How Ramp Engineers Speed Up Code Review with GPT-5.5 and Codex

Ramp, a corporate spend platform, uses OpenAI's Codex, powered by GPT-5.5, to speed up code review. Instead of waiting hours for a free senior engineer to look at a PR, engineers get detailed feedback in minutes.

How AI speeds up code review

Codex is integrated into GitHub and analyzes every pull request on the fly. The system looks at the code changes, checks style (variable names, indentation, structure), looks for potential bugs (null checks, logic errors, race conditions) and suggests refactoring.

For Ramp, this was a turning point. The startup is growing, and dozens of PRs wait for review every day. Hiring of senior engineers lags behind the pace of development. That is why they chose Codex: not to replace review, but to automate the first pass.

The process looks simple: an engineer opens a PR → Codex generates comments right in the code → the developer sees the feedback immediately. Once the AI has given its remarks, the code is ready for human review: faster, cleaner, and with less chance of missing something. The human reviewer focuses on design and architecture.

What changes in the process

• Shipping speed: minutes instead of hours for code review
• New engineers learn from the AI's feedback and see best practices in context
• Seniors focus on architectural comments and design decisions
• Fewer cycles: the AI catches stylistic and logic errors the first time
• Consistency: the machine always gives the same feedback under the same rules

In parallel, Ramp is working on the integration: Codex sees the context of the entire repo, understands the team's architectural conventions (for example, how it usually structures error handling), and can give feedback that sounds like it comes from an experienced Ramp colleague. Quality has effectively improved, because no error slips past, neither a human's nor the machine's.

"With Codex we saved thousands of hours on mechanical code checking.

Engineers now focus on what really matters."

What this means

AI code review is turning from an experimental tool into an industry standard. When a machine can give instant feedback on style and simple bugs, people are freed up for work that requires judgment: architecture, test strategy, and the UX consequences of code.