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Pioneering AI Speech Models To Transform The Call Center

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Manage episode 431898936 series 3585274
Contenu fourni par Chad Oda. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Chad Oda ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.

Imagine a world where call center agents can effortlessly understand customers with thick accents, and background noise becomes a thing of the past. This future is closer than you think, thanks to advancements in Artificial Intelligence (AI) and specifically, audio-based Large Language Models (LLMs). AI and audio-based LLMs are poised to revolutionize the call center landscape for enterprises and BPOs.

In a recent interview, we had the privilege of discussing these groundbreaking concepts with James Fan, the Chief Technology Officer of Tomato.ai. Before joining Tomato.ai, James served as a Senior Manager at Google, where he played a pivotal role in developing the CCAI analytics solution and led the Google Cloud Speech Group. Additionally, James founded and successfully exited Hello Vera, a contact center start-up.

Here are some key takeaways

AI is not a replacement for human agents. James emphasizes that AI is designed to augment human capabilities, empowering agents to provide a more personalized and efficient service.

Modern Call Center Challenges: Balancing cost-efficiency and customer experience remains a significant challenge for enterprises.


Addressing the Accent Challenge.
Tomato.ai’s technology was born from the need to bridge the communication gap caused by accents. Their solutions directly address a pain point for many call centers.

Seamless Integration is Key. Tomato.ai integrate seamlessly with existing call center infrastructure, minimizing disruption and maximizing ROI.

Overcoming Implementation Hurdles. Successful AI implementation requires thorough testing, phased deployment, and addressing concerns about AI’s impact on the workforce.

  continue reading

9 episodes

Artwork
iconPartager
 
Manage episode 431898936 series 3585274
Contenu fourni par Chad Oda. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Chad Oda ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.

Imagine a world where call center agents can effortlessly understand customers with thick accents, and background noise becomes a thing of the past. This future is closer than you think, thanks to advancements in Artificial Intelligence (AI) and specifically, audio-based Large Language Models (LLMs). AI and audio-based LLMs are poised to revolutionize the call center landscape for enterprises and BPOs.

In a recent interview, we had the privilege of discussing these groundbreaking concepts with James Fan, the Chief Technology Officer of Tomato.ai. Before joining Tomato.ai, James served as a Senior Manager at Google, where he played a pivotal role in developing the CCAI analytics solution and led the Google Cloud Speech Group. Additionally, James founded and successfully exited Hello Vera, a contact center start-up.

Here are some key takeaways

AI is not a replacement for human agents. James emphasizes that AI is designed to augment human capabilities, empowering agents to provide a more personalized and efficient service.

Modern Call Center Challenges: Balancing cost-efficiency and customer experience remains a significant challenge for enterprises.


Addressing the Accent Challenge.
Tomato.ai’s technology was born from the need to bridge the communication gap caused by accents. Their solutions directly address a pain point for many call centers.

Seamless Integration is Key. Tomato.ai integrate seamlessly with existing call center infrastructure, minimizing disruption and maximizing ROI.

Overcoming Implementation Hurdles. Successful AI implementation requires thorough testing, phased deployment, and addressing concerns about AI’s impact on the workforce.

  continue reading

9 episodes

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