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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