Retell AI lets companies create 'voice agents' to answer phone calls.

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Call centers are embracing automation. Whether this is a good thing is debated, but it is happening – and quite possibly accelerating.

According to research firm TechSci Research, the global market for contact center AI could grow from $2.4 billion in 2022 to nearly $3 billion in 2028. next year.

The motivation is clear: call centers are trying to cut costs while expanding their operations.

“Companies with heavy call center operations, looking to scale quickly without the constraints of human contact center agents, are highly receptive to adopting effective AI voice agent solutions,” entrepreneur Evie Wang told TechCrunch. “This approach not only reduces their overall costs, but also reduces wait times.”

Wang is one of the co-founders of Retell AI, which provides a platform that companies can use to build AI-powered “voice agents” that answer customer phone calls and perform tasks such as scheduling appointments. Perform basic functions. Retell's agents are powered by a collection of large language models (LLMs) for customer service use cases and a speech model that voices the text generated by the LLMs.

Retell's customers include some contact center operators but also small and medium-sized businesses that regularly deal with high call volumes, like telehealth company Ro. They can build voice agents using the platform's low-code tooling, or they can upload their own custom LLM (such as an open model like Meta's Llama 3) to further optimize the experience.

“We invest heavily in the voice conversation experience, as we see it as the most important aspect of the AI ​​voice agent experience,” Wang said. “We see AI voice agents not just as toys that someone can build with a few lines of code, but as tools that can deliver substantial business value and transform complex workflows. are.”

Retell worked quite well in my brief testing, at least on the call-facing side.

I arranged a call with the Retell bot using the demo form on the Retell website. The bot walked me through the process of scheduling a mock dentist appointment, asking questions like my preferred date and time, phone number, etc.

I can't say the bot's synthetic voice was the best I've heard in terms of realism – certainly not on par with Eleven Labs or OpenAI's text-to-speech API. In retail's defense, Wang said much of the team's focus has been on reducing delays and handling edge cases, such as communication bottlenecks.

Delay Is low: In my test, the bot responded to my answers and follow-up questions without hesitation. And it stuck to its script. Try as I might, I can't confuse him or prompt him to behave in a way he shouldn't. (When I asked Bot about my dental records, he insisted I speak to the office manager.)

So are platforms like Retell the future of call centers?

Probably. For basic tasks like appointment scheduling, automation makes a lot of sense, which is why both startups and large tech firms alike offer solutions that compete with Retell's. (See Parloa, PolyAI, Google Cloud's Contact Center AI, etc.)

It's low-hanging — and seemingly income-generating — fruit. Retell claims it has hundreds of customers, all of whom are paying per minute of voice agent conversations. Retell has raised a total of $4.53 million in capital to date, courtesy of backers including Y Combinator (where the company was incubated).

But the jury is out on more complex questions, especially given the tendency of LLMs to fabricate facts and go off the rails despite safeguards.

As Retell's ambitions continue to grow, I'm curious to see how the company navigates the many well-established technical challenges in the space. Wang, at least, seems confident in Retell's approach.

“With the advent of LLMs and recent advances in speech synthesis, conversational AI is improving enough to create really interesting use cases,” Wang said. “For example, with sub-second latency and the ability to disrupt AI, we've seen users speak in full sentences and communicate as if they were talking to another person. So AI is trying to make it easier to build, test, deploy and monitor voice agents, ultimately helping them achieve productivity readiness.”

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