Customer Success Story: How Stable Money Accelerated Code Reviews with Panto AI

About the Client

Stable Money is India’s first digital fixed-return investment platform and has been growing rapidly. As their engineering team scaled, so did the complexity and volume of code changes. To maintain high code quality while improving speed, Stable Money adopted Panto AI for intelligent code review automation.

The Challenge

With an increasing number of pull requests (PRs) and a growing team, Stable Money’s developers and reviewers were spending too much time on routine code checks. Reviewers were caught up in bug hunting rather than focusing on design and architecture, while developers had to wait hours, or sometimes days, for feedback.

The Solution

In June 2025, Stable Money implemented Panto AI to streamline its code review pipeline. The AI began analyzing PRs immediately after code was pushed, offering smart, contextual suggestions, and not just on the lines changed, but across the entire file.

The Impact

The transformation was immediate and measurable.

According to Sunny Agarwal, who heads engineering at Stable Money:

“Reviewers now just focus on design — no more hunting for bugs, and devs get quick feedback right after they push code. The additional suggestion section includes some really good suggestions on the entire file, not just on the changes done. It’s even caught some nasty bugs we totally missed. Overall Panto AI is saving us a lot of time.”

Tangible Results in Just One Month

By the end of June 2025, Stable Money saw remarkable improvements:

• Pull Request Velocity:
  Stable Money saw an increase of over 19% in PR throughput after having panto onboard.

• Merge Time Reduced by Nearly Half:
  The average time to merge a PR dropped from 21 hours 46 minutes to just 11 hours 53 minutes, a 45.39% improvement. This meant faster code to production, reduced context switching for developers, and quicker iterations.

• Smarter Review Feedback:
  With Panto AI actively suggesting changes, reviewers could focus on higher-level concerns. Close to 38% of review comments initiated by the tool were accepted by the team.

• Faster Detection of Hidden Bugs:
  Panto AI even flagged subtle issues that traditional reviews missed, improving code safety and reliability.

Final Thoughts

By bringing Panto AI into their workflow, Stable Money dramatically reduced merge times, boosted code quality, and freed up reviewers to focus on what really matters: design, logic, and long-term maintainability. With AI doing the heavy lifting on bug detection and stylistic suggestions, the engineering team can now move faster, with greater confidence.