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.
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,” Acharya 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.
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 project. The company noted that it’s on track for “an eight-figure annualized revenue” but didn’t provide specific numbers.
It also didn’t say how many experts are on the platform, but said that roughly 35,000 people are joining each week. (Ethos sends invites to people whom they think can benefit from it.)
One challenge for the startup is growing an expert user base that’s relevant to its clients. The company said that AI labs spending money to map human talent has been helping its cause.
“Our perspective here is the AI labs have — are pointing a giant capital gun at every economically valuable occupation in the world. They’re trying to map out every profession. And so that’s an amazing tailwind for us,” Lo said.
He noted that these labs are building professional services in areas of law, health, finance, and management, so they would want all kinds of experts in these networks to build out their models and get feedback about their products and strategy.
The company has eight people on its team now, and its goal is to keep the team compact while scaling up.
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