uReview: Scalable, Trustworthy GenAI for Code Review at Uber
Uber's code reviewers were overwhelmed by increasing code volume from AI-assisted development, with limited time to identify subtle bugs, security issues, or consistently enforce best practices — leading to missed errors, slower feedback loops, production incidents, wasted resources, and slow release cycles.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Developer submits change
When a developer submits a change on Uber's code review platform, uReview begins processing.
uReview analyzes over 90% of Uber's weekly ~65,000 diffs, maintains a sustained usefulness rate above 75%, saves approximately 1,500 developer hours per week (nearly 39 developer years annually), and delivers feedback within a median of 4 minutes per commit across all six monorepos.
What failed first
Third-party AI code review tools required GitHub (Uber uses Phabricator), generated many false positives and low-value suggestions, could not interact with Uber's internal systems, and cost an order of magnitude more than the internally built solution.
Results
Time savedover 90%
Volume~65,000 diffs per week
Cost replacedan order of magnitude less than what typical third-party tools charge