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Ethos, a London‑based AI startup, closed a $22.75 million Series A round on May 6, 2026, led by a16z with participation from General Catalyst, XTX Markets, Evantic Capital, and Common Magic. The capital will accelerate its voice‑powered expert onboarding system, which promises richer skill signals than traditional job‑title based platforms. Voice‑Powered Onboarding Redefines Expert Matching Ethos replaces static forms with conversational interviews, allowing experts to describe their knowledge in their own words. The platform then parses these recordings to extract granular competencies, enabling companies to pose natural‑language queries such as “find experts who worked at a funded startup backed by A‑grade investors solving finance automation.” Funding Numbers and Investor Line‑up Highlight Market Confidence Series A amount: $22.75 million Lead investor: a16z (Andreessen Horowitz) Other participants: General Catalyst, XTX Markets, Evantic Capital, Common Magic Founders: James Lo (ex‑McKinsey, SoftBank) and Daniel Mankowitz (former DeepMind AI researcher) Implications for the Expert‑Network Landscape and AI‑Driven Talent Mapping By capturing sub‑specializations through voice, Ethos challenges legacy platforms that rely on shallow job‑title signals. The startup also enriches its graph with public data—blogs, academic papers, and social links—creating a hybrid human‑AI talent map. Early adopters include top hedge funds, private‑equity firms, foundational AI labs, and enterprise consultancies, which pay a 30%+ per‑project fee. Future Outlook: Scaling, Revenue Targets, and Competitive Position Ethos aims for “eight‑figure annualized revenue” while keeping its team lean (currently eight members). With roughly 35,000 new experts joining each week, the company plans to deepen its voice‑agent interview pipeline and expand into sectors such as law, health, and finance. Competitors like Listen Labs and Outset offer conversational AI interview tools, but Ethos bets its curated expert network will deliver higher‑value matches for complex, domain‑specific queries.