Ethos raised $22.75 million Series A from a16z. Eight employees. 35,000 experts onboarded per week via voice interviews. Eight-figure annualized revenue. A per-project fee of 30% or more from clients that include top hedge funds, private equity firms, and leading AI labs.

This is what a focused AI talent mapping business looks like at Series A scale.

The Expert Network Problem

Traditional expert networks — GLG, Third Bridge, Alphasight — match companies with specialists using job titles and basic forms. The problem: job titles don't capture the full scope of an individual's expertise and capabilities.

A query like "find me people who worked at a funded startup by A-grade investors solving for finance automation" requires matching across multiple dimensions — investment history, technical domain, organizational context, and specific problem-solving experience. Job titles can't encode that. Forms certainly can't.

Ethos's insight: voice interviews capture sub-specializations that job titles miss. Instead of checking boxes, experts talk about what they actually know across multiple domains.

Why Voice Is the Right Input Format

a16z partner Anish Acharya put it simply: "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."

This is true. The gap between what people know and what they can write about is significant. Voice interviews capture nuance — hesitation, emphasis, tangential expertise — that structured forms cannot.

The voice-onboarding approach also surfaces the "and also" dimension of expertise. A doctor who has written papers on drug development. An engineer who has experience with regulatory compliance. These secondary specializations matter for complex queries but are invisible in traditional expert network profiles.

The Business Model

Ethos charges 30% or more per project. The clients are top hedge funds, private equity firms, leading AI labs, and enterprise consulting companies. These are sophisticated buyers paying premium rates for precise expert matching — not generic referrals.

35,000 experts onboarded per week through invitations. That's 1.4 million experts per year at current velocity. The platform is taking 30% of a per-project fee from sophisticated clients, on track for eight figures in annualized revenue with eight employees.

These economics are only possible because the voice-onboarding is partially automated — the AI conducts and processes the interviews. A traditional expert network would need armies of coordinators to achieve this scale.

The AI Labs Talent Mapping Angle

The most interesting client segment: AI labs building professional services in law, health, finance, and management. These companies are investing heavily in mapping human talent — understanding who knows what, where the expertise gaps are, and how to build services augmented by human specialists.

Ethos is both a tool for these labs and a beneficiary of their talent mapping investments. As AI labs scale professional services, they need to know which human experts to partner with, which to hire, and which to reference in AI-generated outputs. Expert networks that map human expertise are becoming AI infrastructure.

What This Means for AI Builders

The Ethos story reveals several patterns:

Voice AI has enterprise B2B applications beyond transcription: Voice as an input format for capturing expertise — not just for meetings or note-taking — is a genuine use case. The form factor matters because it changes what can be captured.

Talent mapping is becoming AI infrastructure: AI labs building professional services need to understand the human expertise landscape. Companies that map human knowledge — who knows what, where expertise lives, how to reach it — are building the reference layer for AI-augmented professional work.

Expertise verification at scale is the hard problem: The quality of AI-generated professional advice depends on the quality of human expertise it references. Ethos is solving the upstream problem: ensuring that human expertise is accurately captured and matchable. This is the prerequisite for reliable AI professional services.

Eight employees. Eight-figure revenue. $22.75M raised. The expert network is being rebuilt for the AI era — and the buyer cohort is AI labs themselves.