Senior Software Engineer at a tech vendor with 10,001+ employees
Real User
Top 5
Jun 30, 2026
In my project, we use Timeseer.ai to analyze time-series data generated by our applications and infrastructure. A real scenario, for example, our application consists of a .NET application, Azure App Services, Kubernetes, SQL Server REST API. The application continuously generates metrics such as API response time, CPU utilization, memory utilization, database query execution, and request time-out. Basically, it monitors API performance, server health, database activity, and system resource utilization. Using AI-based anomaly detection, it identifies unusual patterns and alerts our operations team early, allowing us to resolve issues before they affected customers. That improved system reliability and reduced the time spent manually monitoring dashboards and logs. We integrated Timeseer.ai with .NET applications. In our project, we integrated our application monitoring telemetry ecosystem. Microservices, databases, and infrastructure continuously generated time-series metrics such as API response and CPU utilization. These metrics were collected through a monitoring platform and sent to Timeseer.ai. Timeseer.ai then applied AI-based analytics to identify anomalies, visualize trends, and generate alerts. It was integrated because of .NET microservices application telemetry and is used for data collection. In a real project, if an API response time increased abnormally or CPU usage remained high for an unusual period, Timeseer.ai generated an alert so the operations team could investigate before users experienced a significant impact. It has great benefits.
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In my project, we use Timeseer.ai to analyze time-series data generated by our applications and infrastructure. A real scenario, for example, our application consists of a .NET application, Azure App Services, Kubernetes, SQL Server REST API. The application continuously generates metrics such as API response time, CPU utilization, memory utilization, database query execution, and request time-out. Basically, it monitors API performance, server health, database activity, and system resource utilization. Using AI-based anomaly detection, it identifies unusual patterns and alerts our operations team early, allowing us to resolve issues before they affected customers. That improved system reliability and reduced the time spent manually monitoring dashboards and logs. We integrated Timeseer.ai with .NET applications. In our project, we integrated our application monitoring telemetry ecosystem. Microservices, databases, and infrastructure continuously generated time-series metrics such as API response and CPU utilization. These metrics were collected through a monitoring platform and sent to Timeseer.ai. Timeseer.ai then applied AI-based analytics to identify anomalies, visualize trends, and generate alerts. It was integrated because of .NET microservices application telemetry and is used for data collection. In a real project, if an API response time increased abnormally or CPU usage remained high for an unusual period, Timeseer.ai generated an alert so the operations team could investigate before users experienced a significant impact. It has great benefits.