Engagement Overview
Client
AI-enabled sports education company
Focus
Golf instruction, digital learning, movement analysis, and ongoing application support
Technology Environment
AWS, Google Cloud, iOS and Swift, Java and Spring Boot, Python and FastAPI, REST APIs, PostgreSQL, Redis/Valkey, RabbitMQ, Docker, GPU-enabled AI model serving, monitoring, deployment automation, and AI governance controls
TGP’s principal supported an AI-enabled sports education company in building and maintaining a digital platform for golf instruction, movement awareness, and coach-led learning.
The platform connects golfers and coaches through mobile applications, video-based training, structured feedback, and ongoing learning workflows. Users can capture practice activity, review instructional content, receive coaching guidance, and maintain continuity across multiple training sessions.
The engagement included developing and refining application features, integrating services and APIs, supporting video and data workflows, and maintaining cloud-hosted processing components. Ongoing work also included troubleshooting, defect resolution, configuration updates, performance improvements, deployment support, and technical documentation.
AI Model Integration
AI models were incorporated into video-processing and movement-analysis workflows to support golf instruction and performance education. The technical work included model integration, model-serving APIs, GPU-enabled processing, service configuration, validation, monitoring, and coordination between AI outputs and user-facing application features.
The platform’s AI capabilities were integrated with mobile applications, backend services, cloud storage, and coaching workflows. This allowed AI-assisted analysis to function as part of a broader educational experience rather than as a standalone technical component.
Cloud and Application Environment
The platform used technologies across AWS and Google Cloud to support storage, application services, containerized workloads, build and deployment workflows, and GPU-enabled AI model processing.
Application components included native iOS development with Swift, backend services developed with Java and Spring Boot, and Python-based AI and video-processing services exposed through APIs. Supporting technologies included PostgreSQL, Redis/Valkey, RabbitMQ, Docker, monitoring tools, and deployment automation.
AI Governance
AI governance considerations were incorporated into the delivery and maintenance approach. These included responsible data handling, access controls, model and system validation, operational monitoring, documented limitations, and human review where appropriate.
AI-generated information was positioned to support instruction, movement awareness, and coaching decisions. The platform was not designed to replace professional judgment or provide medical diagnosis. This approach helped maintain appropriate oversight while allowing AI capabilities to support practical sports education workflows.
Outcome
The engagement helped establish a more reliable and supportable technical foundation for continued platform development. Coordinated maintenance across mobile, backend, cloud, and AI model components made it easier to improve existing functionality and introduce new learning and coaching capabilities.
The resulting platform supports digital instruction, golf education, coach-participant collaboration, sports performance learning, and wellness-oriented movement awareness. Its underlying capabilities can also support broader training, recreation, and educational applications.
This engagement reflects TGP’s experience in education technology, sports technology, mobile and cloud application development, AWS and Google Cloud environments, AI model integration, AI governance, system integration, deployment support, and ongoing software maintenance.
