AthletAI

Technology

The AthletAI intelligence architecture.

AthletAI connects wearable data collection, cloud-based athlete records, and AI-assisted analytics to support performance, development, and scouting workflows.

Architecture

From field data to decision support.

Wearable Devices Movement and location inputs from sporting environments
Mobile App Device assignment and session workflow
Cloud Data Platform Athletes, activities, assignments, and processed metrics
AI Analytics Pattern detection, trends, benchmarks, and load indicators
Insights Performance, development, and scouting decisions

Data model

Athlete-centric analytics with traceable raw data.

AthletAI's raw ingestion layer starts with devices and device sessions, while the product value is built around athlete development over time. Assignments connect a device, athlete, time window, and sporting activity context.

This structure supports real operational use: one device can be used by different athletes, one athlete can use different devices, and raw files can still be traced back to athlete records and activities.

Core entities

  • athlete
  • device
  • device session
  • activity
  • assignment
  • processed metrics
  • benchmarks

AI Layer

Machine learning and statistical models for structured insight generation.

The intelligence layer is designed to use machine learning and statistical models to analyze large volumes of athlete data over time. The system supports decision-making and does not provide medical diagnosis or guarantee injury prediction.

Long-term trends

Detect performance changes across repeated sessions and comparable activity contexts.

Development comparison

Compare athlete development over time using structured histories and relevant benchmarks.

Workload monitoring

Identify abnormal workload patterns and fatigue indicators for further human review.

Athlete profiles

Create objective profiles that combine movement, workload, and context-specific performance signals.

Scouting support

Improve scouting decisions with repeatable data rather than replacing expert judgment.

Load indicators

Detect physical load patterns that may require attention from coaches and performance staff, without clinical claims.