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Retell AI lets businesses create “voice agents” to answer phone calls

Call centers are embracing automation. There is some debate over whether this is a good thing, but it is happening – and perhaps even accelerating.

According to research firm TechSci Research, the global contact center AI market could reach nearly $3 billion in 2028, up from $2.4 billion in 2022. Meanwhile, a recent survey found that approximately half of contact centers are considering adopting some form of AI worldwide. next year.

The motivation is pretty obvious: call centers are looking to reduce costs while growing their operations.

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

Wang is one of the co-founders of Retell AI, which provides a platform that businesses can use to create AI-powered “voice agents” that answer customer phone calls and perform basic tasks such as appointment planning. Retell’s agents are powered by a combination of large language models (LLMs) fine-tuned for customer service use cases and a voice model that gives voice to the text generated by the LLMs.

Retell’s customers include some contact center operators, but also small and medium-sized businesses that regularly handle high call volumes, such as telehealth company Ro. They can create voice agents using the platform’s low-code tools, or upload a custom LLM (for example, an open template like Meta’s Llama 3) to further personalize the experience.

“We are investing a lot in the voice chat experience because we consider it to be the most critical aspect of the AI ​​voice agent experience,” Wang said. “We don’t view AI voice agents as simple toys that can be created with a few lines of prompts, but rather as tools that can deliver substantial value to businesses and replace complex workflows.

Retell worked quite well in my brief tests, at least on the calling side.

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

I can’t say the robot’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. Wang, in Retell’s defense, said the team focused primarily on reducing latency and handling edge cases, like interruptions that might occur in a conversation.

Latency East weak: during my test, the bot responded almost without hesitation to my answers and additional questions. And he stayed true to his script. Try as I might, I couldn’t confuse him or make him behave in a way he shouldn’t. (When I asked the robot about my dental records, it insisted that I speak with the head of the office.)

So, are platforms like Retell the future of call centers?

Maybe. For basic tasks like scheduling appointments, automation makes perfect sense, which is probably why startups and big tech companies are offering solutions that compete head-on with Retell’s. (See Parloa, PolyAI, Contact Center AI from Google Cloud, etc.)

This is low-hanging fruit – and apparently a money-maker. Retell claims to have hundreds of customers, all of whom pay per minute of conversation with a voice agent. Retell has raised a total capital of $4.53 million to date, thanks to 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 make up facts and go off the rails, even with safeguards in place.

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

“With the advent of LLMs and recent breakthroughs in text-to-speech, conversational AI is becoming powerful enough to create some truly exciting use cases,” Wang said. “For example, with sub-second latency and the ability to interrupt the AI, we observed users speaking in fuller sentences and conversing as they would with another person. We’re trying to make it easy for developers to build, test, deploy, and monitor AI voice agents, ultimately helping them be production-ready.


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