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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 and Deepgram; infrastructure companies like Vapi, Retell, and LiveKit; and dedicated customer support shops like Decagon and Sierra. San Francisco-based Rime 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. Blumberg is joining the startup’s board as part of the fundraise. When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence. 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. You can contact or verify outreach from Ivan by emailing im@ivanmehta.com or via encrypted message at ivan.42 on Signal. View Bio

Vint Cerf says his favorite place is where he’s never been before. One of the architects of the protocols behind the open internet, Cerf left Google after 20 years last week, but he’s not done thinking about the digital future. Starting today, he’s advising Innovation Labs, an organization trying to create the open architecture for AI agents to identify themselves. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, which sees domain-name infrastructure as a practical way to hold AI agents accountable and position itself for a future where more online interaction happens between agents than people. Cerf joins a handful of other internet luminaries lending their names to the effort. Most AI agents today stay within proprietary systems, calling on internal resources for specific purposes. But businesses are already envisioning a world where they operate far more autonomously across the internet and interact directly with other agents. So far, a key road block has been a lack of a shared standard for identifying and auditing agents. A variety of standards are beginning to emerge, and Innovation Labs has proposed DNSid, a registry for agent identification that links each one to an existing internet domain name and uses cryptographic proofs to log its registration over time. Innovation Labs’ interim CEO Allie Kline says the company is trialing the standards with several unnamed hyperscalers and identity companies. “I felt like I might be able to help them in a period of time when naming and identification is becoming increasingly important,” Cerf told TechCrunch. “This is largely triggered by the notion of AI agents and the question of what authorities they have, where they have derived those authorities, who is accountable for the behavior of an agent in this context, and where and how its identity is established, and why [you’d] trust it.” Those questions promise to be thorny, Cerf says, because AI agents are so much more active than domains, and it’s not yet clear what commitment an organization is making when they register one. “It’s going to be a fascinating—and at the same time maybe even exasperating—period in the in the evolution of the internet and the things that depend on it, because the functionality is so dramatically powerful,” Cerf said. With multiple solutions to the problem under consideration, Cerf says the key to a wide adoption of any protocol will be its functionality. “Company X uses agent Y’s technology, and company A uses agent C’s technology, and then they don’t interwork with each other,” Cerf said. “Nobody can do everything that you might want every agent to do… and so we’re going to have to rely on the pressure coming from the users. This is what happened with TCP/IP.” One key to Innovation Labs’ proposal is that it does not come with broader plans to do other kinds of AI business or own the registration data, Kline says. “I think there’s a lot of organ rejection to a hyperscaler releasing [a standard] and having that proprietary data,” she told TechCrunch. And does Cerf think the agentic economy is the internet’s destiny? “I don’t think it’s inevitable,” he said. “But what I do think is inevitable is that people will try to do that. We are fundamentally lazy creatures, and if we find a way to have an an agent do something for us, we’re very likely to choose to do that because [it’s] just easier.” When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence. Tim Fernholz is a journalist who writes about technology, finance and public policy. He has closely covered the rise of the private space industry and is the author of Rocket Billionaires: Elon Musk, Jeff Bezos and the New Space Race. Formerly, he was a senior reporter at Quartz, the global business news site, for more than a decade, and began his career as a political reporter in Washington, D.C. You can contact or verify outreach from Tim by

Known for its cloud infrastructure that allows developers to deploy agents without managing servers, Vercel has quietly become one of the most central companies in AI software. The company currently sees 6 million deployments a day, half of them triggered by coding agents, and more than 1 trillion tokens flow through the company’s AI gateway daily. After the company’s ShipNYC conference last week, we sat down with Vercel CEO Guillermo Rauch for his take on this moment in AI, and how platform companies like Vercel end up competing with major labs. Here’s a lightly edited transcript. It feels like there’s a different energy in the community this year, fewer pilot programs and more focus on how to make things work well in practice. I’m sure you’ve seen that a lot with clients, but I’m curious what that journey has looked like within Vercel. Last year was about prototyping. The sky’s the limit, unleash the agents, everyone can build, and so on. We did that, and we learned a lot because we had hundreds of agents organically developed and deployed within the company, and then you started getting into the realities of agents in production, and some of the challenges. The biggest lesson for me was the home-run use cases, the two killer apps of agents. One is the coding agent, of course. That’s driving a lot of the token utilization in the world, but when you produce so much software, you need somewhere to put it. The second killer app of agents is the internal agent that helps you run the company. The challenge there is, how do you securely access data? How do you audit what the agent is doing? How do you get a trail of all of the tool calls and access controls that the agent had to incur in order to get a job done? To solve that, we came up with this framework called Eve, where you can lay out an agents’ instructions and skills in natural language. And another tool is Vercel Sandbox, where you put the agent in a little cage. It can have the freedom still to do to express its intelligence, but then you can apply policy on what data it can access and what data can leave the sandbox. What sort of problems does that help you avoid? For Sandbox, the biggest advantage is data control. A real risk of AI that I always think about is, when you get a coding IDE like Devin or Cursor, if you’re in the wrong setting, they may train on your entire codebase. I remember talking to the president of Airbus about this. You have decades of wealth of very specific C++ code for aerospace engineering. Someone comes in and installs the wrong developer tool and boom, all the code goes out to the cloud for training. I’m curious to hear more about that second killer use case. We all know about coding agents, but what does an internal corporate agent look like in practice? So, there’s a sales rep sitting out there [in Vercel’s office]. She works on install base. Her job is to grow existing accounts. The bottleneck for people like her has not been her creativity, intelligence, ability to build relationships, it’s been data. “I don’t understand what accounts are growing faster. Give me the five accounts that have added the most seats in the last two weeks, so that I can prioritize my work.” She couldn’t ask that question in the past. She needed to wait until a Q1 project for a new sales dashboard completed. We were in that bottleneck for years at Vercel, and it was really frustrating because on the R&D side, we’re the fastest-moving company in the world. But on the sales engine, the Salesforce engineering [side], I was so incompetent. I had never opened Salesforce in my life when I started. Now I feel like I can actually have impact across the entire company, because Eve can be used for our customer-facing agents and can be used to improve productivity. Same technology, it’s just APIs. Agents are forcing companies to open up, and that will have dramatic long-term implications. So many of these SaaS giants build their entire kingdoms on trapping your d