Challenge
Paytm Lab's development team was under intense pressure with tight deadlines and a legacy code module burdened by manual, error-prone boilerplate tasks. This outdated process slowed feature delivery and escalated debugging efforts.
Solution
- Context-Aware Code Suggestions: Delivered real-time insights that streamlined the coding workflow.
- 'Generate' Feature: Enabled developers to highlight code snippets and instantly generate optimized solutions, reducing repetitive tasks.
- Refactoring Guidance: Offered tailored recommendations for legacy modules, promoting coding consistency and elevating overall quality.
Results
- Accelerated Feature Delivery: Eliminated manual overhead, enabling faster release cycles and sharper focus on core development.
- Efficiency Gains: Reduced workload on critical modules by half while boosting performance by 30% through smart refactoring.
- Reduced Debugging Time: AI-assisted code generation minimized errors, instilling greater confidence in production-ready releases.
"Zencoder is like having an AI pair programmer—it anticipates my needs, reduces repetitive tasks, and lets me focus on solving complex problems. A must-have for IntelliJ developers."
Zineng Yuan, Paytm Lab Developer
Conclusion
By integrating Zencoder into IntelliJ, Paytm Lab transformed its legacy module into a streamlined, efficient workflow. This shift accelerated feature delivery, boosted efficiency, and reduced debugging time—empowering the team to deliver better software, faster.
