How to Clearly Classify Artificial Intelligence in Real-time Communications

By Allie Mellen on August 24, 2018
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Discussing artificial intelligence in real-time communications can be a daunting task. The real-time communications space encompasses so many different verticals, from team collaboration to one-way conversational devices, that artificial intelligence applications in RTC are extensive.

Furthermore, the real-time communications space is unique in that the customer-facing side can be customized with new, exciting applications that add to the user experience just as much as the infrastructure side.

It is for these reasons that we divide real-time communications upgrades into two distinct categories: service level applications and infrastructure level applications.

Categorizing Real-time Communications for Artificial Intelligence

 

artificial intelligence applications for real-time commuication

In the network, service level describes the services a network service provider gives the customer within a specific time period. Alternatively, the infrastructure refers to the foundation that supports a system. This is why these terms are the perfect fit for the different features real-time communications products can be updated with to be improved. In this instance, these upgrades are specific to how we improve real-time communications products with artificial intelligence. No one side - service level or infrastructure level - is more important or influential than the other. Both serve unique and intriguing purposes to further real-time communications.

 

Service Level Applications

Service level applications are implemented more directly on the customer-facing side: for example, speech analytics, image recognition, and video conferencing features. These applications tend to be very exciting to the end user and get a significant amount of media attention. They are features that revolve around new, emerging use cases of real-time communications, and are often associated with an awe moment: the future has arrived.

At callstats.io, we value these kinds of bold, extraordinary applications. To help our customers build the best products and services with these features, we make sure our product gives useful metrics. In fact, we recently updated our service level metrics to include our new Top 5 Metrics and Distributions feature.

Infrastructure Level Applications

Infrastructure level applications are implemented more directly on the developer-facing side: for example, audio and video quality, network quality, and improving the quality of experience. These optimizations can significantly improve the quality of real-time communications, though they do not tend to get the same amount of media attention.

At callstats.io, we spend a lot of time focused on building optimizations in our product and things that can be used to optimize other products to improve real-time communications on the infrastructure level. An example of this is our new AI-powered product, Optimize. Optimize uses artificial intelligence to provide media and network settings to deliver optimal audio and video quality. We leverage data from millions of conferences to bring optimal audio and video quality to every interaction.


Want to learn more about how to improve the quality of audio and video calls in your WebRTC application? Try out our dashboard.