average score per engineer
| Team | Avg score | Top engineer |
|---|---|---|
| Platform | 94 | Alice Chen |
| Frontend | 88 | Marcus Johnson |
| Backend | 75 | Sarah Wong |
You can't improve what you can't measure. Aidealy gives you the measurement - clearly, fairly, and with the why one question away.
Methodology
We measure the work, not the paperwork.
Tickets are never fully up to date. Code is the only honest record of what actually shipped - so that's what we measure. What never reaches production doesn't count; what breaks it counts against you. For better and for worse.
What productivity IS - how engineers and agents:
- Contribute to production - in volume and quality
- Make the most of their IDE working time
- Lift their team's efficiency
- Take work end to end
What productivity is NOT:
- Not measured:How many tokens they consume
- Not measured:How many lines of code they write
- Not measured:How many tickets they close
- Not measured:How many hours they log
Each rank is built from four signals, over rolling 7-, 30-, and 90-day windows:
- 1Code you author
- merged to production, weighed by quality, with a penalty when it had to be fixed soon after.
- 2Code you review
- how much, and how carefully, with a penalty when a pull request sat waiting on your review.
- 3Breadth
- how many languages and repositories you work across.
- 4AI in your editor
- how effectively you put AI to work, like running several sessions in parallel instead of waiting on one (a minor signal - what ships still leads).
These four signals combine - through our own formula - into one signed score:
the Aidealy Productivity Rank. We show you every signal that feeds it; the way they're weighed into a single rank is ours.
Tokens and hours are tracked - but they never feed the rank. A token count is a fuel bill, not an achievement.
One thing we deliberately don't score: the business value of the work. That's a judgment - yours - not a metric.
We measure the execution.
The lenses
One platform, many lenses - each valuable on its own.
Each of these answers a real question by itself.
Code that lasts - and where bugs come from
How long your code survives in production, and a way to trace any bug back to the pull request that introduced it - and the author and reviewer who let it through.
The Aidealy Code Quality Score
A single 0-100 score for your source code, measured against the full Clean Code discipline - production and test code judged separately, tracked across every pull request.
What your team uses AI for - and what it costs
Aidealy classifies every AI-assisted session by what each team uses AI for - and shows what the AI cost: by model, by engineer, and per pull request. Cost is context, never used to rank anyone.
Code review - and where it stalls
For every merged pull request an engineer reviewed, Aidealy credits the real review work and counts the cost when a PR stalls or an approval lets broken code through - so you see which reviewers and teams keep delivery moving.
Each stands on its own. Three of them - your code's quality, how long it lasts, and how your team reviews - feed the Productivity Rank; the fourth is there to inform you, never to rank anyone.
Integrations
Connect your stack - and get answers in your first day.
Aidealy plugs into the tools your team already uses. No new workflow to adopt, and no empty dashboard to wait around for.
- Git:GitHub, GitLab, Bitbucket, and Azure DevOps.
- AI in the editor:Cursor and Claude Code.
Value from day one
The moment you connect, Aidealy backfills 1-3 years of your Git history, plus your engineers' Cursor and Claude Code history - all collected, enriched, and ready to query.
- Your Git activity and code-quality scoring, ready within your first 24 hours.
- Your engineers' Cursor and Claude Code activity, added as the extension rolls out.
- No empty dashboard, no waiting weeks for data to accumulate - once you're connected, your own R&D is there from your first day.
Security
Built to be trusted with your most sensitive asset.
Choose your data residency - EU or US - and your data is stored in the region you pick.
Every customer is isolated at every layer that matters: your data lives in its own separate per-tenant stores, encrypted under a key provisioned just for your tenant that can never decrypt another customer's data - and your AI calls run under an AI-provider credential dedicated to your tenant, never a single shared platform key. Every workload runs for one tenant at a time, scoped so it can reach only your data and keys, with row-level security protecting your core records. Everything is encrypted in transit and at rest. Single sign-on (SAML SSO) is included on every plan.
We analyze your Git history through short-lived access and don't keep a copy of your repositories. Our security, privacy and AI-governance program is designed to meet SOC 2, ISO 27001, ISO 42001, and GDPR requirements.
See what your R&D is really doing.
Tell us what you're trying to figure out, and we'll get you set up to answer it on your own R&D: your engineers, your AI agents, and the code they ship together.
Talk to us