Clippd – Universal Platform for Golfers

Introduction

Clippd is a sports-tech startup building a universal performance data platform that helps golfers and coaches understand, improve, and share on-course performance. After raising over $10M in venture funding, the team set out to streamline how swing, shot, and practice data flows through their system and to unlock new analytics that help players and coaches make better decisions. They needed an AI and cloud development partner to optimize core backend components, accelerate delivery, and bring new data science features to production with confidence.

Based on our expertise in AI and Cloud application development, we partnered with Clippd to redesign new data analytics modules and improve core components of their platform. We also needed to integrate machine learning features and algorithms into their system, while integrating automated testing practices.

Aegasis Labs partnered with Clippd to streamline data pipelines, implement advanced analytics, and embed automated testing and delivery practices across the product.

• Delivered BI & analytics services to power a high-performance data platform for golf metrics and insights.
  
• Built machine-learning pipelines and algorithms to process player performance and practice effectiveness.
  
• Designed and shipped two new analysis/algorithm modules to production.
  
• Introduced automated testing, CI/CD, and monitoring to speed up iterations and improve release quality.

The Story of Clippd

Clippd is a sports-technology company on a mission to give golfers and coaches a single place to understand performance. After securing more than $10M in venture funding, the team set out to unify swing, shot, and practice data from multiple devices and sources, transforming raw telemetry into insights a player can act on during practice and on the course.

To achieve this, Clippd needed a technology partner who could help them scale their data engineering and analytics capabilities. Their challenge was not just collecting and consolidating large volumes of structured and unstructured performance data, but also processing it in real time and surfacing insights that were both accurate and intuitive for players and coaches.

Using Clippd’s proprietary data models, combined with advanced machine learning and cloud-native engineering practices, Aegasis Labs partnered with them to build a reliable and scalable performance analytics platform. This enabled Clippd to accelerate feature delivery, provide high-value analytics, and position themselves as a leader in the golf technology space—empowering athletes with knowledge, saving time for coaches, and ultimately improving performance outcomes across the sport.

The Opportunity

Golfers at all levels face the challenge of measuring progress objectively. Traditional scorecards and basic statistics fail to capture the deeper nuances of performance. Coaches, meanwhile, often spend excessive time manually interpreting data instead of focusing on personalized training strategies. This lack of an integrated, data-driven view prevents players from truly understanding their strengths and weaknesses.

At the same time, the global growth of sports analytics has raised expectations. Investors and athletes alike are demanding platforms that can harness machine learning and cloud scalability to process high volumes of data, generate accurate benchmarks, and deliver insights instantly. Without this capability, emerging sports-tech brands risk being left behind in a rapidly evolving, highly competitive space.

Clippd recognized this opportunity to build a universal performance platform that would centralize golfer data, apply AI-driven analysis, and provide clear, personalized insights for both players and coaches. By doing so, they aimed to position themselves as the go-to technology solution for golf performance analytics bridging the gap between raw data and actionable improvement.

If Clippd could deliver low-latency insights with clear, explainable calculations, it could win adoption with elite programs and create repeatable value for everyday golfers, unlocking premium subscriptions, team licenses, and partner integrations.

The Solution

Aegasis Labs designed and developed a cloud-based performance data and analytics platform for Clippd. The platform unifies shot-level, sensor, and practice data, and applies machine learning to surface clear, coachable insights for golfers. We re-architected data ingestion and modeling around an event-driven pipeline, created a governed analytics warehouse, and introduced MLOps practices so new metrics and models can move from research to production quickly and safely.

Working with Clippd, we delivered a universal golf analytics stack that enabled:

• Successfully designed and delivered two production analytics/algorithm modules within Clippd’s pipelines, expanding the platform’s insight surface and enabling richer performance reporting for end users.
• Designed a normalized analytical model for golf events, shots, rounds, and player profiles to power consistent KPIs.
• Integrated BI and analytics services for players, coaches, and operations, and comparability across time, course conditions, and peer groups.
• Built batch and streaming feature pipelines for performance analytics (e.g., shot/round difficulty, consistency, benchmark comparisons)
• Trained Machine Learning Models and algorithms to measure player performance and progression, producing interpretable scores that align with coaching practices
• Introduced container services, CI/CD workflows, and automated unit/integration tests to raise reliability and speed up releases
• Progress & Recommendations Module – ML pipelines that detect trends, forecast improvement, and surface role-based coaching insights and practice priorities inside Clippd’s product.
• End-to-end ML pipelines (feature computation, training, evaluation, and model registry) with automated retraining and safe rollouts.
• Analytics & dashboards that expose trusted metrics to product and customer-facing teams, plus a secure API for partner integrations.
  
  

Technologies used:

• Python (Pandas, scikit-learn)
• SQL, dbt, Airflow, MLflow, Great Expectations;
• AWS (S3, Lambda, Glue/Athena or Redshift, Step Functions);
• Docker,
• Kubernetes, Terraform; modern BI (e.g., Looker/Tableau/QuickSight);
• GitHub Actions for CI/CD.

 

The Results

Aegasis Labs worked with Clippd to design, train, and deliver new analytics modules, Integrate BI and analytics features into their app and shipped two production-grade ML modules that now power player and coaching insights across the product.

• New insights in production: Two analytics/algorithm modules now power performance scoring and comparisons directly in the user experience.
  
• Faster, safer delivery: CI/CD and automated tests reduced manual steps and lowered regression risk, enabling more frequent, predictable releases.
  
• Operational efficiency at scale: Streamlined pipelines and observability reduced maintenance overhead and improved stability during peak usage.
  
• Data reliability & trust. Pipelines now run with >99% success, automated retries, and end-to-end data validation, giving product and coaching teams a single, governed source of truth.
  
• New intelligence in production. We delivered two ML scoring/analytics modules—built on cleaned pipelines that analyze player performance and practice effectiveness, and surface insights directly in Clippd’s UI.