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Application Performance Management

Monitor and manage your software application

Application Performance Monitoring and Management (APM) is a set of practices and tools used to monitor and manage the performance of software applications. APM is used to detect and diagnose performance issues, optimize application performance, and ensure that applications are running effectively.

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A must-have for team success

Being able to start quickly is a crucial aspect of your team's experience that can affect job satisfaction, engagement, and productivity. Our services allow you to get started with minimal effort by offering off-the-shelf integration options. Add analytics to your new or existing application with minimal effort. There is little to no training required to get started.

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Use Immersive APM exclusively or supplement an existing APM solution

Our products are designed to provide customers with the ultimate flexibility in how they use them. Whether they want to use our products exclusively or supplement their existing installations, our products are designed to meet your team's needs. This can provide additional functionality that is not available in an existing analytics solution, or provide a more streamlined workflow that improves productivity and reduces cost.

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Application immersion is a game-changer

Application Operations Visibility and Diagnostic Output

Traces and Logs (Term Explanations)

Traces are a type of telemetry data that provides a detailed view of the behavior of requests as they flow through a distributed system. They allow you to see the timing and dependencies of requests across multiple services and components. With OpenTelemetry, you can instrument your code to generate trace data and use it to analyze the performance and behavior of your application.

Logs, on the other hand, provide a detailed view of what is happening in an application. They are used to record events and messages that occur during the execution of an application. OpenTelemetry supports structured logging, which allows you to create logs with a predefined structure that can be easily queried and analyzed.

Traces and logs are correlated which means that you can use trace data to enrich your logs with additional context, such as the timing and dependencies of requests. This can be incredibly useful when troubleshooting issues in a distributed system.


Using 3D/VR visualization for telemetry information is incredibly efficient and effective. With traditional methods of analyzing telemetry data, users often find themselves sifting through endless grids, filtering through pages of data, and trying to decipher complex charts. This can be a time-consuming process, and it can be difficult to extract meaningful insights from the data.

End-to-End Application Operations

Spans and Distributed traces (Term Explanations)

Spans are a type of telemetry data that provides a detailed view of the behavior of requests as they flow through a distributed system. They allow you to see the timing and dependencies of requests across multiple services and components. You can instrument your code to generate span data and use it to analyze the performance and behavior of your application.

Distributed traces are a collection of spans that represent the journey of a request through a distributed system. When a request is made, it can be traced across multiple services and components, and each span in the trace represents a specific operation or event in the journey of the request. Collect and analyze distributed traces, giving you a comprehensive view of the behavior of your application.


With the use of 3D/VR visualization, users can see all telemetry information about an application in real-time without the need for endless grids, filtering, or digging through pages of data. The benefits of using 3D visualization are clear: it allows users to quickly and easily identify patterns and trends in the data, without the need for extensive analysis.

High Fidelity Process Data

Tags and events and other customizations (Term Explanations)

Tags are key-value pairs that can be attached to spans and metrics to provide additional context. For example, you could attach a tag to a span that identifies the version of your application that generated the span. This can be useful when analyzing performance and behavior data across different versions of your application.

Events are a type of telemetry data that represent a specific point in time during the execution of your application. They can be used to record significant events, such as the start or end of a request, or the occurrence of an error. Events can be attached to spans and metrics, and can be used to provide additional context when analyzing telemetry data.


3D/VR visualization allows users to easily identify outliers and anomalies in the data, which can be difficult to detect with traditional methods. This can be particularly helpful in identifying potential issues or bugs in an application, which can then be addressed before they become more serious.

Application immersion is a game-changer

Immersive Fusion redefines efficiency and effectiveness in analytics

By providing users with a clear and intuitive way to analyze data, we allow your team to make more informed decisions in a shorter amount of time at a reduced financial and effort cost.

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Application Data Flow

A complete turn-key solution that allows developers to quickly and easily integrate observability and analytics into their applications using only an API key and standard open-source packages. With this solution, developers can access a range of pre-built tools and services, without having to worry about the underlying infrastructure.

Diagram: Application Data Flow

It's more than monitoring. It's being inside your application.

Immersive Fusion services enable you to Enter the World of your Application ™ because we organically use all four dimensions to create an experience that you are used to in the real world: Be able to interact with and see any aspect of your application at any granularity with limited effort and immediate results.

Application Performance Monitoring Evolution
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