FUNDING

Ethos Raises $22.75M to Rework Expert Discovery with Voice-Led AI Onboarding

The company is already working with clients across sectors such as finance, consulting, healthcare, and technology, where access to precise knowledge can influence research, strategy, and decision-making.

By Donna Joseph
May 7, 2026 1:40 AM
Ethos Raises $22.75M to Rework Expert Discovery with Voice-Led AI Onboarding Photo by SBR

Summary
  • Ethos has raised $22.75 million in a Series A round to build an expert network that moves beyond CVs and keyword searches, using voice-led onboarding and AI to capture and structure professional knowledge in greater detail.
  • The platform replaces traditional profile building with structured voice interviews, where experts describe their experience conversationally, allowing machine learning systems to extract and organize deeper signals of expertise.
  • AI-driven matching interprets voice transcripts and external data to connect organizations with highly specific expertise, helping surface professionals whose skills often remain hidden in conventional discovery systems.

LONDON, May 6, 2026 — London-based startup Ethos has raised $22.75 million in a Series A round as it builds a new kind of expert network designed to capture and interpret professional knowledge in greater depth. The company focuses on how expertise is recorded and matched, moving beyond static CVs and keyword searches toward voice-led onboarding and AI-driven profiling.

Founded in 2024 by James Lo and Daniel Mankowitz, Ethos is built on the idea that traditional expert discovery systems fail to represent the full scope of an individual’s experience. Standard profiles tend to rely on job titles, short summaries, and manually entered data, which often miss context, nuance, and less visible forms of expertise.

Ethos addresses that gap by asking experts to speak rather than type.

Voice Onboarding Captures a Deeper Professional Context

Ethos uses a voice-based onboarding process. New users participate in structured, AI-guided interviews that prompt them to describe their work, decisions, and domain exposure in detail. Instead of completing forms, experts respond through conversational exchanges.

This process captures how individuals describe their experience in their own words. It surfaces details that often do not appear in written profiles, including project-level contributions, cross-functional knowledge, and informal specializations developed over time.

The spoken responses are transcribed and processed by machine learning systems that extract meaning, classify expertise, and organize the data into structured profiles. These profiles go beyond traditional resumes, offering a more detailed representation of what a person knows and has done.

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AI Systems Refine Matching Beyond Titles and Keywords

Ethos uses artificial intelligence to interpret both structured inputs and unstructured data, including voice transcripts and external signals such as publications or professional output. The system then matches experts to highly specific queries from organizations.

Instead of filtering candidates based on titles or years of experience, the platform evaluates relevance at a more detailed level. For example, it can identify professionals who have worked on a narrow type of financial transaction, contributed to a specific research topic, or operated in a particular market condition.

This allows organizations to locate expertise that would otherwise remain hidden in conventional databases. It also reduces reliance on manual sourcing, where human intermediaries attempt to interpret client requests and identify suitable experts.

Ethos is already working with clients across sectors such as finance, consulting, healthcare, and technology, where access to precise knowledge can influence research, strategy, and decision-making.

Growth Reflects Demand for Richer Knowledge Mapping

The company reports that thousands of experts are joining the platform each week, spanning both traditional professions and skilled trades. This breadth reflects a broader demand for systems that recognize different forms of expertise, not limited to formal credentials or corporate roles.

As artificial intelligence becomes more embedded in knowledge work, there is growing interest in tools that can map human expertise with greater accuracy. Ethos positions voice as a key input layer in that process, capturing nuance that text-based systems often miss.

The funding will support further development of the platform, including improvements to its onboarding flows, data processing systems, and matching capabilities. Expansion into new markets and categories of expertise is also part of the company’s roadmap.

Founders Bring AI and Advisory Experience

James Lo brings experience from consulting and investment roles, including work at McKinsey and SoftBank, where he focused on organizational design and strategy. Daniel Mankowitz has a background in artificial intelligence research, having worked at DeepMind on machine learning systems.

Their shared experience shapes the technical and conceptual direction of Ethos. The platform treats expertise as something that can be measured, structured, and queried with greater precision, rather than inferred from surface-level indicators.

Ethos also integrates external data sources to enrich its profiles over time. Publications, digital footprints, and other signals are used alongside interview data to build a more complete view of each expert’s capabilities.

Ethos uses a voice-based onboarding process. New users participate in structured, AI-guided interviews that prompt them to describe their work, decisions, and domain exposure in detail. Instead of completing forms, experts respond through conversational exchanges.


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