Medallion

Liquid 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.

Liquid AI

Liquid AI builds foundation models, the big general-purpose brains that power chatbots and other AI tools. The difference is efficiency.

Their models, called Liquid Foundation Models, are designed to do more with far less memory and computing power. Companies pay to use them, either through a cloud API or by running the models on their own machines.

That second option matters. A bank or a hospital often cannot send private data to someone else's server.

Liquid AI lets them keep the model in-house. The pitch to a customer is basically this.

You get strong AI without renting a mountain of expensive chips.

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.

Liquid AI

Liquid AI is a spinout from MIT's Computer Science and Artificial Intelligence Laboratory, better known as CSAIL. The core science came from Ramin Hasani, who spent his PhD studying the nervous system of a tiny worm called C.

elegans. That worm has just 302 neurons and still manages to move, hunt, and react to its world.

Hasani and his collaborators asked a simple question. What if you built an AI that worked more like those neurons and less like a giant static network?

The answer was the liquid neural network, a model whose internal wiring keeps adjusting even after training is done. The team incorporated the company in 2023.

Daniela Rus, who runs CSAIL and is one of the most respected robotics researchers alive, came on as a co-founder. So did Mathias Lechner and Alexander Amini, both longtime research partners of Hasani.

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.

Liquid AI

The growth angle is size, or the lack of it. Most frontier models are so heavy they only run in data centers.

Liquid AI aims for models light enough to run on a phone, a car, or a factory robot. That opens doors the giants cannot easily reach.

The AMD partnership pushes the same idea. AMD makes chips, and it wants efficient models that show off its hardware.

So AMD led the Series A and became both an investor and a distribution partner. Liquid AI also releases open models to researchers, which builds a following before the sales team ever makes a call.

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.

Liquid AI

The honest problem is Goliath. OpenAI, Google, Anthropic, and Meta have raised tens of billions of dollars between them.

Liquid AI has raised under $300 million. The transformer architecture those giants use is battle-tested and improving fast.

Liquid AI has to prove its different approach not only works but keeps working as models get bigger. Betting against the most successful design in AI history is bold.

It is also the kind of bet that ends in either a great acquisition or a quiet shutdown, with not much room in between.

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.

Liquid AI

The main products are the Liquid Foundation Models, a family that ships in several sizes so a customer can pick the smallest one that does the job. The second generation, LFM2, arrived in 2025, including LFM2-VL vision-language models built for phones, laptops, wearables and embedded chips.

The company has leaned hard into on-device AI rather than the cloud. The focus is always the same.

Match the quality of a much larger model while using a fraction of the memory and power.

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.

Liquid AI

It started with a $37.5 million two-stage seed in December 2023 at a $303 million valuation, from OSS Capital, PagsGroup, Automattic (the company behind WordPress), Samsung Next and others. Angels included GitHub co-founder Tom Preston-Werner, Shopify's Tobi Lütke and Red Hat co-founder Bob Young.

Then AMD Ventures led the $250 million Series A in December 2024, which is unusual because AMD is a chipmaker, not a typical venture fund. That backing gave Liquid AI both money and a hardware ally.

When a major chip company writes the lead check on an efficiency-focused model maker, it signals a bet that the future of AI is not just bigger, it is leaner.