Medallion

Magic AI

Medallion

Medallion charges healthcare organizations a subscription based on the number of providers being managed. The platform handles initial credentialing (verifying a new provider) and ongoing monitoring (ensuring licenses stay current, no disciplinary actions arise).

The value is measured in speed and cost savings. Credentialing departments at hospitals can cost millions annually in staff salaries.

Medallion replaces much of that manual labor with software. Faster credentialing also means faster time-to-revenue — a doctor who can start seeing patients weeks earlier generates revenue weeks earlier.

The platform also manages payer enrollment — getting providers into insurance networks — which is a related but separate bureaucratic process that's equally painful and equally automatable.

Magic AI

Magic is building an AI that works like a software engineer, not a chatbot. The goal is a model you can hand a whole code base and a task, and it writes, reviews, and debugs like a teammate.

Their edge is memory. In 2024 they showed off LTM-2-mini, a model with a 100 million token context window.

To put that in perspective, that is about 10 million lines of code the model can look at in one shot, by far the biggest of any model at the time. The plan is to sell this as an AI coworker to companies that write a lot of software.

As of the 2024 rounds, the product was still not for sale.

Medallion

Derek Lo was working in healthcare operations when he encountered credentialing — the process of verifying that healthcare providers have the proper licenses, education, training, and malpractice history to practice medicine. Every doctor, nurse, and therapist in America must be credentialed before they can see patients at a new facility or join an insurance network.

The process was absurdly manual. It involved contacting medical schools, state licensing boards, previous employers, malpractice insurers, and the DEA — often by fax or mail.

A single credentialing verification could take 90-180 days. Healthcare organizations employed entire departments of people doing nothing but chasing down verifications through a patchwork of government databases, phone calls, and paper forms.

Lo founded Medallion in 2020 to automate this process. The platform connects directly to primary sources — state licensing boards, the National Practitioner Data Bank, DEA databases, and medical school registries — and pulls verification data automatically.

What used to take months of manual work now takes days or weeks.

Magic AI

Eric Steinberger and Sebastian De Ro started Magic in 2022. Steinberger is an AI researcher who became convinced that the missing piece for a true AI software engineer was memory.

Chatbots forget what you told them a few thousand words ago. He wanted a model that could read your entire code base and remember all of it.

They raised a small seed in 2022, then a $23 million Series A in early 2023 led by Alphabet's growth fund CapitalG. From day one the pitch was not a chatbot.

It was an AI colleague that could handle real engineering work.

Medallion

Medallion grew by targeting healthcare organizations that were drowning in credentialing backlogs. Telehealth companies scaling rapidly during COVID were the first ideal customers — they needed to credential hundreds of providers quickly across multiple states, and the manual process couldn't keep up.

The platform expanded to hospitals, health systems, and staffing agencies that manage large provider networks. Each customer type faces the same fundamental problem: too many providers to credential, too few staff to do it, and too many regulatory requirements to track manually.

Integration partnerships with HR systems and practice management platforms drove distribution. When Medallion plugs into a health system's existing tech stack, the switching costs climb and the data integration deepens.

Magic AI

Magic's strategy is research first, product later, which is both unusual and expensive. Instead of shipping an app and iterating in public, they raised huge sums to chase a technical breakthrough in model memory.

The headline move was the 100 million token context claim, which got the whole AI world talking. They also cut a deal with Google Cloud in 2024 to build two supercomputers, called Magic-G4 and Magic-G5, using NVIDIA chips, to train bigger models.

Star backers like Eric Schmidt served as validation. The bet is that if the tech is far enough ahead, the product and the customers follow.

Medallion

Healthcare is notoriously slow to adopt new technology. Decision-makers at hospitals and health systems are risk-averse and procurement cycles are long.

Selling enterprise software to healthcare organizations requires patience, compliance certifications, and relationships that take years to build.

The credentialing ecosystem involves hundreds of independent data sources — each state licensing board has its own system, formats, and response times. Building and maintaining connections to all of these sources is operationally complex and requires constant updating as boards change their processes.

Competition from established credentialing verification organizations (CVOs) and legacy software providers means Medallion is displacing existing processes and vendors, not entering a greenfield market.

Magic AI

Magic has raised close to half a billion dollars and, years in, still has no product the public can buy. That is the whole problem in one sentence.

A giant context window is a great demo, but rivals like Cursor, GitHub Copilot, and Cognition's Devin already have real users and revenue. Magic has to prove that more memory actually makes a smarter engineer, not just a bigger number on a slide.

And it has to do that while burning enormous sums on compute against competitors who are already shipping. The clock and the cash both keep running.

Medallion

Medallion Credentialing — automated primary source verification of healthcare provider qualifications including licenses, education, training, and malpractice history. Medallion Monitoring — continuous license and exclusion monitoring that alerts organizations when a provider's credentials change or expire.

Medallion Payer Enrollment — automated management of insurance network applications and re-enrollments across multiple payers. Medallion Provider Network Management — a unified platform for managing the complete provider lifecycle from recruitment through credentialing to ongoing compliance.

Medallion Analytics — dashboards showing credentialing pipeline status, bottleneck identification, and compliance metrics.

Magic AI

The centerpiece is Magic's LTM, or Long-Term Memory, model family. In 2024 they unveiled LTM-2-mini, a model with a 100 million token context window, pitched as the key to an AI that can understand an entire code base at once.

The broader product is an AI software engineer, or coworker, that can write and change code across a whole project. The company also announced Magic-G4 and Magic-G5, two supercomputers built with Google Cloud and NVIDIA to train these models.

Much of it is still research rather than a shipping product.

Medallion

Sequoia Capital led the Series B. Optum Ventures (part of UnitedHealth Group) invested — a significant strategic validation from the largest healthcare company in America.

Tiger Global and GV (Google Ventures) participated in earlier rounds.

Magic AI

Magic's backers are a murderers' row of AI insiders. It started with a $5 million seed round in 2022 and a $23 million Series A in early 2023 led by Alphabet's CapitalG.

Then in February 2024 came a $117 million round led by Nat Friedman and Daniel Gross, two of the most connected investors in AI. The blowout was August 2024.

Magic raised $320 million with checks from former Google CEO Eric Schmidt, CapitalG again, Atlassian, Jane Street, Sequoia, and Elad Gil. That pushed total funding to around $465 million, all for a company still building toward its first sellable product.