150 Repos, $34 in Review Costs, 147 Issues Fixed
How an enterprise Java team reviewed 905 pull requests across 150 repositories for $34 total — and resolved 147 issues that would have shipped.
The Team
A 12-person engineering team managing a large Java-based platform across 150 active repositories in Bitbucket. The team ships 150–260 pull requests per month and uses JIRA for ticket tracking.
The Challenge
With 150 repositories and a steady stream of PRs, keeping review quality consistent was becoming harder. Senior developers were spending significant time on reviews, but some issues were inevitably slipping through — subtle bugs, security oversights, and cases where code changes didn't fully match what the ticket had specified.
The team wanted an automated safety net that would work across every repo without adding friction to their workflow.
The Solution
The team connected all 150 repositories to Korekt in a single onboarding. Using Bitbucket webhook integration, every pull request now gets an automated AI review before human reviewers look at it. Korekt analyzes the code, checks it against the linked JIRA ticket requirements, and flags potential issues by severity and category — giving reviewers a head start on what to focus on.
Results
Over 5 months of production usage:
| Repositories connected | 150 |
| Pull requests reviewed | 905 |
| Potential issues identified | 1,961 |
| Issues resolved by the team | 147+ |
| Total AI review cost | $34 |
| Average cost per review | $0.03 |
The team now has visibility into code quality patterns across the entire codebase — not just individual PRs — and uses the data to identify areas for improvement and track progress over time.
Examples of Issues Caught Early
Korekt catches the kind of subtle issues that are easy to miss in manual review:
A cache timeout was set to 30 seconds when the JIRA ticket explicitly specified 30 minutes. The PR had been approved by a human reviewer — Korekt flagged the discrepancy before it shipped.
A Liquibase changeset ID was modified in an existing file. If already deployed, this would have caused Liquibase to re-run it as a new changeset — a potentially destructive operation on production data.
A builder method returned null instead of this, meaning any fluent chain using that builder would fail at runtime — a problem that wouldn't surface until the specific code path was hit.
A cache lookup was hashing queries with a timestamp, making every key unique — the cache would never return a hit, silently defeating its entire purpose.
The Ticket Compliance Difference
What the team found most valuable was Korekt's ability to check whether the code actually implements what the ticket specified. By pulling in context from JIRA, Korekt flags cases where a PR technically works but doesn't meet the requirements — a gap that traditional code review tools don't address.
"We connected 150 repositories and every PR gets reviewed automatically. The ticket compliance check is something we haven't seen anywhere else — it catches cases where the code technically works but doesn't match what was specified."
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