MongoDB Case Study — 50% Smaller PRs, Faster Cycles
How MongoDB Cut PR Size by ~50% and Improved PR Cycle Time with Optimal AI
Accelerate delivery, reduce costly rework, and improve engineering predictability with instant, full-context reviews and real-time insights across your entire eng org.
Reduction in PR Size
~50%
Smaller, reviewable changes → faster approvals + fewer merge conflicts.
Faster PR Cycle Time
~30%
Clear visibility into root causes → quicker unblocking.
Centralized Engineering Visibility
100%
From Jira to GitHub → one place for metrics teams trust.
"Optimal AI cut our PR review time nearly in half. Our team finally has visibility into what's slowing us down"
The Problem
Ad-hoc scripts, spreadsheet gymnastics, and limited adoption
Before Insights, visibility depended on manual effort across teams:
- Custom scripts maintained by different departments to extract data
- CSV/Excel workflows to transform and aggregate metrics
- Hand-built dashboards that required constant updates
"We were using scripts folks had written, dumping data into Excel, then producing higher-level dashboards… it was never easy to use."
The Solution
A single source of truth with Investments, Jira integration, and smooth UX
MongoDB adopted Optimal AI Insights to replace scripts and spreadsheets with a single, navigable system for engineering metrics.
- Unified dashboards: One place to see all metrics, with fast filters by team, person, and activity.
- Investments view: Clear allocation across technical debt, bug fixes, discovery, feature development, and more—by week, sprint, or quarter.
- Jira integration: Real-time view of effort by epics/initiatives, where work got blocked, and why—fueling root-cause discussions.
- Adoption-ready UX: Smooth switching between views and filters → better usage from engineers to directors and above—no code, no scripts.
The Results
PRs ~50% smaller, faster cycle time, and organization-wide adoption
The move to Insights produced noticeable, measurable improvements:
- PR size reduced by nearly 50%.
- Improved PR cycle time, driven by clarity on where/why reviews were slow.
- Higher PR quality, supported by focused conversations on large PRs.
- Improved deployment frequency, enabled by smaller, easier-to-review changes.
- Widespread adoption across levels (engineers → senior engineers → directors+).
The Impact in Numbers
Before and after metrics for MongoDB's Internal Tools team using Optimal AI Insights
| Metric | Before Insights | After Insights | Improvement |
|---|---|---|---|
| PR Size | Large, inconsistent, hard to review | Right-sized, easier to review | ~50% reduction |
| PR Cycle Time | Slow on large PRs; unclear drivers | Root-cause visibility; faster reviews | Improved cycle time |
| Engineering Metrics Access | Custom scripts + CSV/Excel + manual dashboards | Single system; fast filters by team/person/activity | One source of truth |
| Investment Visibility | Fragmented view of effort | Investments by tech debt, bugs, discovery, features | Clear focus areas |
| Jira Integration | Manual rollups; limited blockage insight | Effort by epic/initiative; blocked-work detection | Fewer surprises |
| Adoption & UX | Low adoption; hard to switch views | Engineers → Directors use shared dashboards | Org-wide adoption |
| Deployment Frequency | Held back by large PRs | Smaller PRs enable faster releases | More frequent deploys |
Cut cycle time by 50% and get visibility into engineering productivity
Start reviewing PRs faster, catching issues earlier, and shipping with confidence.