In logistics, scalability and reliability are essential. Load testing ensures performance under growing demands, but manual analysis is time-consuming and overlooks critical factors like latency and resource use.

Learn how we integrated AI into load test analysis to automate result evaluations. AI analyzes complex data, providing actionable insights and identifying issues like memory leaks and CPU inefficiencies. This approach helps us scale quickly, meeting resilience goals and maintaining 95% of requests under 150ms.

Target Audience: Engineers willing to handover routine to AI
Prerequisites: This presentation will be insightful for anyone from entry level to experienced engineers.
Level: Basic

Extended Abstract:
In the fast-paced world of logistics, scalability and reliability are non-negotiable, especially when handling over 10 million daily orders. To ensure seamless performance under growing demands, we rely on load testing to assess and optimize our services. However, manual analysis of load test results is both time-consuming and incomplete, often overlooking critical factors like latency, resource utilization, and error patterns.

This session will explore how we revolutionized load test analysis by integrating AI into our workflow. By automating result evaluations and leveraging AI's ability to analyze complex datasets, we moved beyond static PDF reports. Our system now delivers actionable insights, uncovering issues like memory leaks and CPU inefficiencies while providing improvement suggestions tailored to our benchmarks.

Join us to learn how this AI-powered approach helps us meet resilience and performance goals, scaling from average load to 4x peak demand within minutes—all while maintaining 95% of requests under 150 milliseconds.