Rain Innovation & Technology Culture

The most rewarding moments for me come when new features and improvements ship. It’s so satisfying to see them directly improve the lives of our customers. Internally, there’s nothing better than watching something go from an idea, to a design, to production and, finally, launch.

What’s your rule for fast, safe releases — and what KPI proves it works?

Our rule is that humans are responsible for what ships. If something breaks, AI takes zero blame. We have to own it.

In practice, this means we plan, iterate, and implement with Claude Code. A team of agents runs pre-commit review; CodeRabbit and Codex act as independent checkers. On push, our bot, Osmosis Jones, picks the right reviewer based on team and PR context, and CodeRabbit and Claude run another review pass alongside them. We share review skills across the team, and each of us writes our own for the things we personally want to catch.

We measure two numbers to keep us honest. The first is whether the median PR open-to-merged time is under four hours, and the second is whether the revert rate is near 1 percent. For context, we grew our headcount by eight times in one year without quality slipping. Most teams watch stability degrade through hypergrowth. Ours didn’t. The reason isn’t just AI; it’s the critical thinking around it.

A perfect coding agent without context still builds the wrong thing. So, we brainstorm, plan, review the plan, and pressure-test AI’s assumptions before a line gets written. Communicating clearly with AI is a skill that matters more as models get better.

Which standard or metric defines “quality” in your stack?

The number I care most about is card authorization approval rate on legitimate transactions. It’s a hard metric in fintech because a card swipe either goes through or it doesn’t. For more than a year, we’ve held it between 95 to 97 percent while authorization volume grew by 21 times. Every AI-assisted change ships against a live system, moving billions in annualized card volume across hundreds of active programs.

Revert rate is the other half of the picture, which is the share of PRs we have to roll back, and it sits near 1 percent. The two numbers together tell the real story: AI lets us ship five times more code without moving auth approval and without raising reverts. If either one was drifting the wrong way, we’d be failing; velocity doesn’t count if the system isn’t boringly reliable underneath.

Name one recent AI/automation shipped and its impact on the team or business.

I recently built a daily AI skill that monitors for fraud patterns across our card program. It pulls from BigQuery, metrics and logs, runs a 24-hour trend check every morning, and posts a summary to a Slack channel. We already have real-time monitors for urgent things, and this one answers “what moved overnight, what’s trending, and what we should be watching.”

In its first weeks, the skill surfaced the kind of slow-moving patterns our real-time systems aren’t designed to catch — trends you can only see with a 24-hour lens. And because every daily summary is saved, we can zoom out to weekly or monthly views and catch patterns that evolve across time. Now, our risk team starts their morning with that context instead of reacting to whatever just pinged.