Case Studies

Real Teams, Real Results

See how engineering teams use Jumbi to identify risks, remediate vulnerabilities, and ship AI-assisted code with confidence.

NF

NexaFlow

FinTech

Caught 14 critical security vulnerabilities before production launch

NexaFlow was preparing to launch a payment processing feature built primarily with AI assistance. Jumbi's assessment revealed 14 critical security issues including 3 hardcoded API keys, 2 SQL injection vectors, and 9 missing input validation checks.

82/100

Initial Risk Score

14

Critical Issues Found

3 days

Time to Remediate

18/100

Final Risk Score

Jumbi caught issues that our existing CI pipeline completely missed. It paid for itself on day one.

Sarah Chen, CTO
SF

Stackform

Developer Tools

Reduced technical debt by 60% across a vibe-coded MVP

Stackform's engineering team had rapidly built an MVP using AI coding assistants. Before scaling, they used Jumbi to assess the full codebase. The assessment revealed significant copy-paste antipatterns, phantom dependencies, and untested code paths.

71/100

Initial Risk Score

1,240

Files Analyzed

60%

Debt Reduction

~4 weeks

Time Saved

Jumbi's risk score of 71 made us pause and fix issues that would have cost us weeks of downtime.

Marcus Rivera, Lead Engineer
CB

CloudBase

Cloud Infrastructure

Integrated automated risk assessment into every pull request

CloudBase adopted Jumbi's CI/CD integration to automatically assess every pull request before merge. In the first month, the team blocked 23 PRs that exceeded their risk threshold, preventing an estimated 40+ production issues.

187

PRs Assessed

23

PRs Blocked

22s

Avg Assessment Time

40+

Issues Prevented

The AI pattern detection is incredibly accurate. Essential for any team using AI coding assistants seriously.

Priya Sharma, VP Engineering

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