In the high-stakes world of healthcare, there is a winning formula for AI — and most companies are only solving half of it. It starts with data that clinicians can actually trust, and it ends with insights that fit seamlessly into the reality of how care is delivered. Miss either half, and the technology never escapes the lab. The companies that get both right are the ones that actually change care.
At OneStep, we are building the infrastructure for the future of mobility-related care by bridging the gap between raw data and actionable intervention. We believe that for AI to be truly impactful, it must be built for the "wild" of the real world. From senior living facilities to post-acute care and everyday life, we are moving past population-level averages and occasional data points.
Our goal is a new standard of clinically proven movement intelligence made actionable by AI that actually works where care happens.
In healthcare AI, the quality of the output is only as good as the data behind it. Most models are built on generic or scraped data that is clean enough for a computer but too detached from real patients to be clinically useful.
Our foundation is built differently.
OneStep's proprietary mobility data foundation comprises millions of fully captured gait cycles collected across hundreds of thousands of patients, spanning diverse conditions, populations, and care workflows. It is that depth and diversity of data that makes it possible to go beyond recognizing that someone is walking, and understand how.
While generic AI might recognize that someone is walking, OneStep’s movement intelligence understands how they are walking. By capturing the subtleties of stride length, cadence, asymmetry, and variability, we transform raw movement into objective, clinically actionable intelligence.
This level of detail is only possible because our data is:
A strong data foundation is what makes transparency possible. As noted in a scoping review published in BMJ Open, the primary barriers to AI adoption aren't strictly technical. Clinician hesitancy, ethical concerns, and doubts about AI conclusiveness consistently emerge as barriers, with the opaque nature of many AI systems undermining the trust clinicians need to act on recommendations.
By building our AI on a foundation of validated clinical data, every score OneStep surfaces is backed by a traceable clinical rationale. When a resident's fall risk or walk score changes, we can show exactly which movement patterns drove that change, such as stride length, cadence, asymmetry, expressed in the same clinical language care teams already use to document and communicate. We aren't asking for blind trust; we are providing evidence-based insights.
There is a concept in healthcare technology sometimes called the "last mile" problem: data and insights exist, but they never reach the person who can act on them. Reports go unread. Dashboards get ignored. Alerts pile up until they become background noise.
This is one of the primary challenges of AI in care settings. The hurdle to building models sophisticated enough to detect a pattern, but also design systems that translate that pattern into something a clinician can act on, in the moment, in their existing workflow.
Consider fall prevention. Falls are the leading cause of fatal and nonfatal injuries in older adults, resulting in $80 billion in medical costs each year. The tools to treat or prevent them often exist in the form of reactive video surveillance, identifying mobility patterns, medication interactions, or prior incident history.
The gap is in what happens next. Does that score reach the right person? Is it specific enough to drive a personalized intervention? Does it arrive in time to matter?
The AI tools making real headway in this space are the ones solving for that last mile. Leading solutions, like OneStep, are making those outputs clinically meaningful, workflow-ready, and connected to the specific patient in front of the care team.
Clinicians have a natural intuition for movement. However, intuition alone is difficult to scale and impossible to quantify objectively. OneStep’s movement intelligence is trained on real-world mobility data to speak the same language as the care team.
This is the OneStep philosophy. We are not using AI to replace clinical judgment but to arm it with the precision and continuity of a motion lab. By providing a clear and evidence-based "why" behind every insight, we move past the black box. We provide the clarity clinicians need to act with confidence and the "force multiplier" effect that overstretched care teams need to stay ahead of risks.
We aren't just building a tool; we are building the infrastructure for the future of mobility-related care. We believe that foundation models for human motion will eventually be as fundamental to healthcare as electronic health records are today.
To reach that future, we are committed to:
The AI tools that will truly matter in healthcare are not those that perform best in a vacuum. They are the ones that get both halves of the formula right: data that clinicians can trust, and intelligence that fits the reality of how care is actually delivered. When you solve for both, you don't just have a useful tool. You have technology that shows up reliably in the workflow, earns confidence through accuracy, and ultimately changes the trajectory of a patient's life.
Clinically trusted movement intelligence, made actionable by AI. That is the standard we have built. That is the OneStep advantage.