Retell AI to Integrate AI Voice Agents to Call Centers 

Retell AI is assisting companies in building AI voice agents for their call centers, aiming at making the process easier without relying on humans. 

Nowadays, call centers are shifting towards the adoption of automation. According to TechSci researcher, the global market for contact center AI could reach around $3 billion by 2028, compared to $2.4 billion in 2022. 

Moreover, a recent survey shows that half of the contact centers are planning to integrate some AI functionalities by next year with the aim to reduce costs and expand their operations. 

Evie Wang, a co-founder of Retell AI, emphasized that companies that heavily rely on call center operations are eager to implement this new technology that consists of AI voice agents with the aim to elevate their operations without the need to fully rely on human agents. 

This strategy reduces the costs and reduces the wait time for clients. To this end, Retell AI provides a platform that creates AI-powered voice agents. These agents can handle various tasks entrusted to human ones, such as customer calls and appointments. 

It is worth noting that these AI powered agents are based on large language models, as well as speech models to convert texts to speech. 

The company’s clients include contact center operators and small- to medium-sized enterprises that deal with a large amount of calls, like the telehealth firm Ro. Users have two options when it comes to developing their voice agents. They either go for the platform’s low code tools or upload a customized large language model (LLM) for their operations. 

Wang emphasized that Retell AI invests heavily in the voice conversation experience, viewing AI voice agents as valuable business tools rather than ordinary toys. 

On the other hand, some tests have been conducted on the Retell AI and showed low latency and adherence to the prompts even when trying to confuse it. Despite the voice quality of the company’s AI agents may not match that of other providers, the focus was to reduce latency and handle conversation interruptions. 

However, the success and the effectiveness of such platforms when used for more complicated requests remains uncertain, due to the challenges related to LLMs. 

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