HOLMZ ECOSYSTEM

In developing the AI we began with the characteristics of the experts we were emulating. Experts are knowledgeable, experienced, adaptable, and can accurately judge situations and data, infer information and share rationale. The Holmz system has several components that extend these skills.

Highlighted Features

Extensive Data Integration

Holmz starts with a data pipeline which interfaces with multiple device APIs to aggregate data from a variety of sources. The system integrates data from multiple data sources such as wearables, IoT devices, surveys, caregivers, coaches, trainers, athletes, radiology, population health and user accounts.

Running Woman With Mobile Device Insights

Cognitive Expert System

Holmz uses self-training to develop new methodologies and protocols. Data is fed into the system through multiple levels of processing to produce a human model. The human model is used for decision making, insight, rationale, and product design and provides a familiar basis for explaining rationale to caregivers.

Dashboard Visualization

Our language layer translates the the human model data interpretation into reports. The Holmz user interface is very intuitive and uses a library of swappable data widgets to personalize a dashboard on-the-fly.

Actionable Insights

Holmz functions as any expert conducting research, testing theories, improving its capabilities and capacity, and providing the rationale behind its assessments. The system can predict lower extremity musculoskeletal injuries, identify repetitive stress injury through a series of unique factors, and can even be used to generate personalized products such as footbeds or grips.

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