NetGain APM

Application Transaction Data Management
NetGain APM collects the performance data of key applications in real-time to ensure that applications are performing as expected and have the right IT resources to perform optimally. Its powerful search capabilities lets users easily check how specific applications are and were performing should anyone complain about their performance. Alarms will also be generated should performance levels fall below preset threshold levels for users to investigate and take pro-active action.

Find Application Bottlenecks
End to end visibility of your application transaction, from end user monitoring to the database to the server
Identify issues quickly and accurately, enabling faster time to resolution

Real User Monitoring (RUM)
NetGain APM measures the actual application performance as experienced by the end user. It collects the full end-to-end performance data, starting from the browser to the middleware server to the database, and any other IT resource used in between. By measuring the performance data and determining the response time and resource utilization at each stage of the application usage, a detailed performance analysis can be done to identify problem areas and how the application performance can be improved.

Watcher And Alerts
Set Watcher to track your applications, and get alerted when you have any performance issues
Send alerts to SMS, email, Slack, Microsoft Teams, Telegram, Opsgenie and more

Synthetic Monitoring
Synthetic monitoring allows the app team to create a simulated user transaction, run tests on a preferred scheduled, and monitor the app for functionality, availability and response performance. The simulated transaction is created by using Chrome Recorder to record a transaction flow. This recording is then automatically run on a pre-determined test schedule.

If something goes wrong, NetGain will alert the IT team about the problem immediately.

Anomaly Detection
Anomaly detection is the identification of the behaviour of apps that deviate from its normal behavior. By using historical data, the AI determines the baseline behaviour of the app, and identifies anomalies as it happens. IT ops may also set the sensitivity of the AI detection.

Outlier Detection
An outlier is an app that deviates drastically from the given norm or average of the data set. AI is used to identify the outlier in the given data set. IT ops may also set the sensitivity of the AI detection.

With outlier detection, the AI is able to find potential issues even if the fault does not exceed the threshold, and is able to alert the IT ops team automatically.