When companies are looking for opinions or advice on a project, they tend to go to LinkedIn or use expert networks like GLG, Third Bridge or Alphasights. But they often don’t find quality inputs, despite their searches.
Today, these sites ask experts to fill out a form based on their job title, which is then used to match them with companies that need their help.
based in London Ethos believes that artificial intelligence can improve both sides of this experience. For experts, it offers voice integration to ask a wider set of questions and get more data about their knowledge in various areas not covered by their job titles. For companies, Ethos can better match the natural language queries asked by these organizations to their work, thanks to the wider range of data it has collected.
Ethos said its voice-based onboarding and data enables it to answer complex customer questions, such as: “Find me people who worked at an A-backed startup solving funding automation solutions.”
Another example the startup gave was how a pharmaceutical company using its platform could seek out doctors who specialize in a certain area, but who have also written papers on the topic or have an understanding of drug development.
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.
Anish Acharya of a16z believes that legacy platforms like LinkedIn and GLG only show shallow signals with job titles. He believes that Ethos captures different subspecialties through the 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 in a very concise, compelling and accurate way. Voice is a big unlock for Ethos,” Acharaya told TechCrunch after 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 such as WeWork and Arm. Mankowitz worked as an AI researcher at DeepMind, where he worked on YouTube’s video compression algorithm Gemini and classification algorithm AlphaDev.
Both founders ended up approaching the problems of building a network of experts from different perspectives. Lo has always wanted to work to provide proper economic and employment opportunities to people. Mankowitz thought that the economy is a knowledge graph of people, companies, and products, and by using the right algorithms, you can map these entities to each other.
“Traditional expert platforms focus almost purely on a combination of job titles and job descriptions. What we’re seeing is that most customers and most employers aren’t looking for a job title company. They’re looking for a specific skill and a specific ability. We’ve also noticed that, over time, skill and ability search will gradually merge between the human economy and the agent economy.”
Beyond data provided by experts, Ethos also looks at other public sources such as 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 information. The start-ups like Listen to Workshops and Principle already provide a way for companies to use conversational AI for interviews, offering some competition on that front. However, Ethos believes that its network of experts is more suitable for specific clients than its competitors.
Ethos wouldn’t name its customer base, but said top hedge funds, private equity firms, top fundamental AI labs and business consultancies already use its product. He receives 30% or more as a per-project fee from businesses, depending on the nature of the project. The company noted that it is on track for “eight-figure annual revenue,” but did not provide specific numbers.
He also did not say how many experts are on the platform, but said about 35,000 people join each week. (Ethos sends invitations to people who think they can benefit from this.)
A challenge for the startup is developing a dedicated user base that is relevant to its customers. The company said AI labs spending money on mapping human talent helped its cause.
“Our perspective here is that the AI labs have — they’re pointing a giant capital weapon at every economically valuable occupation in the world. They’re trying to map every occupation. And so that’s an amazing tailwind for us,” Lo said.
He noted that these labs create professional services in the fields of law, health, finance and management, so they would like all kinds of experts in these networks to build their models and get feedback on their products and strategy.
The company has eight people on its team now, and its goal is to keep the team compact while scaling.
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