Voice AI startup Rime lands $24M to tackle enterprise call handling
San Francisco-based Rime has closed a $24 million Series A funding round, the company announced Wednesday. The round was led by M13 Ventures, with participation from Twilio Ventures, Corazon Capital, Unusual Ventures, and others. The startup builds voice AI models designed specifically for enterprise customer calls — an increasingly crowded space.
Founded in 2022 by former Stanford PhD student Lily Clifford, ex-Amazon Alexa engineer Brooke Larson, and Stanford engineer Ares Geovanos, Rime is taking a different approach from many rivals. Instead of scraping the web for audio data, the company built a recording studio in San Francisco to collect its own conversational data. That proprietary dataset, Clifford says, helps the models nail pronunciation of brand names and industry jargon without forcing clients to retrain models from scratch.
The problem with legacy IVR — and why AI still isn’t enough
Despite rapid advances in voice AI, Clifford is surprisingly blunt about the technology’s limits. Enterprises still lean heavily on legacy IVR systems, she told TechCrunch, because AI voice agents just aren’t good enough yet.
“The voice technology is still not there to automate the vast majority of enterprise phone calls,” Clifford said. “LLMs have made it a lot easier to build voice applications that work, but they haven’t changed how it feels to interact. Talking with a voice AI agent is not the most compelling experience for the end user. It’s kinda like a new IVR, but with a better voice.”
That honesty might seem unusual for a startup CEO pitching a voice AI product. But it also signals where Rime sees its edge: not in flashy demos, but in the gritty work of making models that actually sound natural on a call.
From three models to one: Rime’s shift to speech-to-speech
Rime initially used a pipeline of separate models for speech-to-text, text-to-speech, and a large language model. But the company is now pivoting toward a unified speech-to-speech architecture. The goal? Lower latency, better turn-taking, and handling real-world problems like background noise.
That shift also reduces the burden of orchestrating multiple models. Fewer moving parts means less complexity — and, ideally, more reliable performance. For enterprise clients in regulated industries like healthcare and finance, reliability matters more than buzzword compliance.
Who’s using Rime — and why they stay on the call longer
Rime claims its approach is already winning enterprise contracts. The company says it has customers in food service, healthcare, airlines, and fintech. Named clients include Mayo Clinic, Dialpad, Upstart, and Asurion.
The startup asserts that because of its training data and model design, customers stay on calls longer — a key metric for enterprise call centers. Longer calls can mean better issue resolution, higher satisfaction, and more upsell opportunities. That’s the kind of concrete outcome that wins budgets.
M13’s Morgan Blumberg, who is joining Rime’s board as part of the Series A, sees the company’s focus on technical fundamentals as a differentiator. “Companies like ElevenLabs have moved into being an orchestration and the application layer, going head to head with the Sierras and Decagons of the world,” Blumberg said. “I think there’s just so much more to be done technically, and Rime’s approach of pushing forward on the best model with low latency and high reliability in a regulated environment stands out.”
Hiring spree ahead: Rime plans to double down on R&D
With the fresh capital, Rime plans to expand its current team of 35 people. The company is hiring for model development, engineering, and partnerships. It recently brought on Rafael Valle, who worked on audio understanding at Meta Superintelligence Labs and NVIDIA’s applied deep learning audio research team, as Chief Scientist.
Rime had previously raised $5.5 million in a seed round last May. The new funding gives it a runway to compete in a market that includes ElevenLabs, Deepgram, Vapi, Retell, LiveKit, Decagon, and Sierra. But the startup is betting that its proprietary data and focus on regulated verticals will give it an edge that more generalist voice AI companies can’t easily replicate.
For now, Clifford and her team are banking on a simple thesis: enterprise call automation won’t be won by the fanciest demo, but by the model that sounds most human — and doesn’t make customers want to hang up.
