When companies are looking for opinions or advice on a project, they tend to go to LinkedIn or use expert networks such as GLG, Third Bridge, or Alphasights.
Amazon Ring, facing a surge in customer-support calls during last year’s holiday season, evaluated more than 40 AI voice vendors before choosing startup Vapi to handle its inbound phone traffic. Today, Ring routes 100% of its inbound calls through Vapi’s platform.
That deployment helped Vapi raise a $50 million Series B led by Peak XV Partners at a valuation of around $500 million after investment, according to a person familiar with the matter.
Ring turned to Vapi in mid-Q4 last year, when it was weighing whether to expand call-center capacity, rely more heavily on traditional automated phone systems, or deploy AI agents that could respond more naturally to customers, Vapi Chief Executive Jordan Dearsley (pictured above, left) told TechCrunch. Dearsley believes Ring chose Vapi
because if offered Ring engineers granular control over how the AI agents behaved in live customer interactions.
Jason Mitura, vice president of software development at Amazon Ring, said Ring’s customer satisfaction scores improved after deploying Vapi’s platform and that the company’s teams were able to tune the AI agent experience without depending on engineering. “A lot of AI tools promise great outcomes — Vapi has delivered on them,” he said.
Founded by Dearsley and his University of Waterloo classmate Nikhil Gupta (pictured above, right), Vapi grew out of an AI therapist Dearsley built in 2023 for conversations during his daily walks. The pair, who had gone through Y Combinator with productivity startup Superpowered, found that while few people wanted the therapy product itself, startups were increasingly interested in the low-latency voice infrastructure underneath it. This led them to pivot to Vapi and launch the platform publicly in 2024.
Vapi provides tools for companies to build, deploy, and manage voice agents across customer support, lead qualification, appointment scheduling, and outbound sales.
Image Credits:Vapi
The startup says it has now handled more than 1 billion calls through its platform, with usage accelerating as enterprises move more customer interactions onto AI systems. Vapi, Dearsley said, currently processes between 1 million and 5 million calls a day, with enterprise customers accounting for the bulk of that volume.
In addition to Amazon Ring, Vapi's enterprise customers include Kavak, Instawork, New York Life, UnityAI, Cherry, and Intuit. The startup also operates a self-serve developer platform that has been used by more than 1 million developers.
"Because we started from self-serve and had such a wide developer footprint, we were already battle-tested at significant scale before we signed our first major enterprise customer," Dearsley said.
Other investors participating in the Series B round included Microsoft's M12, Kleiner Perkins, and Bessemer Venture Partners, bringing Vapi's total funding to $72 million. The startup is currently at an annual recurring revenue run rate in the "healthy" eight figures, an investor source told TechCrunch.
Vapi is part of a growing wave of AI voice startups that includes Sierra, Decagon, PolyAI, Bland, Retell, and ElevenLabs, as companies race to build systems capable of handling customer conversations with minimal human involvement. Dearsley said Vapi differentiates itself by focusing less on pre-packaged applications and more on the infrastructure and orchestration layer behind voice agents, particularly for enterprises that want greater control over reliability, compliance, and model behavior.
The startup currently has around 100 employees and plans to use the new funding to expand its engineering, infrastructure, and go-to-market teams.
"The golden problem is taking this indeterminate beast that is a model and taming it," Dearsley said. "If you can do that, then you can provide value to the world." When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
Jagmeet covers startups, tech policy-related
When companies are looking for opinions or advice on a project, they tend to go to LinkedIn or use expert networks such as GLG, Third Bridge, or Alphasights. But they often don’t find quality inputs, despite their searches.
Today, these sites ask experts to fill in a form based on their job title, which is then used to match them with companies in need of their help.
London-based Ethos thinks that AI can improve both sides of this experience. For experts, it offers voice-powered onboarding to ask a broader set of questions and get more data about their knowledge in various domains that their job titles don’t cover. For companies, Ethos can better match natural language queries posed by these organizations for their project, thanks to the wider range of data it has collected.
Ethos said that its voice-based onboarding and data allows it to answer complex client questions like, “Find me people who worked at a funded startup by A-grade investors solving for finance automation.”
Another example the startup gave was how a pharma company using its platform could search for doctors who specialize in a certain area, but who have also written papers on the subject or have an understanding of drug development.
Image Credits: EthosImage Credits:Ethos
Today, Ethos announced a $22.75 million Series A round led by a16z with participation from General Catalyst, XTX Markets, Evantic Capital, and Common Magic.
a16z’s Anish Acharya thinks that legacy platforms like LinkedIn and GLG only show shallow signals with job titles. He believes that Ethos captures different sub-specializations through its voice interview process with curated questions.
“I think voice is the original form of human communication. Most people, you know, most people don’t know how to write their story down in a very succinct, compelling, and accurate way. Voice is a big unlock for Ethos,” Acharaya told TechCrunch over a call.
How Ethos is scaling its network
Ethos was founded by James Lo and Daniel Mankowitz in 2024. Lo previously worked at McKinsey and later at Softbank, where he worked on the transformation of companies like WeWork and Arm. Mankowitz worked as an AI researcher at DeepMind, where he worked on YouTube’s video compression algorithm, Gemini, and the AlphaDev sorting algorithm.
James Lo and Daniel Mankowitz Image Credits: Ethos AI by ivanweissImage Credits:James_L 0333 ©ivan weiss
Both founders arrived at tackling the problems of building an expert network from different angles. Lo always wanted to work on providing the right economic and employment opportunities to people. Mankowitz thought that the economy is a knowledge graph of people, companies, and products, and using the right algorithms, you can match these entities with each other.
“Traditional expert platforms almost purely focus on a mixture of job titles and job descriptions. What we observe is that most clients and most employers are not looking for a job title company. They’re looking for a specific skill and a specific capability. We also observed that, over time, looking for a skill and capability is going to gradually merge between the human economy and the agent economy,” Lo said.
Beyond the data provided by experts, Ethos also looks at other public sources like blogs and academic papers, along with social links to match companies with the right people.
The company also conducts interviews through its own platform using voice agents and extracts insights. Startups like Listen Labs and Outset already provide a way for companies to use conversational AI for interviews, offering some competition on this front. But Ethos thinks that its network of experts is better suited for certain clients than its competitors.
Ethos doesn’t name its client base, but said that top hedge funds, private equity firms, leading foundational AI labs, and enterprise consulting were already using its product. It’s taking 30% or more as a per-project fee from businesses, depending on the nature of the pr
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