Tegus vs GLG (2026): Transcript Library vs Broker Network

Teams searching "Tegus vs GLG" are usually not comparing two versions of the same product. They are comparing two different answers to the same question: how do you get an informed practitioner's view of a company, a market, or a supply chain, quickly enough to act on it?

Tegus answers that question with a library. GLG answers it with a phone call it arranges for you. Both are legitimate; they suit different research rhythms, different budgets, and different tolerance for waiting. This piece sets out what each one actually is, where each is strongest and where each shows friction, which workflow each fits, and what changes now that AI-moderated calls are part of the category.

What Tegus and GLG Actually Are

The first thing to clear up, because the query implies the confusion: Tegus is not an independent company competing with GLG. Tegus is AlphaSense's product. AlphaSense acquired Tegus in 2024 for $930m, following its 2021 acquisition of Stream, and folded the transcript library into a research environment that also indexes filings, earnings calls, broker research, and news. When you evaluate Tegus, you are evaluating AlphaSense's expert-content layer, priced and sold as part of that platform.

What Tegus contributes to that platform is a compounding archive of expert call transcripts. A researcher looking into a company opens the platform and reads interviews that already exist — conversations commissioned by other subscribers, transcribed, indexed, and searchable — rather than commissioning a new one. Live expert calls are available through the platform too, but the centre of gravity is the library. The value proposition is that the answer may already have been captured.

GLG (Gerson Lehrman Group) is the other model in its most established form: a broker-managed expert network operating at global scale. GLG maintains a proprietary network of over a million experts, recruited and credentialed in-house, and account teams match those experts to a client's specific request, handle scheduling and compliance, and in some cases moderate the call. Nothing is pre-built. Every engagement produces a new conversation, arranged for you, with someone selected for your question.

So the comparison is not library versus network in the abstract. It is stock versus flow — accumulated knowledge you can query immediately, against bespoke knowledge produced on demand.

How They Differ: Vetting, Call Execution, Pricing

Where Tegus is strongest. Speed to first insight is the standout. On a well-covered company, a researcher can go from question to a dozen relevant practitioner perspectives inside an afternoon, with no scheduling, no compliance turnaround, and no per-call negotiation. Because the content is text, it is searchable, quotable, and comparable across time — you can see how operator sentiment on a category moved across two years of interviews. Subscription pricing makes the cost predictable and decoupled from usage intensity, which suits teams whose research volume is lumpy. And because Tegus sits inside AlphaSense, expert commentary lands next to the filings and earnings transcripts a researcher was already reading.

Where Tegus shows friction. The library only answers questions someone else already thought to ask. Coverage is deepest where subscriber demand has been heaviest — large-cap and widely-followed names — and thin at the edges: private companies, niche geographies, emerging categories, unusual supply-chain nodes. Fit is uneven for a related reason: each interview was scoped to somebody else's brief, so you inherit their question set rather than writing your own, and the right expert on the right company may have been asked about something adjacent to your problem. Transcripts also age. A 2024 interview about a channel that has since restructured is a historical document, not current intelligence, and the archive does not tell you which is which. When you do need a genuinely new call, you are back in the same scheduling and matching workflow as everyone else.

Where GLG is strongest. Bespoke reach. If your question has never been asked, GLG's model is built to find someone who can answer it — including in domains where no library has depth. Account teams accumulate tacit matching knowledge that is genuinely hard to replicate: which experts communicate clearly, which have operational rather than theoretical depth, which are actually responsive. For high-stakes, relationship-dependent, or diligence-grade work, that human judgment layer has real value, and the compliance wrapper around it is mature and institutionally accepted.

Where GLG shows friction. Cost and latency both scale with volume. Rates are not published; reported figures for a domestic expert cluster in the $800–$1,500 band, with international and executive-level access above it. Because sourcing, scheduling, and compliance are quoted as a single number, there is no version of the transaction where you buy only the part you actually needed. The model does not get cheaper as your call program grows — more calls means more broker coordination. Turnaround is measured in days, because a human has to source, screen, and schedule. And the output is a raw conversation: someone on your team still has to transcribe it, synthesize it, and circulate it before it becomes an organizational asset rather than one analyst's memory.

Which Fits Which Research Workflow

The honest answer is that the choice follows the shape of your research programme, not a ranking.

Recurring research on a defined universe favours the library. If you cover the same forty companies quarter after quarter, every transcript you read compounds — and much of what you need has probably already been captured by someone covering the same names. Public-markets investors, corporate strategy teams tracking a stable competitive set, and anyone doing continuous monitoring rather than episodic deep dives get the most out of Tegus, because the archive is working in their favour on most questions they ask.

Bespoke or novel coverage favours brokered live access. Private-company diligence, a new geography, an unfamiliar supply chain, a question about something that happened last month — none of these have a library answer, because nobody has asked yet. Here GLG's ability to go find the specific person is the whole product, and the per-call premium buys reach that no archive can substitute for.

Most serious research functions end up using both, and the practical question is proportion rather than selection: what share of your questions are library-answerable, and what share genuinely require a new conversation? That ratio, measured honestly against a quarter of actual research requests, is a better procurement input than any feature comparison.

What AI-Moderated Calls Change

Here is the point both models share: every new expert conversation still depends on a human moderation layer. GLG's calls are moderated by the client or a broker. Tegus's library is a stock of conversations that were, at some point, moderated by a person too — the archive is the output of that layer, not a replacement for it. The library scales the distribution of past interviews; it does not scale the production of new ones.

AI-moderated calls address exactly that production step. The moderator is an agent: the expert picks a slot and joins by phone or web, the agent puts the planned questions and probes the answers, and what lands at the end is already a transcript, a structured summary, and a compliance record rather than an hour of audio waiting to be written up. Two line items disappear from the cost of a call — the coordination and the moderator's hour — and because the same question discipline reaches every respondent, a programme's calls come back comparable instead of each being shaped by whoever happened to run it.

That does two things to the Tegus-vs-GLG comparison. It narrows the latency gap — a structured call can be run and returned in the time it takes to read through library results, rather than waiting on a scheduling chain. And it makes the transcript-library advantage reproducible: a team running AI-moderated calls at volume is building its own searchable archive on its own coverage universe, including the private and niche names no shared library covers.

None of that displaces the networks, because stock is only ever congealed flow. Every transcript in every archive began as a call somebody arranged, with a practitioner somebody had found, screened, and vouched for. Automating the moderation step leaves that entire upstream untouched — the sourcing, the relationships, the compliance answerability — and that upstream is the part of the business that was always genuinely hard to build.

If You Run an Expert Network

If you operate an expert network, the comparison above is the expectation your clients are forming without you in the room. A research team that reads Tegus transcripts in the morning and books a broker call in the afternoon has internalised a standard: knowledge should be searchable, structured, and fast. Whether they articulate it or not, they now bring that standard to every network they work with.

Your advantage over a shared library is the thing a library cannot manufacture: proprietary access to experts nobody else has interviewed. What AI-moderated calls give you is a way to turn that access into structured, searchable output at a cost per call the brokered model cannot reach — so the reach premium your network already earns stops being consumed by coordination overhead.

The practical entry point for most networks is internal vetting. AI-moderated screening calls replace the scheduler-plus-human-screener workflow at a fraction of the per-expert cost, and the vetting function scales without adding headcount whether you onboard 100 or 1,000 experts a month. The client side is the same capability aimed outward, and it is where flow starts converting into stock on your own terms: channel checks, market surveys, and reference calls are repeat formats, so running them through the agent leaves you holding a comparable, searchable set on a universe no shared archive reaches. Broker-led calls stay broker-led wherever that is what the client is actually paying for.

None of this requires building infrastructure in-house. The agent is configured to your client use cases, wired into the expert scheduling workflow you already operate, and the calls go out under your own brand. Pricing starts at $499/mo, which puts this in the same budget category as the tools you already run rather than the ones you build. The InsightAgent for Expert Networks overview covers the full capability set and how it slots into existing network operations.

If you want to judge the quality of an AI-moderated call, the fastest way is to be on one — the demo is a call with the agent, not a slide deck:

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