Many WebRTC applications struggle with outdated or inappropriate media server infrastructure, limiting their ability to scale effectively and support powerful AI features. Alfred Gonzalez, Senior WebRTC Engineer at WebRTC.ventures, walks us through the considerations, options, and steps to successfully migrate to another media server. He’ll then show
Voice assistants powered by real-time AI are increasingly being used to automate phone-based customer interactions. Whether for contact centers, internal help desks, or voice-driven workflows, a reliable architecture needs to support low-latency audio streaming, accurate speech-to-text (STT), intelligent response generation, and real-time speech synthesis. In this post,
Systematically transcribing, summarizing, and analyzing contact center calls reveals critical data. This information can be used to improve efficiency, enhance the customer journey, uncover business trends, ensure compliance, and much more. With AI and ML, there’s no reason for contact centers to operate ‘in the dark.’ To
In A Serverless Approach to Post-Call Analysis Using AWS Lambda, Amazon Transcribe, and Amazon Bedrock, we saw the benefits of leveraging a serverless model for implementing AI-based post-call analysis in contact center solutions. We highlighted how building such a pipeline to evaluate service and sales calls avoids
