Ethos raises $22.75M from a16z for its expert network with voice onboarding
Source: TechCrunch
Overview
When companies need opinions or advice on a project, they often turn to LinkedIn or expert networks such as GLG, Third Bridge, or AlphaSights. However, these platforms frequently fail to deliver high‑quality inputs.
Today’s expert networks typically ask experts to fill out a form based on their job title, which is then used to match them with companies. London‑based Ethos believes AI can improve both sides of this experience. For experts, Ethos offers voice‑powered onboarding that asks a broader set of questions and captures more data about their knowledge beyond job titles. For companies, the platform can better match natural‑language queries thanks to the richer dataset.
Ethos says its voice‑based onboarding enables it to answer complex client questions such as, “Find me people who worked at a funded startup backed by A‑grade investors solving for finance automation.” Another example is a pharma company searching for doctors who specialize in a specific area, have published papers on the subject, and understand drug development.
Image credit: Ethos
Funding round
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 notes that legacy platforms like LinkedIn and GLG only surface shallow signals from job titles. He believes Ethos captures finer sub‑specializations through its voice interview process with curated questions.
“I think voice is the original form of human communication. Most people don’t know how to write their story down in a succinct, compelling, and accurate way. Voice is a big unlock for Ethos,” Acharya told TechCrunch.
How Ethos is scaling its network
Founders
Ethos was founded by James Lo and Daniel Mankowitz in 2024. Lo previously worked at McKinsey and SoftBank, contributing to transformations at companies such as WeWork and Arm. Mankowitz was an AI researcher at DeepMind, where he worked on YouTube’s video compression algorithm, Gemini, and the AlphaDev sorting algorithm.
Image credit: Ethos AI by Ivan Weiss
Lo is motivated by providing the right economic and employment opportunities, while Mankowitz views the economy as a knowledge graph of people, companies, and products that can be matched with the right algorithms.
“Traditional expert platforms focus almost purely on job titles and descriptions. Most clients aren’t looking for a job title; they’re looking for a specific skill and capability. Over time, skill‑based matching will merge the human economy with the agent economy,” Lo explained.
Technology and data
Beyond expert‑provided data, Ethos also ingests public sources such as blogs, academic papers, and social links to improve matching. The company conducts voice‑agent interviews on its platform to extract insights. Startups like Listen Labs and Outset offer conversational‑AI interview tools, but Ethos argues its curated expert network is better suited for certain clients.
Clients and revenue
Ethos does not disclose its client list, but it mentions that top hedge funds, private‑equity firms, leading foundational AI labs, and enterprise consulting groups are already using the product. The firm takes a 30 % or higher per‑project fee, depending on the project’s nature, and is on track for “an eight‑figure annualized revenue,” though specific numbers were not provided.
Image credit: Ethos
Growth and challenges
The platform’s expert pool is growing rapidly, with roughly 35,000 new experts joining each week through targeted invitations.
A key challenge is expanding an expert user base that aligns with client needs. Lo notes that AI labs are investing heavily in mapping human talent, which creates a tailwind for Ethos:
“AI labs are pointing a giant capital gun at every economically valuable occupation. They’re trying to map out every profession, which is an amazing tailwind for us.”
These labs are building professional services in law, health, finance, and management, and they require diverse experts to train models and provide feedback.
Team
Ethos currently has eight employees and aims to keep the team compact while scaling.