G LNK Is Building the LinkedIn for Pharma Experts (Minus the Cringe)

The Macro: Expert Networks Are a $2 Billion Middleman Business

The expert network industry is worth roughly $2 billion and growing. The business model is straightforward: a pharmaceutical company needs to talk to a cardiologist who specializes in a specific drug mechanism, and an expert network finds that person, schedules the call, handles compliance, and takes a cut. GLG is the giant in the room, reportedly doing over $600 million in revenue. Guidepoint and AlphaSights are the other two names that come up in every conversation about this space.

The problem with expert networks is that they’re essentially human-powered search engines. An associate at GLG gets a request, searches an internal database, makes phone calls, sends emails, and eventually connects the client with someone who might be relevant. The process takes days. The matching is imprecise. The cost per consultation runs anywhere from $500 to $1,500 per hour, and a meaningful chunk of that is paying for the matchmaking layer, not the expert’s time.

Pharma is particularly ripe for this kind of disruption because the compliance requirements are intense. You can’t just cold-email a doctor and ask them to consult on your drug trial. There are sunshine laws, conflict-of-interest disclosures, fair market value calculations for compensation. The regulatory overhead is real, and it’s one reason the incumbents have been able to charge what they charge. Compliance is hard to do and easy to use as a moat.

The Micro: UChicago Chemistry Meets Neural Networks

G LNK is an AI-powered matching platform that connects pharma companies with relevant medical professionals for drug development and commercialization work. The core product uses proprietary ranking models to identify qualified experts, with compliance baked into the workflow rather than bolted on afterward.

The founding team is a two-person operation out of New York, part of YC’s Winter 2025 batch. CEO Rayan Ghandour studied Chemistry and Economics at UChicago and did pharma consulting at PwC. That background matters. He’s seen the expert network procurement process from the client side and presumably knows exactly where it breaks down. CTO Raouf Abujaber spent three years doing neural network research at UChicago’s AxLab and has degrees in CS, Economics, and Astrophysics. An astrophysicist building healthcare matching algorithms. I’ve heard worse origin stories.

The compliance angle is probably the most important part of the pitch. AI matching is interesting, but lots of companies can build recommendation engines. What’s harder is building a system that pharma compliance officers actually trust. If G LNK can make the compliance piece seamless, the matching becomes a feature and the compliance becomes the product.

The Verdict

I think G LNK is positioned well, but the path is narrow. The expert network incumbents are large, entrenched, and relationship-driven. GLG’s moat isn’t their technology. It’s the fact that a VP at Pfizer has been using them for eight years and doesn’t want to explain to legal why they’re switching to a startup. Enterprise sales cycles in pharma are measured in quarters, not weeks.

The upside is significant if they can land it. Expert networks are high-margin businesses with sticky revenue. If G LNK can match experts faster, cheaper, and with better compliance documentation, the value proposition is obvious to anyone who’s ever waited four days for GLG to come back with three names, two of whom aren’t quite right.

In the next 30 days, I’d want to see how many pharma companies are in the pipeline and how far along those conversations are. At 60 days, the question is whether compliance teams at real pharmaceutical companies have signed off on the platform. That’s the gate. At 90 days, I’d look at repeat usage. Did the first clients come back for a second engagement? Expert networks live and die on repeat business. If clients use G LNK once and then go back to Guidepoint for the next project, the matching isn’t good enough. This is a company that could be very big or very stuck, and the difference will come down to whether pharma procurement teams are willing to try something new. My guess is some of them are ready.