How Commvault cut review cycle time with a reviewer that has already seen the attack
CodeAnt AI transformed our code reviews, reducing cycle time and helping us quickly fix quality and security issues.
Bhavyan Mehta
VP - Engineering, Commvault (NASDAQ:CVLT, $8B+ Market Cap)
Commvault protects data for some of the largest enterprises in the world, at an $8B+ market cap. Their engineering organization is large, distributed, and shipping constantly.
CHALLENGE
Protecting everyone else's data sets a high bar for your own code.
At Commvault's size, review quality is not a preference. It is the thing standing between a large distributed engineering org and a defect reaching customers who bought them for resilience.
WHY CODEANT
Most scanners solve throughput by generating noise.
Commvault wanted the defensive half of the platform working at full strength. Code reviews that catch real quality and security issues at the pull request, without slowing cycle time or drowning teams in false positives.
The reason ours catches what scanners miss is the offensive side. The engine has already proven which of these patterns is genuinely exploitable in the wild, so it knows what to escalate and what to leave alone.
WHAT WE RAN
The defensive engine in the pull request across the engineering organization, with escalation informed by what the offensive engine has proven in live environments.
WHAT CHANGED
