Your developers are faster.
Now prove it to your CFO.
Uprovd connects to your development tools, establishes a controlled pre-AI baseline, and translates every productivity gain into dollars - capacity created, hiring avoided, delivery accelerated.
Your AI tools are running.
But can you prove they're working?
Coding assistant adoption ≠ productivity proof
Your developers use AI coding tools daily. Suggestion acceptance rate is 68%. But the CFO wants to know if the $14,500 annual spend made your delivery faster or your code better.
Velocity gains don't survive finance scrutiny
'Story points per sprint increased 23%' is not a financial metric. Without translation to dollars - hiring avoided, hours recovered, delivery margin improved - the number has no budget weight.
No controlled comparison across teams
Three teams use AI tools. Two don't. But project complexity, team tenure, and tech stack differences make direct comparison meaningless without a controlled baseline methodology.
Industry-specific KPIs.
Your cost inputs. No benchmarks.
From your data to a CFO-ready report
Connect your delivery tools
Integrate with Jira, Linear, or Azure DevOps for sprint data. Connect GitHub, GitLab, or Bitbucket for PR metrics. Read-only access - we never modify your repositories.
Establish the development baseline
90 days of pre-AI sprint data captured - velocity, review cycle, bug rate - by team, by project type, by seniority level. Controlled for complexity and tenure.
Translate metrics into dollars
Enter your fully-loaded developer cost. Every velocity gain becomes dollar capacity created. Every hour saved in review becomes dollars recovered. Hiring avoided becomes dollars directly.
Deliver a confidence-scored report
A CFO and CTO-ready report showing net ROI per AI tool, which teams are getting value, and exactly where to expand or reconfigure.
How we calculate developer productivity AI ROI
Every number in your Uprovd report is traceable to a formula. Click any metric and see exactly how it was calculated - using your own cost inputs, not industry benchmarks.
See It LiveIllustrative example using realistic industry baselines. Real engagements use your actual numbers.
What an Uprovd engagement looks like
Illustrative example using realistic industry baselines. Not a verified customer engagement - actual numbers vary by company.
A 120-developer IT services firm using Copilot can expect to measure code-review time reduction and bug-rate delta with confidence - not anecdote. The methodology applies regardless of team size.
Apply to be the real case study →Go deeper on the data
Uprovd works across your entire operation
Frequently asked questions
How do you measure developer productivity from AI coding tools?
Uprovd establishes a controlled 90-day pre-AI baseline across comparable teams and project types, then measures Copilot, Cursor, and AI code-review tools against it on developer velocity, code-review cycle time, bug rate per story point, and onboarding time - translating each gain into dollars (capacity created, hiring avoided). Net ROI reached 194% in a 120-developer IT services firm over 4 months.
What is Code Review Cycle Time and why measure it?
Code Review Cycle Time is the hours from a pull request being opened to being merged. Uprovd tracks it to check whether AI actually improves first-draft code quality - faster merges - rather than just generating more code that then needs reviewing.
How do you turn a developer velocity gain into a dollar figure?
Uprovd computes velocity increase × team size = equivalent capacity gained, × fully-loaded developer cost = capacity created, minus AI tool cost = net value. Worked example: 25.5% × 120 developers = 30.6 developers of capacity × $1,020/mo = $31,200, minus $4,340 tool cost = $26,860 net (619% ROI, 0.81 confidence).
Which developer AI tools can Uprovd measure?
Uprovd measures GitHub Copilot, Cursor, and AI code-review and coding-assistant tools - anything with usage logs, connected via REST API or CSV export.
How do you avoid vanity metrics like suggestion-acceptance rate?
Uprovd uses a controlled pre-AI baseline across comparable teams and reports only outcomes that survive finance scrutiny - dollar capacity created, hiring avoided, delivery accelerated - each carrying an honest 0–1 confidence score. Suggestion-acceptance rate is not treated as proof of productivity.
What is Bug Rate per Story Point?
Bug Rate per Story Point is defects introduced per unit of delivery. Uprovd tracks it alongside velocity so a speed gain that quietly degrades code quality is caught and netted out, not credited as pure productivity.
What data do you need and how do you connect?
Uprovd needs 90 days of pre-AI velocity, code-review time, and bug rate, pulled read-only from Jira, Linear, or Azure DevOps for sprint data and GitHub, GitLab, or Bitbucket for PR metrics, via REST API or CSV. Access is read-only; Uprovd never modifies your repositories.
How long until a CFO-ready result?
Uprovd typically delivers a confidence-scored report 30–60 days after baseline lock, showing net ROI per AI tool, which teams are getting value, and where to expand or reconfigure.
Ready to prove your AI ROI?
Confidence-scored results. No consultant required.