Rime 自建录音棚采集对话数据训练模型，并采用音素架构适配不同行业术语发音，客户无需重新训练。 公司已服务 Mayo Clinic、Dialpad 等客户，并计划将 35 人团队扩招至模型开发、工程和合作岗位。
语音人工智能初创公司的最大突破是处理企业在销售、营销和客户支持等领域的电话。大型机构正在将通话外包给语音模型开发商，如 ElevenLabs：https://techcrunch.com/2026/02/04/elevenlabs-raises-500m-from-sequioia-at-a-11-billion-valuation/ 和 Deepgram：https://techcrunch.com/2026/01/13/deepgram-raises-130m-at-1-3b-valuation-and-buys-a-yc-ai-startup/；基础设施公司如 Vapi：https://techcrunch.com/2026/05/12/vapi-hits-500m-valuation-as-amazon-ring-chose-its-ai-platform-over-40-rivals/、Retell 和 LiveKit；以及专注于客户支持的公司如 Decagon 和 Sierra。
总部位于旧金山的 Rime：https://rime.ai/ 正在通过其基于录制对话数据训练的语音 AI 模型，在这个竞争激烈的市场中争取优势，旨在减少客户的定制化工作量。
Rime 由前斯坦福博士生 Lily Clifford、前亚马逊 Alexa 工程师 Brooke Larson 以及斯坦福工程师 Ares Geovanos 于 2022 年创立。Rime 在旧金山建立了一个录音室，以收集自己的对话数据，而不是依赖网络抓取音频。
这家初创公司表示，它专注于调整语音模型，以精准发音不同品牌实体和行业特定术语。它采用基于音素的架构以适应不同发音，从而让客户无需为其特定行业重新训练模型。
Rime 星期三表示，它在 A 轮融资中筹集了 2400 万美元，由 M13 Ventures 领投。Twilio Ventures、Corazon Capital、Unusual Ventures 及其他现有投资者也参与了此次融资。
Clifford 表示，尽管语音 AI 的发展已有进展，企业仍然倾向于使用传统 IVR 实现，因为 AI 语音技术仍然无法达到 IVR 的效果。
“语音技术仍然无法自动处理绝大多数企业电话。大语言模型使得构建可用的语音应用变得容易，但它们并没有改变互动的体验。与语音 AI 代理通话对终端用户并不是最吸引人的体验。有点像一个新的 IVR，但声音更好，”她说。
这家初创公司最初建立了一条独立模型管道，包括语音转文本、文本转语音和大型语言模型。但现在它正在转向开发更好的语音对语音模型，以减少延迟、改善轮换对话，并解决背景噪音等问题。新方法还将减少对编排的依赖，因此公司不必管理大量的模型。
Rime 表示，它在餐饮服务、医疗保健、航空公司和金融科技领域拥有客户。公司声称，得益于其训练数据和模型定位，客户在通话中停留的时间更长，这帮助它赢得了来自梅奥诊所（Mayo Clinic）、Dialpad、Upstart 和 Asurion 等客户的企业合同。
借助这笔新融资，Rime 计划扩大其35人的团队，目标是招聘模型开发、工程和合作伙伴相关岗位。它最近聘请了 Rafael Valle 担任首席科学家，Valle 曾在 Meta 超智能实验室和 NVIDIA 应用深度学习音频研究团队从事音频理解工作。
“像 ElevenLabs 这样的公司已经转向成为编排和应用层，与全球的 Sierras 和 Decagons 直接竞争。我认为在技术上还有很多工作要做，而 Rime 在受监管环境下以低延迟和高可靠性推进最佳模型的做法非常突出，” M13 的 Morgan Blumberg 告诉 TechCrunch。
该公司此前于去年五月完成了550万美元的种子轮融资：https://www.axios.com/pro/enterprise-software-deals/2025/05/29/exclusive-ai-voice-startup-rime-55m。Blumberg 将作为此次融资的一部分加入初创公司的董事会。
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Voice AI startups’ biggest unlock has been handling calls for enterprises in areas like sales, marketing and customer support. Large organizations are offloading calls to voice model developers like ElevenLabs：https://techcrunch.com/2026/02/04/elevenlabs-raises-500m-from-sequioia-at-a-11-billion-valuation/ and Deepgram：https://techcrunch.com/2026/01/13/deepgram-raises-130m-at-1-3b-valuation-and-buys-a-yc-ai-startup/; infrastructure companies like Vapi：https://techcrunch.com/2026/05/12/vapi-hits-500m-valuation-as-amazon-ring-chose-its-ai-platform-over-40-rivals/, Retell, and LiveKit; and dedicated customer support shops like Decagon and Sierra.
San Francisco-based Rime：https://rime.ai/ is trying to gain an edge in this crowded market with its voice AI models that are trained on conversational data that it records, aiming to reduce its clients’ customization load.
Founded in 2022 by former Stanford PhD student Lily Clifford, ex-Amazon Alexa engineer Brooke Larson, and Stanford engineer Ares Geovanos, Rime has built a recording studio in San Francisco to collect its own conversational data rather than relying on scraping the web for audio.
The startup said it focuses on tuning its voice models to nail the pronunciation of different brand entities and industry-specific terms. It employs a phoneme-based architecture to adapt to different pronunciations so that customers don’t have to retrain models for their specific industry.
Rime on Wednesday said it has raised $24 million in a Series A funding round that was led by M13 Ventures. Twilio Ventures, Corazon Capital, Unusual Ventures and other existing investors also participated.
Clifford said that despite progress in voice AI development, enterprises still prefer legacy IVR implementations, as AI voice technology still can’t match up to IVR’s effectiveness.
“The voice technology is still not there to automate the vast majority of enterprise phone calls. 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,” she said.
The startup started off with a pipeline of separate models for speech-to-text, text-to-speech, and a large language model. But it is now shifting focus to develop better speech-to-speech models to reduce latency, improve turn-taking, and tackle issues like background noise. The new approach will also serve to decrease reliance on orchestration, so the company doesn’t have to manage a bunch of models.
Rime says it has customers in food service, healthcare, airlines, and fintech. The company claims that because of its training data and model positioning, customers stay longer on the call, which has helped it win enterprise contracts from clients like Mayo Clinic, Dialpad, Upstart, and Asurion.
With the new funding, Rime is planning to expand its team of 35 people, aiming to hire 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 its Chief Scientist.
“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. 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,” M13’s Morgan Blumberg told TechCrunch.
It had previously raised $5.5 million in a seed round last May：https://www.axios.com/pro/enterprise-software-deals/2025/05/29/exclusive-ai-voice-startup-rime-55m. Blumberg is joining the startup’s board as part of the fundraise.
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Ivan covers global consumer tech developments at TechCrunch. He is based out of India and has previously worked at publications including Huffington Post and The Next Web.
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