Salt Lake City-based MTN has secured a $2.34 million NIH Fast Track grant to advance technology that maps relationships across fragmented data systems. The company sees applications in healthcare and other data-intensive industries
Salt Lake City, Utah — October 13, 2026
Artificial intelligence may be able to analyze enormous amounts of information, but organizations still face a fundamental obstacle: their data remains trapped across systems that don’t speak the same language.
In healthcare, patient information is scattered across electronic health records, monitoring devices, clinical notes, and legacy databases. Even when useful data exists, connecting it to support AI analytics or real-time clinical workflows requires heavy integration—and raises significant privacy concerns around handling sensitive records.
Salt Lake City-based Medical Timeseries Networks (MTN), which goes by “Mountain,” is developing a platform designed to solve that problem by mapping what data means, where it lives, and how disparate systems relate to one another.
Today, the company announced it has secured a $2.34 million National Institutes of Health (NIH) SBIR Fast Track award for a project titled Unlocking Medical AI: A Scalable, Privacy-Preserving Annotation Platform for Clinical and Physiological Data.
The Nucleus Institute, a key regional deep-tech ecosystem partner, was instrumental in helping MTN land the SBIR Fast Track award.
MTN’s approach prepares fragmented data for AI applications without requiring the platform to inspect or move underlying sensitive health records during the mapping process—significantly shortening security and compliance hurdles.
For MTN co-founder and CEO Dr. Warren Woodrich Pettine, the opportunity extends beyond any single clinical application. In a recent conversation with TechBuzz, Pettine explained that while the company initially set out to solve remote patient monitoring and workflow coordination, the broader challenge of data fragmentation quickly emerged as the core bottleneck.
“We realized the data problem was the problem,” Pettine said, summarizing the philosophy behind MTN’s strategy. Before AI can reliably act on information, organizations must first understand what that information represents, where it overlaps, and how it connects across systems.
Mapping the data before putting AI to work
MTN’s platform is built around two components: MTN Guide and MTN FieldMap.
MTN Guide is an agentic data-engineering tool designed to examine the structure and documentation of data systems. Rather than relying on direct access to sensitive records, MTN Guide works with information such as database schemas, technical documentation and application programming interface (API) specifications to determine what fields mean and how systems are organized.
That interpretation is not necessarily a one-time exercise. Data systems change as organizations add fields, revise workflows or adopt new software. MTN’s approach is intended to keep those interpretations current as the underlying systems evolve.
The resulting knowledge is represented in MTN FieldMap, a semantic map of relationships among data elements and systems. The map is designed to document not only the relationships MTN identifies, but also the evidence and confidence associated with those mappings.
The distinction matters because data integration is not simply a matter of matching similarly named fields. Two systems may use different terms for the same concept, or use the same term to represent different things. A useful map must capture those distinctions rather than assume that the data is interchangeable.
MTN also emphasizes the temporal dimension of information. In many applications, understanding what happened, and in what order, is as important as knowing the current value of a field. A system that can account for the timing and relationships of events may give downstream AI applications a more useful view of how a situation has developed and what has changed.
The company’s intended result is a foundation that other software and AI agents can use to query, connect and act on information across systems.
The approach does not eliminate the need for secure data access when an application ultimately requires patient-level or other sensitive information. Rather, MTN’s stated distinction is that building and maintaining the map can rely on system structure and documentation instead of requiring access to the underlying records for that purpose.
A Healthcare Problem with Operational Consequences
Healthcare remains MTN’s primary focus, where its technology grew out of work in remote patient monitoring and the real-world friction of coordinating care across fragmented systems.
In post-acute care settings, for instance, patient data often needs to move between skilled nursing facilities and hospitals. Because facilities frequently rely on different electronic health record (EHR) platforms, critical clinical context can be lost during transfers.
MTN’s data-mapping technology aims to make information more accessible across those disparate systems. Its work with Cyrus Health, a developer of post-acute AI products, offers a practical illustration.
“MTN’s technology helps us scale our AI products across post-acute facilities and supports the transfer of critical information to hospitals,” said Cyrus Health CEO Chance Basinger.
The partnership underscores a fundamental reality for healthcare AI: a model or application is only as useful as the information it can access and interpret. Unifying systems requires heavy underlying data work that operates behind the scenes, yet remains essential for scaling software across care networks.
Beyond clinical workflows, Dr. Pettine pointed to operational challenges faced by consolidated dental groups. As dental networks acquire independent practices, parent entities inherit a patchwork of legacy software, complicating cross-location financial comparisons. MTN’s unified data mapping allows dental groups to evaluate metrics like payer concentration, revenue, and operating expenses without forcing every clinic onto identical software.
While these use cases demonstrate MTN’s target applications, the company has not yet provided quantified metrics on time or cost savings—meaning potential operational benefits should be distinguished from verified outcomes.
From Clinical Workflows to Broader AI Infrastructure
MTN’s long-term vision extends beyond healthcare. Dr. Pettine noted the company is actively in discussions with enterprise partners in financial services, investment management, and networking equipment—sectors facing similar friction with fragmented data and strict privacy constraints.
This move reflects an evolution in how MTN defines its business. Rather than positioning solely as a clinical workflow tool, the company is framing its platform as a core data-infrastructure layer that supports third-party software built on top of disparate systems.
However, expanding into enterprise data does not signal a departure from healthcare. Instead, MTN is applying tools hardened in healthcare’s high-compliance environment to broader corporate markets facing integration bottlenecks.
Looking ahead, Dr. Pettine sees an opportunity to democratize sophisticated data integration for midmarket organizations that lack the resources for massive software overhauls. He drew an analogy to payment processing, comparing MTN’s goal to how Square simplified merchant services for smaller businesses.
Dr. Pettine explicitly identified Palantir as a competitor, positioning MTN as a lighter, more cost-effective alternative for organizations seeking cross-system interoperability. These represent the company’s competitive claims, as direct, independent comparisons of cost, speed, and technical capabilities have not yet been established.
NIH award supports the next stage of development
The $2.34 million NIH award provides funding for MTN’s next stage of research and development. Pettine described it as a fast-track Small Business Innovation Research (SBIR) award spanning Phase I and Phase II, an unusual combination that he said reflected the reviewers’ assessment of the technical approach and team.
Dr. Pettine told TechBuzz the award arrived in July 2026 after a longer-than-expected process. Because MTN had completed substantial work related to its original proposal during the delay, the company worked with its program officer to revise the project aims.
Dr. Pettine said the funding provides roughly two and a half years of research and development runway. The precise scope of the NIH-funded work should be understood in terms of the approved project aims; the award should not be read as funding for every commercial application MTN may pursue in other industries.
For MTN, the funding comes as it prepares to make the platform easier for customers to adopt. Dr. Pettine said the company is planning a self-service offering and expects to publish customer case studies that can provide more detail about how organizations use the technology. The company is also building its commercialization capabilities.
Those next steps will help show whether MTN can translate its data-mapping approach into a repeatable product with demonstrable value for customers.
The underlying problem is widely recognizable: organizations want to apply AI to information spread across systems, but connecting that information can be expensive, difficult to maintain and sensitive from a privacy and security standpoint. MTN is betting that mapping the meaning and relationships of data—rather than beginning with the records themselves—can make that work easier to manage.
The NIH award gives the Utah company additional resources to develop that approach. Its longer-term test will be whether the platform can reliably map complex systems, keep those maps current and help customers move from disconnected data toward useful AI applications.
Learn more at www.themtn.ai. See previous TechBuzz coverage of MTN here.