Wheel Pros manages 300+ microservices with AI-powered code quality via Amazon CodeGuru

Background

Wheel Pros operates more than 300 microservices across a global distribution business. As the microservices estate grew, manually diagnosing performance bottlenecks and reviewing code quality across hundreds of services became impractical. The company needed an automated, ML-powered approach to maintain code quality and application performance at scale.

What Was Implemented

• Deployed Amazon CodeGuru Reviewer to automate code quality and security reviews within pull requests across 300+ microservices, using machine learning trained on millions of code reviews at Amazon
• Deployed Amazon CodeGuru Profiler to continuously analyze application runtime performance and surface ML-generated recommendations for optimization
• Implementation executed in partnership with Presidio, an AWS systems integrator, to configure and integrate CodeGuru into Wheel Pros' development workflows

Results

Wheel Pros reports that Amazon CodeGuru Profiler provides ML-powered performance recommendations automatically, eliminating the need for developers to diagnose configuration and performance issues manually. This enables the development team to focus on building features rather than optimizing infrastructure. No specific quantitative outcomes (defect reduction rates, performance gains, developer hours saved) were publicly disclosed in the sources fetched.

Lessons

• For organizations with large microservices estates, ML-powered code review tools can scale review coverage in a way that manual review cannot.
• Runtime profiling with ML-generated recommendations eliminates a class of performance tuning work that previously required specialized expertise.
• Combining code review (static analysis) and runtime profiling (dynamic analysis) in a single vendor platform reduces integration complexity.
• Working with a systems integrator can accelerate deployment and configuration across complex, distributed architectures.