What Is an AI-Native Expert Network? Definition & 2026 Landscape

How is it different from a traditional expert network?

Traditional expert networks are coordination businesses. A typical engagement involves a human associate interpreting the request, searching a proprietary database, calling candidates, scheduling the consultation, and running compliance checks. That labor is paid for through annual contracts — commonly starting at five figures — and per-call rates frequently cited in the $1,000–1,500 per hour range.

An AI-native network automates most of that coordination: sourcing runs as search across public and proprietary data, outreach and screening are agent-driven, and in some models the interview itself is conducted by an AI. When coordination costs fall, the commercial floor falls with them — which is why most AI-native providers can price per engagement or per study instead of requiring an annual minimum.

Dimension	Traditional expert network	AI-native expert network
Who finds the expert	Human associates searching an internal database	AI search and agent-driven outreach, often beyond the pool
Who runs the interview	The client's analyst	The client's analyst or an AI interviewer, by model
Typical commercial model	Annual contract plus per-call fees	Per engagement, per study, or subscription
Typical output	The call itself; notes are the client's job	Structured transcripts, coverage scores, or time-series data

Every network runs on an expert pool — the models differ in what they sell

All expert networks, traditional and AI-native, maintain a pool of experts. The strategic difference is the product built on top of that pool. Three models cover the market:

Two professionals in an expert consultation

Model 01

The access model — selling the conversation

The network finds and schedules the expert; the client's analyst conducts the call. This is the classic GLG, AlphaSights, and Guidepoint model. Ethos is the AI-native take on its front end: AI-driven expert intake and matching, with clients still running the calls themselves.

An archive of research output representing transcript libraries

Model 02

The coverage model — selling the library

The network interviews its pool continuously and sells the accumulated output: transcript libraries and tracked data. Tegus (now part of AlphaSense) and Third Bridge Forum built this with human analysts; Qualitate is the AI-native version, running an AI moderator over a standing panel to produce quantified, time-series intelligence.

A team working backward from a research question

Model 03

The question-driven model — selling the answer

The engagement starts from a client's specific question. The network works backward to who would know, sources them, conducts a structured interview, and delivers organized findings. Analyst-led research boutiques are the human version; Nomais is an AI-native version, with active sourcing beyond a standing pool and AI-conducted interviews delivered against the client's question framework.

The models are not exclusive: one provider can operate several on the same pool. Nomais, for example, offers both direct analyst-to-expert calls (access) and AI-conducted structured interviews (question-driven).

Who are the AI-native expert network companies in 2026?

As of July 2026, the most visible companies, by the layer they automate:

Ethos — AI-driven expert intake and natural-language matching, built on voice onboarding of experts. Clients conduct calls on-platform. Raised a $22.75M Series A led by a16z in May 2026.
Qualitate — an AI moderator conducts structured discussions with a standing expert panel at scale; output is sold as quantified, time-series data covering 10,000+ public and private companies. Raised a $7M seed led by IA Ventures and Crew Capital in April 2026.
Checkmate Research— an AI interviewer that joins expert calls booked through a client's existing channels and conducts them autonomously, returning transcripts and summary memos; emphasizes MNPI screening of transcripts.
Plausity — parallel AI voice interviews for deal diligence, with synthesis traceable back to individual transcript moments.
Nomais— question-driven and end-to-end: actively sources the practitioners who can answer a specific research question (not limited to a standing pool), runs structured 45–60 minute AI interviews, and delivers transcripts organized by the client's question framework — alongside direct expert calls for teams that want the conversation themselves. Per-engagement pricing, no annual contract.

Adjacent but distinct: AI-moderated consumer-research platforms such as Outset and Listen Labs serve UX and consumer insight teams rather than investment research, and the incumbent networks are adding AI features (search, summarization) to human-run workflows.

Which model fits which research question?

Monitoring known companies over time — the coverage model fits: standing panels produce comparable data at regular intervals.
A specific question about a niche, private, or off-coverage subject — the question-driven model fits: the practitioners who know are found for the question, not filtered from whoever is already listed.
A conversation your analyst wants to steer live — the access model fits: the value is in your analyst's own judgment during the call.

Most research programs mix models — a standing tracker for breadth, question-driven engagements for the questions the tracker can't reach.

What does an AI-native expert network cost?

There is no standard industry price list. Traditional networks typically combine an annual contract with per-call fees; AI-native providers mostly price per engagement, per study, or by subscription. Qualitate stated in its April 2026 funding announcement that its studies run at roughly one-third the cost of traditional expert networks. Nomais quotes each engagement individually — pricing depends on expert seniority and domain — with no annual contract, and clients review vetted expert profiles before confirming an engagement.

Frequently asked questions

Is an AI-native expert network the same as an expert marketplace?

No. An expert marketplace is a self-service pool: experts register, clients browse and book whoever is listed. An AI-native expert network automates the network's own workflow — sourcing, vetting, interviewing, delivery — and in several models can reach practitioners who never registered on any platform.

Do AI-native expert networks still involve human expert calls?

Often, yes. In access-model networks the client's analyst still conducts the call — AI replaces the coordination around it. In question-driven and coverage models, an AI agent may conduct the interview itself. Several providers, including Nomais, offer both.

Are AI-native expert networks compliant for investment research?

The compliance obligations are the same as for traditional networks: sanctions and PEP screening, conflict-of-interest checks, and controls around material non-public information. Automation changes who executes the workflow, not the obligations attached to it. Buyers should apply the same compliance review they would apply to any expert-network engagement.

What is the difference between Ethos, Qualitate, and Nomais?

As of mid-2026: Ethos applies AI to expert intake and matching — clients then run the calls themselves. Qualitate runs an AI moderator over a standing expert panel and sells quantified, time-series output. Nomais starts from a client's specific question, actively sources the practitioners who can answer it, conducts structured AI interviews, and delivers transcripts organized by the client's question framework — and also offers direct analyst-to-expert calls.

Company facts on this page are drawn from public sources — company websites, funding announcements, and press coverage — as of July 2026 and may have changed. All trademarks belong to their respective owners; Nomais is not affiliated with the companies mentioned. Last updated July 7, 2026.