Clippd – Universal Platform for Golfers

Executive Summary

Golf has more data than ever. Swing sensors, shot trackers, launch monitors, GPS devices, etc. What has been missing is a platform sophisticated enough to unify it, process it intelligently, and turn it into something a player can actually use to improve.

Clippd was built to solve that problem. After raising more than $10M in venture funding, they had the vision, the user base, and the ambition to become the universal performance platform for golfers and coaches. What they needed was a technical partner who could scale their data engineering capabilities, ship new analytics modules to production, and introduce the MLOps discipline required to move fast without breaking things.

Aegasis Labs became that partner. We redesigned core data pipeline components, built and shipped two production-grade ML modules, introduced CI/CD and automated testing across the platform, and delivered a governed analytics layer that gives Clippd’s product and coaching teams a single, trusted source of truth. Pipelines now run with greater than 99% success. New intelligence is live in the product. Releases are faster, safer, and more predictable.

In November 2023, Clippd closed a $10 million Series A led by Edge VC — bringing total venture funding to $10.82 million across four rounds since 2017. The analytics platform that Aegasis Labs helped build is the technical foundation that made that outcome possible.

About Clippd

Clippd is a sports-technology company on a mission to give golfers and coaches a single place to understand performance. The platform unifies data from the multiple devices and sources that modern golfers use — swing sensors, shot-tracking systems, launch monitors, round data — and applies analytics to transform that raw input into something a player can actually use to improve.

$10.82 Million Raised Across Four Rounds

Clippd’s funding history reflects a consistent pattern: investors backing a platform with a clear technical differentiation and a growing addressable market. From the first early-stage round in 2017 through the $10 million Series A in November 2023, each round followed demonstrated progress in both product capability and market adoption.

Aegasis Labs’s engagement sits in the context of this growth trajectory. The analytics and ML engineering work we delivered two production ML modules, redesigned data pipelines, BI integration, and CI/CD infrastructure, which contributed directly to the platform’s analytical capability and delivery reliability. A platform that can demonstrate trustworthy, explainable performance insights at scale, with the engineering infrastructure to keep improving them, is a platform that earns institutional investor confidence.

What Institutional Investors Were Backing

A golf performance analytics platform with proprietary ML scoring, trusted data pipelines, institutional adoption across collegiate golf associations, and the engineering foundation to scale reliably. That foundation is what Aegasis Labs helped build.

 

The Challenge

Data Volume Is Not the Same as Data Intelligence

Clippd’s core challenge wasn’t a shortage of data. It was the gap between the data they had and the intelligence they needed to surface from it. Golf performance data is structurally complex shot-level telemetry, round context, course conditions, practice session records, and historical player profiles arriving from different sources, in different formats, at different cadences.

Golfers today have access to more performance data than any previous generation. For example, launch angles, shot dispersion, strokes gained, practice session logs, round-by-round progressions. But data volume alone doesn’t produce insight. Raw telemetry sitting in disconnected sources, processed inconsistently, and surfaced through static dashboards tells a player very little about what to actually do differently. It’s noise dressed up as information.

For Clippd, this was the core product challenge. The platform was pulling in data from multiple devices and sources — each with different formats, different attributes, different levels of reliability. Without a normalized, governed data model underneath, every new analytics feature built on top of that foundation risked inheriting its inconsistencies. Metrics that looked different depending on how data was ingested. Benchmarks that weren’t truly comparable across course conditions or time periods. Insights that coaches couldn’t fully trust when making training decisions.

• The consequences compounded quickly. Coaches who cannot trust their data fall back on intuition. Players who get inconsistent feedback disengage. A sports-tech platform that cannot reliably answer the question “is this player improving?” struggles to justify premium subscriptions, team licenses, or elite program partnerships. 
  
  

• There was also a delivery problem. Without automated testing or CI/CD infrastructure, releasing new features was a manual, high-risk process. Every release carried regression risk. Every new analytics module took longer to ship than it should have. The team was spending engineering time managing releases rather than building product.
  
  

• The machine learning layer added another dimension. Clippd wanted ML-powered insights including trend detection, progression forecasting, and practice effectiveness scoring. But moving models from research to production safely requires MLOps discipline: versioning, evaluation pipelines, automated retraining, and monitored rollouts. Without that infrastructure, ML features remain research projects rather than product features.

  
  
  

Clippd needed a partner who could address all three layers simultaneously: data reliability, ML production readiness, and delivery infrastructure. Not sequentially. Together.

The Engineering Constraint

Clippd had the product vision, the user base, and the funding to become the defining platform in golf performance analytics. What they needed was the data engineering and ML infrastructure to actually deliver on that vision — reliably, at scale, and fast enough to stay ahead of the market.

The Solution

A Production Analytics Engine Built for Scale, Speed, and Trust

Aegasis Labs redesigned Clippd’s data and ML architecture from the foundation up, not as a replacement of what existed, but as a systematic upgrade that addressed each constraint while preserving continuity for the users and coaching teams already relying on the platform.

The engagement covered four interconnected workstreams: re-architecting the data pipeline, building production-grade ML modules, integrating BI and analytics services, and introducing the engineering practices including automated testing, CI/CD, and monitoring that would let the team sustain delivery quality as the platform grew.

Redesigning the Data Foundation

We re-architected Clippd’s data ingestion and modeling layer around an event-driven pipeline built for analytical workloads. At the core was a normalized data model for golf events, shots, rounds, and player profiles. A governed schema that gave every downstream analytical component a consistent, reliable source of truth to build on.

Batch and streaming feature pipelines were built to compute the performance metrics that power Clippd’s analytics: shot and round difficulty, player consistency scores, benchmark comparisons against peer groups, and practice effectiveness signals. dbt handled transformation logic with version control and documentation built in. Great Expectations provided automated data validation. Pipelines now run with greater than 99% success, with automated retries and end-to-end quality checks that catch issues before they reach users.

Building and Shipping Two ML Modules

The central delivery commitment was two production-grade ML modules, designed and shipped into Clippd’s live product. Both were built on the redesigned data foundation, which meant they had clean, reliable inputs from day one rather than inheriting the inconsistencies of the previous pipeline.

• Performance Scoring Module: The Performance Scoring Module applies machine learning to player shot and round data to produce interpretable performance scores, measuring progression, identifying trends, and generating benchmark comparisons across course conditions and peer groups. The scores are designed to align with how coaches think about player development, not just how data scientists define a metric.

   

• Progress & Recommendations Module. A progress and recommendations module that uses ML pipelines to detect performance trends over time, forecast likely improvement trajectories, and surface role-based coaching insights and practice priorities directly inside Clippd’s product interface. Coaches see what’s changing and what to address. Players see what to work on next.

Both modules were delivered through end-to-end ML pipelines covering feature computation, model training, evaluation, and a model registry with automated retraining and safe rollout capabilities. MLflow managed experiment tracking and model versioning throughout.

BI Integration and Analytics Services

We integrated BI and analytics services across Clippd’s product, giving players, coaches, and the internal operations team trusted, consistent access to performance metrics. Analytics dashboards were built to expose verified KPIs with clear lineage, and a secure API layer was established for partner integrations that needed access to Clippd’s data.

The analytical model was designed for comparability: metrics are consistent across time periods, course conditions, and peer groups, so a player’s performance data from six months ago can be meaningfully compared to today’s, and a coach working with multiple players can benchmark across their entire roster.

Engineering Practices That Make Delivery Sustainable

New features and ML capabilities only create value if they reach users reliably and quickly. We introduced automated testing, CI/CD workflows via GitHub Actions, and container-based deployment practices across the platform, replacing manual release processes with a pipeline that validates, tests, and deploys with consistency.

The result was fewer regressions, faster release cycles, and a meaningful reduction in the manual verification overhead that had been consuming engineering time. The team could ship more frequently and with more confidence, which matters in a competitive market where product velocity is a real advantage.

What Was Built

• Normalized Golf Analytics Data Model: A governed schema for golf events, shots, rounds, and player profiles — built with dbt and Great Expectations to provide a consistent, validated source of truth for all downstream analytics and ML features.
  
  
• Batch & Streaming Feature Pipelines: Event-driven pipelines computing shot difficulty, round difficulty, consistency scores, and benchmark comparisons — running on AWS with automated retries and end-to-end data validation at greater than 99% reliability.
  
  
• Performance Scoring ML Module: Production machine learning models that analyze player shot and round data to produce interpretable performance scores, progression metrics, and peer benchmark comparisons aligned with coaching practice.
  
  
• Progress & Recommendations ML Module: ML pipelines that detect performance trends, forecast improvement trajectories, and surface role-based coaching insights and practice priorities directly inside Clippd’s product interface.
  
  
• End-to-End MLOps Infrastructure: Feature computation, model training, evaluation, and MLflow-managed model registry with automated retraining pipelines and safe rollout capabilities — moving new ML capabilities from research to production reliably.
  
  
• BI & Analytics Integration: Analytics dashboards and KPI layers for players, coaches, and operations — with consistent comparability across time, course conditions, and peer groups, and a secure API for partner data integrations.
  
  
• CI/CD & Automated Testing: GitHub Actions CI/CD workflows, automated unit and integration tests, and container-based deployment practices that replaced manual release verification and enabled more frequent, predictable product releases.
  
  
• Observability & Monitoring: End-to-end pipeline monitoring, alerting, and performance observability that reduced maintenance overhead and gave the engineering team early warning of issues before they affected users.

Technologies

• Python (Pandas, scikit-learn) for data processing and ML model development
• dbt for transformation logic, data modeling, and documentation
• Apache Airflow for pipeline orchestration and scheduling
• MLflow for experiment tracking, model versioning, and registry management
• Great Expectations for automated data validation and quality checks
• AWS (S3, Lambda, Glue/Athena, Step Functions) for cloud data infrastructure
• Docker, Kubernetes, and Terraform for containerized, reproducible deployment
• GitHub Actions for CI/CD pipeline automation
• Modern BI tooling (Looker / Tableau / QuickSight) for analytics dashboards and reporting

The stack was selected for the specific demands of a production sports analytics platform: high data volume, low latency insight delivery, and the need for every metric to be explainable and traceable back to its source.

How We Worked Together

Aegasis Labs‘ Discover, Design, Build, Scale delivery model gave the Clippd engagement the structure it needed across four simultaneous workstreams. Data engineering, ML development, BI integration, and DevOps practice changes all had to move in parallel without creating dependencies that would slow each other down.

• Discover. We began by mapping Clippd’s existing data architecture, pipeline behavior, and ML maturity understanding what was reliable, what was fragile, and where the analytical ambitions of the product were being constrained by infrastructure limitations. That audit shaped the redesign priorities.
  
  
• Design. We produced a technical blueprint covering the normalized data model, ML module architecture, pipeline redesign, and CI/CD implementation plan, validated with Clippd’s engineering team before build began.
  
  
• Build. Data infrastructure redesign, ML module development, BI integration, and CI/CD implementation ran in parallel. MLflow provided continuous experiment tracking throughout the ML build, allowing model evaluation against coaching-relevant benchmarks at every stage rather than as a final gate.
  
  
• Scale. Automated testing and monitoring were operational before the ML modules went live. Pipeline reliability targets were validated against real usage patterns, not synthetic test conditions. The delivery infrastructure the team inherited from this engagement was built to sustain, not just to ship.

The Results

Aegasis Labs delivered two production ML modules into Clippd’s live platform, rebuilt the data foundation those modules run on, and established the engineering practices that let the team keep building with confidence. The outcomes are specific, sourced, and map directly to the problems the engagement was designed to solve.

What Shipped

Two production-grade ML analytics modules powering player performance scoring and coaching recommendations inside Clippd’s product. It was built on redesigned data pipelines running at greater than 99% reliability, with CI/CD workflows and automated testing that made the delivery sustainable.

The outcomes across each workstream:

Two ML Modules Live. Pipelines at 99%+. A Platform Built to Keep Improving.

Aegasis Labs delivered measurable outcomes across every dimension of the engagement: data reliability, ML production readiness, delivery velocity, and operational stability.

• Two production analytics modules now live in the product. The Performance Scoring and Progress and Recommendations modules are running in Clippd’s platform, powering player and coaching insights directly in the user experience. These are not internal tools or prototypes. They are product features that paying users interact with.
• 
  Pipelines running at greater than 99% success. Automated validation, retries, and end-to-end data quality checks mean the analytics layer runs reliably at scale. Coaching teams and product managers work from data they trust. Edge cases that previously caused silent failures are caught and handled before they reach the product.
• 
  Faster, safer releases. CI/CD and automated testing removed the manual overhead and regression risk that had slowed delivery. New features and model updates move through a defined pipeline, tested, validated, and deployed with confidence. Releases happen more frequently because each one carries less risk.
• Operational efficiency at scale. Streamlined pipelines and observability reduced maintenance overhead and improved stability during peak usage periods. The engineering team spends less time firefighting and more time shipping.
• 
  A governed single source of truth. The normalized data model and analytics warehouse give every team, product, coaching, operations, and partners, consistent and comparable metrics. Benchmarks mean the same thing regardless of when or where the data was collected. KPIs are defined once and trusted everywhere.
• 
  An ML infrastructure built for the long term. Automated retraining, model versioning, and staged rollouts mean Clippd’s ML capabilities improve continuously as more data flows through the system. The research-to-production pipeline is established. Adding new models is now an engineering workflow, not a one-off project.
  
  
• A platform positioned to lead. With reliable data infrastructure, production ML, and delivery practices in place, Clippd is positioned to pursue the commercial outcomes their funding was raised to achieve: elite program partnerships, team licenses, and partner integrations built on analytics that genuinely outperform anything else in the golf technology space.

The broader shift is one of capability and confidence. Clippd entered the engagement with the ambition to be the universal performance data platform in golf. They leave with the analytics engine, the data infrastructure, and the engineering practices to pursue that ambition at the pace a $10M-funded, venture-backed sports-tech company needs to move.

Build Your Data & AI Platform with Aegasis Labs

The Clippd engagement shows what Aegasis Labs delivers when a data-rich product needs engineering depth: reliable pipelines, production ML, and the delivery infrastructure to keep shipping with confidence.

Aegasis Labs has delivered 70+ projects across 8+ industries, with a 97% project completion success rate and 95% client satisfaction. Our 30+ consultants bring top 1% technical expertise in data engineering, machine learning, and cloud architecture. We are a certified partner of both AWS and Microsoft.

If your platform is sitting on valuable data that is not yet driving product decisions or user outcomes, we would like to show you what is possible.

Ready to Scale Your Analytics Platform?

If your data infrastructure is constraining your product roadmap — or your ML capabilities aren’t reaching users as fast as they should — visit aegasislabs.com/contact to start the conversation.