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, Tegus, AlphaSense, 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: Transcript Library and Interview Access

Tegus is structurally different from GLG. It operates as a combination of transcript library and on-demand expert access, built around the thesis that a large, searchable 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.

The vetting model at Tegus is mixed: the transcript library includes experts who were vetted by whoever commissioned the original call, and the quality of those transcripts reflects the quality of the original interview — which varies. 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.

Tegus has evolved its 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 that differentiates Tegus from pure-broker networks.

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

AlphaSense: The AI-Moderated Proof Point

AlphaSense is the most instructive case study in this comparison — not because it competes with broker networks on their own terms, but because it has moved further than any other named player in building AI-moderated expert calls as a native capability.

AlphaSense started as a document search platform for financial research. Over several years, it acquired its way into expert networks — acquiring Stream in 2021 to seed its expert transcript library, then acquiring Tegus outright in 2024 for $930 million — then built AI-moderated call capabilities in-house. The platform now runs AI-led expert interviews at scale — structured conversations where the AI agent conducts the interview, generates the transcript, and produces summaries that are indexed and searchable alongside its document corpus.

The significance for the broader expert network industry is that AlphaSense did not add AI as a feature on top of a broker workflow. It built AI-moderated calls as a replacement for the moderation layer — systematizing what brokers and analysts previously did manually. The output is integrated into a searchable research environment, which is a meaningfully different end state than a broker call that produces a set of notes.

AlphaSense is the clearest proof-point that AI-moderated calls at scale works. A platform of that size, with that research client base, built it because the economics and output quality justified the investment. The question for the rest of the expert network industry is not whether the model is valid. It is who builds the equivalent capability next, and at what cost.

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 AlphaSense's 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 AlphaSense document corpus is thinner.

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 where the larger networks have thinner supply.

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 more lead time than database-first networks. 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 as an alternative for clients who find the large networks too process-heavy for their research style and who value close coordination with a dedicated recruiter team on each project.

Where Dialectica is strongest: niche specialist research, private market diligence, and geographies where specialist coverage from a dedicated recruiter team outperforms large-network database searches.

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

How Do GLG, Tegus, and AlphaSense Differ?

The three largest players have meaningfully distinct models. GLG is a broker-managed network at global scale — its value is coverage breadth and relationship access. Tegus is a hybrid that leads with a transcript library and adds on-demand expert access as a complement — its value is compounding research context. AlphaSense is a data platform that has integrated expert calls as a native AI-moderated capability — its value is integration with a broader structured search environment.

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 run recurring research on a defined company universe and value prior context lean toward Tegus. Teams that want expert content discoverable inside a document search environment lean toward AlphaSense.

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

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Frequently Asked Questions

How do expert networks work?

Expert networks maintain databases of professionals — former executives, subject-matter specialists, industry practitioners — who agree to speak with clients for a fee. When a client submits a research request, the network matches them with relevant experts, coordinates scheduling, and manages compliance screening. Newer models replace broker coordination with automated systems that handle scheduling, call moderation, transcription, and compliance documentation without human intermediaries.

What is an AI-moderated call?

An AI-moderated call is an expert interview conducted by an AI voice agent rather than a human moderator. The expert joins via phone or web, the AI agent asks a structured set of questions, follows up on responses, manages the conversation, and produces a transcript and summary automatically when the call ends. The model is best suited for structured, repeatable call types — vetting, channel checks, surveys, reference calls — where consistent question coverage and comparable output across respondents matter more than open-ended conversation.

How do GLG, Tegus, and AlphaSense differ?

GLG is a broker-managed network at global scale, strongest for relationship-dependent expert access across broad sectors. Tegus leads with a searchable library of prior interview transcripts and adds on-demand expert access, strongest for recurring research on a defined company universe. AlphaSense has built AI-moderated expert calls as a native capability integrated into a document search platform, strongest for teams that want expert content discoverable alongside structured data. All three serve institutional research clients, but with meaningfully different operating models and output formats.

Which expert network is best for boutique operators?

Boutique expert networks typically benefit most from platforms that reduce the per-call coordination overhead without requiring large upfront engineering investment. AI-moderated platforms allow boutique networks to offer structured expert calls at scale — both to their research clients and internally for vetting — at a cost per call that human-coordinated models cannot match. The question for boutique operators is not which large network to emulate, but which infrastructure lets them offer the same AI-moderated call capability that the large players have built in-house.

What compliance features should expert networks provide?

Expert networks should screen for conflicts of interest between an expert's current and recent employment and the client's research subject. Beyond that baseline, robust compliance infrastructure flags language that could indicate material non-public information during live calls, maintains complete audio and transcript records for every conversation, and produces standardized documentation for compliance team review. AI-moderated platforms embed these screens into every call automatically — which produces more consistent compliance outcomes than broker discretion applied under time pressure.

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For more on the category: see Expert Network Software Compared (2026) and What Are AI-Moderated Calls?. If you operate an expert network, explore the InsightAgent platform for ENs.