Why we built the MTN Data Foundry
We built the Foundry to make our own AI deployments work. Then we realized everyone deploying AI or general analytics has that same problem.
The problem we kept hitting
We were building our own AI-powered solutions across healthcare sites. Deployments hit the same wall: different EHRs, different schemas, IOT devices, and stalled pipelines with each vendor update. Our ML scientists and engineers wanted to avoid debugging data transformations and focus on actionable models.
The problem wasn't unique to us. Traditional integration approaches assume stability, but healthcare data environments are anything but stable. We needed infrastructure that expected change instead of breaking from it.
We weren't the first to hit it. We won't be the last.
What we built for ourselves
So we built our own solution. Our team of ML scientists, physicians and software engineers developed an AI-powered integration layer that could detect schemas automatically, learn from previous mappings, and adapt when sources changed.
The same technology works wherever fragmented data systems need to operate as one.
Built for operators
Designed for people who need visibility now, not perfect data eventually.
Built for governance
Healthcare requires audit trails, human oversight, and deterministic behavior. The Foundry was designed to satisfy compliance requirements, not work around them.
Why now
When we hit the wall, we hit it alone. That's no longer true. Every AI deployment eventually discovers what we discovered: deployments fail on data, not on models. The Foundry is the layer that lets it work.
Technical leadership
An unusual combination: ML scientists who understand deployment constraints, physicians who understand data infrastructure, and engineers who turn it into product. Our work has been published in Nature journals, PNAS, JMIR, Chest, PLoS Computational Biology, The Royal Society, and other leading venues.
Co-Founder & CEO
A machine learning scientist and software builder who has wrestled the data engineering problem for 17 years. Trained in medicine at Colorado and computational neuroscience at Harvard, Stanford, NYU, and Yale. He co-founded MTN in 2023 to address the under-utilization of data in healthcare. Separately, he leads the Medical Machine Intelligence (M²Int) Lab at the University of Utah, an academic research group developing AI for clinical applications. Prior service in U.S. and Colorado health policy, and on the University of Utah IRB, shapes MTN's governance posture.
Enterprise AI Advisor
Head of Medical Data & AI at Sanoptis, one of the largest ophthalmology networks in Europe. PhD and postdoctoral research at Columbia University in computational ML. Previously a Deep Learning Research Engineer at DeepLife, training foundational models on genomic and biometric data. Investigator with the M²Int Lab. Aligns MTN's products with the integration and scalability needs of M&A-driven enterprises.
“We were tired of spending more time on data plumbing than on actual science. So we built a system that could handle the integration complexity for us. Turns out, that system is exactly what a lot of other organizations need.”
— Warren Pettine, Co-Founder & CEO
Our mission
MTN is dedicated to improving the financial sustainability of the healthcare system by making fragmented data systems operable. The Data Foundry replaces months of manual data engineering with software that learns, harmonizes, and adapts. Each new data source is easier to use than the last. We make organizations more nimble, responsive, and adaptive, reducing operational overhead, raising provider satisfaction, and improving patient outcomes.
We're building the connective tissue of modern healthcare.
Want to learn more?
Whether you're operating a portfolio, deploying AI, or trying to make integration work in a roll-up, we'd like to hear from you.