Ethos Raises $22.75M to Replace Job-Title Matching With AI Voice Interviews

Ethos, a London-based startup co-founded in 2024, has raised $22.75 million in Series A funding led by Andreessen Horowitz to rethink how companies locate expert advisors. Rather than relying on job titles and static forms, Ethos deploys AI-driven voice interviews to build richer professional profiles — a structural wager that incumbent expert networks have long been extracting the wrong signal from their members.

The Job-Title Gap in Professional Matching

Platforms like GLG, Third Bridge, and Alphasights have built their businesses on a simple lookup model: take an expert’s job title and employer, then match against company requests. According to TechCrunch, this leaves substantive knowledge undiscovered on both sides. Clients receive shallow matches; professionals never get credit for expertise that falls outside their formal roles.

Ethos attacks the data problem at its origin. Voice onboarding interviews, guided by curated questions, surface cross-domain knowledge — enabling client queries that combine institutional background, domain focus, and thematic depth in a single natural-language request. TechCrunch reports a pharma-sector example: identifying physicians whose clinical specialization is reinforced by published research and hands-on exposure to drug development processes.

Voice Onboarding as Structural Advantage

a16z partner Anish Acharya frames the voice interface as reflecting something fundamental about communication itself. “Most people don’t know how to write their story down in a very succinct, compelling, and accurate way,” he told TechCrunch. “Voice is a big unlock for Ethos.” The implication is that text-based profiles, however detailed, are optimized for self-presentation rather than knowledge retrieval — and spoken interviews elicit a structurally different, more actionable dataset.

a16z led the funding round; General Catalyst and XTX Markets joined alongside Evantic Capital and Common Magic.

Why This Matters

Expert networks represent a multi-billion-dollar market where AI investment has concentrated on matching algorithms rather than the quality of the underlying profiles being matched. Ethos inverts that priority. If voice-derived data is structurally richer than form-based alternatives, the resulting accuracy gap becomes difficult for incumbents to close through algorithmic refinements alone — giving Ethos a potential compounding advantage as its network scales.