Why Traditional Expert Networks Fall Short
Companies hunting for specialized knowledge face a frustrating paradox. They can access millions of professionals through LinkedIn or established expert networks like GLG, Third Bridge, and AlphaSights. Yet the results often miss the mark. A search for a pharmaceutical consultant might return dozens of candidates with the right job title but none with the specific combination of clinical experience, published research, and regulatory knowledge a project actually requires.
The root cause lies in how these platforms capture expertise. Most ask professionals to fill out a form listing their current role, past positions, and education. That data gets fed into a matching algorithm that pairs job titles with company queries. The system assumes a Vice President of Research at one firm possesses identical knowledge to a Vice President of Research at another. Anyone who has worked across industries knows how flawed that assumption is.
This shallow matching creates real costs. Hedge funds waste billable hours interviewing candidates who lack niche sub-specializations. Consulting firms deliver reports built on generic insights rather than deep domain knowledge. Private equity teams miss investment opportunities because they cannot find the one person who understands both industrial automation and Southeast Asian supply chains.
Ethos Reimagines Expert Matching with Voice Onboarding
London-based Ethos takes a fundamentally different approach. Instead of relying on static forms and job titles, the startup uses voice-powered conversations to capture what professionals actually know. This voice onboarding expert network asks curated questions that surface sub-specializations, practical experience, and contextual knowledge that no resume ever conveys.
The process works like this. When an expert joins Ethos, they do not fill out a long form. They speak with an AI-powered voice agent that guides them through a structured interview. The system asks about specific projects they have led, industries they have worked in, types of problems they have solved, and tools or methodologies they have mastered. A machine learning model processes these spoken responses, extracting signals that go far beyond job title data.
For companies seeking expertise, Ethos accepts natural language queries. A client does not need to translate their need into keywords. A private equity firm can ask, “Find people who worked at a funded startup backed by top-tier venture capitalists, where that startup solved for finance automation.” A pharmaceutical company can search for, “Doctors who specialize in oncology, have published papers on immunotherapy, and understand drug development timelines.” The platform matches these complex requests against the rich knowledge graph built from voice interviews.
How Voice Captures What Job Titles Miss
Anish Acharya, a partner at a16z who led the investment in Ethos, explains why voice matters. “Most people don’t know how to write their story down in a very succinct, compelling, and accurate way,” he said. “Voice is a big unlock for Ethos.”
Consider a real example. A data scientist might list “Machine Learning Engineer” as their job title. A standard platform matches them with any company needing machine learning expertise. But their voice interview might reveal they spent three years building recommendation systems for e-commerce platforms using PyTorch, worked with terabyte-scale datasets, and optimized models for latency-constrained mobile applications. That specificity transforms who they match with and what projects they can contribute to.
The voice onboarding process also captures soft signals. Tone, confidence, and the ability to explain complex concepts clearly all emerge during a conversation. A form cannot measure whether someone communicates effectively with executives or can translate technical jargon into business strategy. Voice can.
The $22.75 Million Series A Backed by a16z
Ethos announced a $22.75 million Series A round led by a16z, with participation from General Catalyst, XTX Markets, Evantic Capital, and Common Magic. The investment validates the thesis that voice onboarding expert network technology represents a genuine improvement over legacy approaches.
Acharya noted that existing platforms like LinkedIn and GLG only show shallow signals. Job titles and company names tell you where someone has been, not what they know how to do. Ethos captures the sub-specializations and nuanced expertise that drive real value in professional services, hedge fund research, and consulting engagements.
The funding will accelerate product development, expand the engineering team, and scale the voice onboarding infrastructure. Ethos currently operates with just eight people, a remarkably lean team for a platform processing tens of thousands of new expert signups each week.
Founders Bring Complementary Expertise from McKinsey and DeepMind
Ethos was founded in 2024 by James Lo and Daniel Mankowitz. Their backgrounds converge from opposite ends of the professional spectrum, creating a blend of business strategy and cutting-edge AI research.
Lo previously worked at McKinsey and later at SoftBank, where he contributed to the transformation of companies like WeWork and Arm. His experience taught him how difficult it is to find the right people for high-stakes projects. “I always wanted to work on providing the right economic and employment opportunities to people,” Lo said. He saw firsthand how traditional expert networks failed both the companies paying for insights and the professionals offering them.
Mankowitz brings a deep technical perspective from his time as an AI researcher at DeepMind. He worked on YouTube’s video compression algorithm, contributed to the Gemini project, and helped develop the AlphaDev sorting algorithm. He views the economy as a knowledge graph connecting people, companies, products, and capabilities. “Using the right algorithms, you can match these entities with each other,” he explained.
Their shared insight is that job titles and company names are weak proxies for actual capabilities. “Traditional expert platforms almost purely focus on a mixture of job titles and job descriptions,” Lo said. “What we observe is that most clients and most employers are not looking for a job title company. They’re looking for a specific skill and a specific capability.”
Building a Knowledge Graph Beyond Forms
Ethos does not limit its data collection to voice interviews. The platform also ingests public sources including academic papers, blog posts, social links, and professional publications. This creates a multidimensional profile of each expert’s knowledge, interests, and contributions.
When an expert publishes a research paper on reinforcement learning for robotics, Ethos indexes that content. When they speak at a conference about supply chain optimization, the system notes that too. Over time, the platform builds a rich picture of what each person actually knows and does, not just what their employer says they do.
This approach mirrors how AI labs map human talent. Companies like OpenAI, Google DeepMind, and Anthropic invest heavily in understanding who possesses specific capabilities. Lo noted that this trend benefits Ethos directly. “AI labs spending money to map human talent has been helping our cause,” he said. These labs are building professional services around understanding human expertise, which validates the broader market opportunity.
How Ethos Compares to Competing Platforms
Startups like Listen Labs and Outset already offer conversational AI for conducting interviews. These platforms help companies gather insights through automated voice conversations. However, Ethos differentiates itself by focusing specifically on building a network of vetted experts rather than offering a general-purpose interview tool.
The distinction matters. A general platform might help a company interview fifty customers for market research. Ethos aims to connect that same company with precisely the two or three experts who have deep knowledge of their specific industry, technology, and competitive landscape. The network effect is central to the value proposition.
Ethos also charges a premium for this precision. The company takes 30 percent or more as a per-project fee from businesses, depending on the nature of the engagement. That fee structure reflects the higher value of accurately matched expertise compared to the hit-or-miss results of traditional platforms.
Growth Metrics and Market Traction
Ethos reports that approximately 35,000 people join the platform each week. The company sends invitations to individuals they believe can benefit from and contribute to the network. This selective approach maintains quality while scaling rapidly.
The company is on track for eight-figure annualized revenue, a remarkable achievement for a startup founded only in 2024 with a team of eight. Clients include top hedge funds, private equity firms, leading foundational AI labs, and enterprise consulting organizations. Ethos does not publicly name its client base, but the caliber of firms using the platform speaks to the value it delivers.
The revenue trajectory suggests that companies are willing to pay a premium for better matches. When a hedge fund needs to understand a niche technology before making a multi-million dollar investment, paying 30 percent on a project fee is trivial compared to the cost of making the wrong decision.
The Problem with Job Title Matching
To understand why Ethos matters, consider how traditional expert networks operate. A company submits a request: “I need someone who worked at Tesla on battery technology.” The platform searches its database for profiles containing “Tesla” and “battery” in the job history. It returns a list of candidates who held roles like “Senior Battery Engineer” or “Director of Energy Storage.”
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The problem is that job titles are inconsistent across companies. One organization calls a role “Data Scientist” while another uses “Machine Learning Engineer” for identical work. A “VP of Product” at a large enterprise might have strategic oversight while a “VP of Product” at a startup might write code and conduct user interviews. The title alone tells you almost nothing about actual capability.
Furthermore, people accumulate knowledge that their current job title does not reflect. A marketing director might have a PhD in computational chemistry from earlier in their career. A software engineer might have founded a biotech startup on the side. These hidden capabilities represent enormous value that traditional matching systems simply miss.
How Voice Onboarding Surfaces Hidden Expertise
The voice onboarding expert network model solves this by asking the right questions. When an expert joins Ethos, the voice agent does not ask for their job title. It asks about the hardest problem they have solved in the last year. It asks what industries they know deeply, what methodologies they apply, and what kind of projects excite them.
Consider a hypothetical expert named Sarah. Her LinkedIn profile says she is a “Senior Product Manager at a fintech company.” A traditional platform matches her with any fintech project. But her Ethos voice interview reveals that she spent four years building compliance automation tools for European banking regulations, speaks fluent German, has consulted for three startups that achieved Series B funding, and published a white paper on open banking APIs. Now the platform can match her with a private equity firm evaluating an investment in a German compliance software startup. That match would never happen through job title alone.
The Technical Infrastructure Behind Ethos
Ethos combines several AI technologies to make its platform work. Natural language processing models extract structured data from unstructured voice conversations. Knowledge graph algorithms connect people, skills, companies, and projects into a searchable network. Recommendation systems rank matches based on relevance and predicted fit.
The voice agents themselves use conversational AI to conduct natural, flowing interviews. They adapt questions based on previous answers, probing deeper into areas where the expert shows particular depth. This dynamic interview process generates far richer data than a static form ever could.
Mankowitz’s background at DeepMind informs the technical approach. The platform likely uses transformer-based models similar to those powering modern language AI, fine-tuned on the specific task of extracting expertise signals from spoken conversations. The knowledge graph architecture reflects his view that the economy is fundamentally a graph of interconnected entities.
Challenges and Risks for Ethos
Growing an expert network presents significant challenges. The platform must attract enough high-quality experts to make the network valuable for clients, while also attracting enough clients to make the network valuable for experts. This chicken-and-egg problem is common in marketplace businesses.
Ethos addresses this by proactively inviting experts it identifies through public sources. Rather than waiting for professionals to discover the platform, the team reaches out to individuals whose expertise aligns with client demand. This targeted approach accelerates network growth while maintaining quality standards.
Another challenge is maintaining data freshness. Expertise evolves. Someone who was an expert in Python development two years ago might now work primarily in Rust. Ethos likely needs to periodically re-interview experts or update profiles based on new publications and projects to keep its knowledge graph current.
Competition also looms. Listen Labs and Outset could expand into the expert network space. LinkedIn could add voice-based profiling features. GLG and other incumbents could invest in AI matching technology. Ethos must continue innovating to stay ahead.
What This Means for the Future of Expert Networks
The voice onboarding expert network approach represents a broader shift toward AI-native professional services. As language models become more capable, the gap between what a job title communicates and what a person actually knows becomes more costly. Companies that can accurately map human expertise will have a significant advantage.
Ethos is not just building a better expert network. It is building infrastructure for the knowledge economy. When AI agents need to find humans with specific capabilities, they will query systems like Ethos rather than scanning resumes. The platform’s knowledge graph could become a foundational layer for how organizations discover and engage talent.
Lo sees this convergence between the human economy and the agent economy. “Over time, looking for a skill and capability is going to gradually merge between the human economy and the agent economy,” he said. Ethos positions itself at the center of that merger.
Practical Implications for Professionals
For experts considering joining Ethos, the platform offers a way to monetize knowledge that their current job title does not capture. A voice interview can surface capabilities that would otherwise remain invisible to the market. This can lead to consulting opportunities, advisory roles, and speaking engagements that a traditional resume would never generate.
The platform also rewards depth over breadth. Someone with deep expertise in a narrow domain may be more valuable than a generalist with a prestigious job title. Ethos’s matching algorithm prioritizes actual knowledge and experience over brand names and credentials.
For companies, the message is clear. If you are paying for expert insights based on job title matching, you are likely overpaying for underqualified advice. Platforms that use voice onboarding expert network technology can deliver more precise matches, saving time and improving decision quality.
