G LNK Turned Pharma's $70 Billion Doctor Problem Into a Search Bar
Two University of Chicago friends built a searchable database of millions of clinicians - and an AI that tells pharmaceutical teams who to call, what to pay, and how to stay compliant.
Every year, pharmaceutical companies pay doctors somewhere north of $70 billion to consult, speak, sit on advisory boards, and read early trial data. It is one of the largest, least-discussed line items in medicine, and for decades it has run on a strange mix of spreadsheets, recruiting firms, and consultants who bill by the hour to answer a deceptively simple question: which expert should we call, and what is a fair price for their time?
G LNK, a New York company in Y Combinator's Winter 2025 batch, thinks that question should take seconds, not weeks. Its founders looked at the whole apparatus - the outsourced expert searches, the manual compensation math, the compliance filings scattered across disconnected systems - and decided most of it could be replaced by a search bar sitting on top of a very large database.
01 / The problemA market that still runs on phone calls
When a drugmaker needs a key opinion leader - the oncologist who ran the pivotal trial, the cardiologist whose name carries weight at conferences - the usual path is to hire someone to find them. Consulting and recruiting firms charge handsomely for that introduction. Then comes the harder part: deciding what to pay. Overpay a physician and you can trip a compliance violation with federal weight behind it. Underpay and the expert walks. In between sits a benchmarking exercise that most teams do by hand, pulling from data that lives in a dozen places.
G LNK's founders frame this as three problems stacked on top of each other: identification is expensive, compensation is risky, and the data is fragmented. Solve all three in one place, and a process that took a services firm weeks collapses into an afternoon.
02 / The productAsk in plain English, get a ranked answer
At its core, G LNK is a searchable clinician database with an AI layer on top. A user can type a natural-language query - a specialty, a procedure history, a publication record - and get back a ranked, tiered list of doctors, drawn from a base that the company reports at more than nine million profiles and, at launch, described as growing quickly month over month. Upload a physician's CV instead, and the system parses it and slots the person into a tier.
The tiering is where the domain expertise shows. G LNK's AI assigns fair-market valuations - a defensible rate for a given clinician's time - by reading credentials against real compensation data. Around that sits a CRM for contacting and tracking engagements, and financial tracking for transfer-of-value payments, drug coverage, and monthly summaries by therapeutic area. Compliance checks draw on public sources like NIH, PubMed, and CMS, plus the rules of more than 150 countries.
Identify
Search 9.2M+ profiles by natural language, or upload a CV.
→Tier & price
AI assigns a tier and a fair-market valuation from real data.
→Engage
Contact and track the clinician inside a built-in CRM.
→Stay compliant
Real-time checks across NIH, CMS, and 150+ countries' rules.
03 / The foundersA decade of friendship, then a company
G LNK is run by two people who met in a University of Chicago research lab and kept in touch for more than ten years before starting anything together. Rayan Ghandour, the CEO, studied chemistry and economics, then went to PwC as a life-sciences consultant focused on pharmaceutical compliance - where he was recognized as a top consulting analyst in the US for automating exactly the kind of work G LNK now sells. He is, in other words, building the tool he wishes he'd had.
His co-founder and CTO, Raouf Abujaber, holds degrees in computer science, astrophysics, and economics, and came from machine-learning research at AxLab, working on neural-network architectures and adaptive systems. The split is clean: one founder knows the regulatory terrain cold, the other knows how to teach a model to navigate it.
04 / The moatThe data is the product
Anyone can build a search box. What's hard is what sits behind it. G LNK's edge is the aggregation: nine million-plus clinician profiles with specialty, procedures, and prescribing behavior; tens of thousands of institutions; billions of prescribing and procedure claims; and more than $11 billion in tracked payments feeding the fair-market benchmarks. Pulling that from NIH, PubMed, CMS, claims feeds, and other public sources into one queryable graph is the real work. The interface is just the front door.
05 / The competitionTwo people against the incumbents
G LNK is not entering an empty field. Healthcare commercial intelligence already has heavyweights - IQVIA, Veeva, Definitive Healthcare, H1, Komodo Health - all sitting on large clinician datasets. The traditional alternative is even older: the consulting and recruiting firms pharma has always paid to find experts and defend the pricing. G LNK's wager is that a natural-language interface plus fair-market benchmarking, aimed squarely at the engagement-and-compensation workflow, is a sharper tool than either the legacy data vendors or the services firms. It is a two-person team betting that focus beats breadth.
06 / The modelSoftware instead of a services invoice
The business is B2B SaaS. G LNK sells subscription access to its platform and database to the commercial, medical-affairs, and compliance teams inside pharma, biotech, and medtech companies. The pitch is straightforward arithmetic: replace the recurring spend on consultants and recruiters with a self-serve tool, cut the time from question to answer, and get a compensation number you can defend to a regulator. Y Combinator and UChicago Medicine Ventures have both backed the company, which raised a reported seed round in 2025. The UChicago tie is notable - a hospital system's venture arm deciding it wants a stake in the tool its pharma partners use.
07 / Where it fitsVertical AI, aimed at the back office
Strip away the specifics and G LNK is a case study in a broader pattern: AI pointed not at the flashy front of an industry but at its regulated, unglamorous middle. The work of matching experts, benchmarking pay, and filing compliance is exactly the kind of high-stakes, rules-heavy process that rewards a founder who has lived it. G LNK's team learned the rules first, then built software to run them. Whether it can grow the database and win pharma's trust faster than the incumbents adapt is the open question - but the choice of problem is a deliberate one.
The takeaway. G LNK is early, small, and pointed at a market most people never think about. But the shape of the bet is clear: turn a $70 billion process that runs on phone calls and spreadsheets into a search bar with compliance built in. If the data keeps growing and the benchmarks hold up, the pitch gets simpler every quarter.