Over 60% of Coding At Rapido Is Done By AI: Cofounder Rishikesh SR

Why It Matters

Accelerating development cycles gives Rapido a competitive edge in the crowded ride‑hailing market and showcases how AI can reshape product velocity for fast‑moving consumer tech firms.

Key Takeaways

• 66% of Rapido's code now generated with AI tools
• Release cycle cut from two months to one week
• New "Bikes for Women" feature built in 24 hours
• Quality engineers shifted to developer roles for AI workflows
• $240M funding round targets market expansion and tech investment

Pulse Analysis

AI‑assisted coding is moving from experimental labs into the core of enterprise product teams, and Rapido provides a vivid illustration of that shift. By embedding large language models and code‑generation tools into daily workflows, the Bengaluru‑based ride‑hailing firm has turned what used to be a months‑long development pipeline into a rapid, iterative process. This mirrors a broader industry trend where startups and incumbents alike leverage AI to reduce technical debt, accelerate onboarding of new talent, and democratize software creation across non‑engineer roles.

The operational impact at Rapido is tangible. Reducing the feedback loop from two months to two weeks has enabled the company to test market hypotheses in near‑real time, exemplified by the "Bikes for Women" service that moved from concept to production in a single day. Faster release cycles also mean more frequent feature updates, higher driver engagement, and an ability to respond swiftly to regulatory or competitive pressures. By converting quality engineers into developer‑focused roles, Rapido maximizes the value of its human capital while allowing AI to handle repetitive coding tasks, thereby freeing engineers for higher‑order problem solving.

Looking ahead, the recent $240 million infusion—part of a $730 million series—will fund geographic expansion, driver network growth, and further AI‑driven technology investments. However, Rapido’s leadership remains cautious, acknowledging that AI models still require rigorous testing and cannot replace human judgment in every scenario. The company’s balanced approach—unrestricted AI access paired with disciplined validation—offers a blueprint for other high‑growth tech firms seeking to harness generative AI without compromising product quality or safety. As AI tooling matures, the competitive advantage will increasingly belong to firms that embed these capabilities into their development culture early.