Expert Network Companies Compared (2026): GLG, Tegus, AlphaSense, Dialectica & the AI-Moderated Shift

The expert network industry has been restructuring quietly. What started as a few large broker-managed networks connecting analysts to industry experts has expanded into a fragmented landscape of incumbents, specialists, data hybrids, and — most recently — platforms built around AI-moderated calls. If you run an expert network, or if you are evaluating which networks to work with, understanding how the major players differ is no longer optional context. It determines which clients you can win and whether your operating model is durable.

This comparison covers the major expert network companies — GLG, AlphaSense (and its Tegus transcript-library product), and Dialectica — on the dimensions that matter operationally: expert supply, vetting model, call execution, compliance infrastructure, and pricing structure. It then addresses the shift happening at the top of the market: the move to AI-moderated calls, what it means, and what boutique networks can do about it.

How Do Expert Networks Work?

Expert networks maintain a supply of professionals — former executives, industry veterans, technical specialists — who agree to speak with clients (typically investment analysts, consultants, or corporate strategists) for a fee. The core value exchange is simple: clients get access to domain expertise they do not have internally; experts get compensated for sharing knowledge that would otherwise go unmonetized.

In practice, the mechanics differ substantially across networks. The traditional model works through broker intermediation: a client submits a research request, a network recruiter searches a proprietary database, proposes expert candidates, coordinates scheduling, and manages the call itself. The broker absorbs coordination friction but adds time and cost at every step.

Newer models have progressively unbundled these functions. Self-service platforms give clients direct database access. AI-moderated platforms replace human coordination and call moderation with automated systems. Some data providers have acquired expert networks and bolted them onto their existing platforms, creating hybrids that sell both structured data and expert access through a single subscription.

Four dimensions determine whether a network is operationally useful for any given research purpose:

Expert supply and coverage. Raw headcount is a weaker signal than coverage in specific sectors. A network with 800,000 profiles and thin coverage in healthcare technology is less useful for a healthcare-focused client than a specialist network with 40,000 deeply vetted practitioners in that domain.

Vetting model. Who confirmed that the expert actually knows what their profile claims? Self-reported profiles are common; recruiter judgment is inconsistent; AI-conducted vetting calls are systematic and auditable. The vetting model determines the call quality a client should expect.

Call execution. Who conducts the call, how it is documented, and what output is produced. A broker-coordinated human call produces raw audio and analyst notes. An AI-moderated call produces a structured transcript, automated summary, and compliance record automatically.

Compliance infrastructure. How conflicts of interest are screened, how MNPI exposure is managed, and what audit trail is produced. For regulated investment managers, this is not a secondary consideration.

GLG: Scale and Sector Depth

GLG (Gerson Lehrman Group) remains the largest expert network by headcount, with over a million professionals in its database spanning most major industries and geographies. It operates through a combination of proprietary network members and on-demand recruitment for requests that fall outside its existing coverage.

GLG's vetting model relies on recruiter judgment and compliance screening conducted at onboarding. Experts are recruited into topic-specific "councils" and their credentials are verified before they are made available to clients. The compliance infrastructure is comprehensive by broker-model standards — GLG has invested significantly in MNPI screening and conflict-of-interest protocols over the years following early-2010s regulatory scrutiny.

Call execution at GLG is broker-managed: a research manager proposes candidates, coordinates scheduling, and may facilitate the call. Pricing is per-engagement, typically bundled into annual subscription agreements that give clients a set number of engagements per year with overage pricing for volume above the contracted tier.

Where GLG is strongest: clients with broad, multi-sector research needs who value relationship continuity with a network that has genuine global reach. The broker model works well for relationship-dependent conversations where the expert's willingness to engage depends on GLG's long-standing recruitment relationship.

Where GLG shows friction: high-volume structured research programs (channel checks, vetting surveys, repeated expert screenings) where broker coordination adds time and cost that does not scale linearly with the research need.

Tegus: AlphaSense's Transcript Library

Tegus is AlphaSense's transcript-library product, acquired in 2024. As part of the AlphaSense platform, it operates as a combination of searchable prior-interview database and on-demand expert access, built around the thesis that a large, indexed database of prior expert conversations has standalone research value. When a client needs to understand a company or sector, the first step on Tegus is searching for relevant prior interviews before commissioning new ones.

Fit within the transcript library varies by construction: each interview was scoped to whoever commissioned the original call, so how closely a given transcript lands on your own question varies, coverage is uneven across companies and categories, and older transcripts describe conditions that may have moved on. On-demand expert connections follow a more traditional broker process. The interview-plus-transcript model means research teams are not always starting from zero: prior context on an expert or topic is often available before a new call.

AlphaSense has evolved the Tegus pricing toward research team subscriptions rather than pure per-engagement billing, which suits firms that want predictable annual spend and run high volumes. The transcript library is the distinctive asset of this AlphaSense product.

Where the Tegus product is strongest: investment teams that run recurring research on a defined universe of companies and sectors, where the transcript library compounds in value over time. The prior-interview context makes new calls more efficient because the analyst arrives with domain familiarity built from past conversations.

Where the Tegus product shows friction: research needs that fall outside the existing transcript coverage, where the platform reverts to a more traditional expert-matching model without the library advantage.

For a direct comparison, see Tegus vs GLG.

AlphaSense: Document Search Plus Native AI-Moderated Calls

AlphaSense started as a document search platform for financial research, then acquired its way into expert networks — Stream in 2021, and Tegus outright in 2024 for $930 million — before building AI-moderated call capabilities in-house. Today it runs AI-led expert interviews at scale, with transcripts and summaries indexed alongside its document corpus.

Where AlphaSense is strongest: research teams that want expert call content integrated directly into a broader document and data search environment, especially for coverage of public companies where its document corpus is deep.

Where AlphaSense shows friction: bespoke research needs outside its coverage depth, and clients who run expert calls across domains where the document corpus is thinner.

For a full breakdown of AlphaSense's competitors, how its Tegus library fits in, and what its AI-moderated build means if you operate an expert network, see AlphaSense alternatives and competitors.

Dialectica: Specialist Supply and Project Flexibility

Dialectica operates with a model closer to traditional broker intermediation, with a focus on specialist coverage in private markets, emerging geographies, and niche industry verticals.

The vetting model is recruiter-managed and project-specific: Dialectica reports that over 60% of the experts it places are recruited fresh for each engagement rather than drawn from a pre-existing database, which produces high relevance for narrow requests but requires lead time for each search. Pricing tends to be project-based or per-engagement rather than large annual subscriptions, which suits research teams with episodic rather than continuous expert call needs.

Dialectica has positioned itself around close coordination with a dedicated recruiter team on each project.

Where Dialectica is strongest: niche specialist research, private market diligence, and geographies where coverage comes from a dedicated recruiter team sourcing to the brief.

Where Dialectica shows friction: high-volume, repeatable structured research where systematic automation would reduce costs and the boutique coordination model does not scale.

For a direct comparison, see Dialectica vs GLG.

The Three Operating Models

Underneath the brand names there are three distinct models, and most networks are a variant of one of them. The broker-managed network operates at global scale on coverage breadth and relationship access — GLG is the archetype. The transcript-library model leads with a searchable database of prior expert conversations and adds on-demand access as a complement — Tegus, now an AlphaSense product following the 2024 acquisition. The integrated data platform treats expert calls as one native capability inside a broader structured search environment — AlphaSense.

For research teams choosing between them, the deciding factor is usually the research workflow. Teams that want flexible, relationship-dependent access to a global expert supply lean toward GLG. Teams that want a searchable library of prior transcripts use AlphaSense's Tegus product; teams that want expert content integrated with broad document search use the wider AlphaSense platform.

For expert network operators who serve research teams, understanding these differences is essential — because each model implies different client expectations around call execution speed, output format, and compliance documentation.

The AI-Moderated Calls Shift

The AlphaSense build is not an isolated technology bet. It reflects a directional shift in how expert calls are conducted at scale: away from broker-mediated human calls toward AI-moderated calls that automate the coordination, moderation, transcription, and compliance layers.

An AI-moderated call works like this: the expert receives a scheduling link, selects a time, and joins a voice conversation with an AI agent that conducts the interview. The agent asks structured questions, follows up on responses, manages the time, and produces a transcript, summary, and compliance record automatically at the end. No broker coordination. No scheduling back-and-forth. No manual transcription. No post-call write-up.

The use cases that benefit most from this model are structured and repeatable: expert vetting calls, channel checks, recurring market surveys, reference check programs, onboarding interviews for new network members. These are the calls where the question set is known in advance, consistent application matters, and the output needs to be comparable across respondents — which is exactly what AI-moderated calls deliver.

For boutique expert networks specifically, there are two distinct business applications for AI-moderated calls:

Offering AI-moderated calls as a product to clients. Large research teams increasingly expect the option to conduct expert calls at a pace and cost that human moderation cannot support. Offering AI-moderated calls gives a boutique network a capability that, until recently, only the largest players had built for themselves.

Using AI-moderated calls internally to reduce vetting opex. Expert vetting is expensive when done at scale through human calls. Running AI-moderated vetting calls — where the AI agent conducts the initial screening, assesses domain depth, and updates the expert's profile with verified information — cuts the per-vetting cost substantially and creates a consistent, auditable trail for every expert in the network.

What AI-Moderated Calls Mean for Expert Network Operators

The gap between what AlphaSense built and what boutique expert networks operate today is real — but it is not permanent. The technology infrastructure that powers AI-moderated calls does not require building it from scratch in-house.

InsightAgent is the platform purpose-built for expert networks that want to offer AI-moderated calls to their clients, or use AI-moderated calls to cut internal vetting opex, without the engineering investment of building it themselves. The same capability that AlphaSense built in-house is available to boutique ENs from $499/mo.

The economics are straightforward: an AI-moderated vetting call costs a fraction of a human-coordinated call. A network running hundreds of vetting calls per month can reduce that opex substantially while producing more consistent, more searchable output. And the client-facing version opens a new product line that justifies premium positioning against networks that still rely exclusively on human coordination.

If you run an expert network and want to see what this looks like in practice, the demo is an AI-moderated call, not a slide deck:

Talk to the agent — live

For deeper dives on specific networks and the operator playbook, see GLG alternatives & competitors, AlphaSense alternatives & competitors, Tegus vs GLG, Dialectica vs GLG, AlphaSense vs Tegus, what expert networks cost, and white-label expert calls.