Gamearly provides a platform for mobile game developers to identify and resolve performance bottlenecks. Its automated telemetry and analytics detect issues like frame rate drops and memory leaks, enabling targeted optimization for improved player experience and retention.
Funding
Funding not disclosed
Founders
Product
Problem
Mobile game developers face challenges in identifying and resolving performance bottlenecks that negatively impact player experience and retention. Manual debugging and performance analysis are time-consuming and often fail to uncover the root causes of issues like frame rate drops, memory leaks, and long load times.
Solution
Gamearly offers a comprehensive platform designed to streamline the process of mobile game performance optimization. The platform provides advanced telemetry and analytics that automatically detect and diagnose technical issues across various devices and operating system versions. Developers can leverage Gamearly's insights to pinpoint specific areas of their game code or asset pipeline that are causing performance degradation. This allows for targeted remediation, leading to improved frame rates, reduced memory consumption, and faster loading times, ultimately enhancing player engagement and retention.
Target Audience
The primary target audience includes mobile game studios and independent game developers seeking to enhance the technical performance and player experience of their titles.
Features
- Real-time performance monitoring with granular metrics for CPU, GPU, memory, and network usage.
- Automated anomaly detection for identifying performance regressions and critical errors.
- In-depth profiling tools to analyze game loops, rendering pipelines, and asset loading sequences.
- Cross-platform compatibility for iOS and Android mobile game development environments.
- Integration with popular game engines such as Unity and Unreal Engine.
- Customizable dashboards and reporting for tracking key performance indicators (KPIs) over time.
- Crash reporting and analysis with stack trace and device context.