In a previous post, Evaluating and Improving Voicebot Flows with Call Stats, we introduced this vital health check for the continuous improvement of voicebot performance. Call statistics gauge bot effectiveness and identify bottlenecks and potential flow disruptions in AI agent workflows. Adding call stats to voicebot flows
At Enterprise Connect 2024, I saw the latest updates around AI in enterprise communications. It was an interesting contrast to my 2023 visit to the same conference. While the hype was more or less the same, there has definitely been progress in turning that hype into reality.
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
AI and LLMs are everywhere these days. In contact center applications, they are the key to implementing next generation post-call analysis, making manual work by supervisors and third party surveys a thing of the past. AI-based post-call analysis provides an automated approach to capture recordings of customer
