Word Collections
The Client
Word Collections is a global music publishing administration and royalty management company founded by Jeff Price, the industry innovator behind TuneCore and Audiam. The company helps songwriters, publishers, and rights holders maximize royalty earnings through modern rights management, royalty tracking, and licensing solutions.
As the volume of newly released music continued to scale globally, Word Collections needed a more automated and intelligent way to identify derivative works, licensed recordings, covers, and reused content across massive streaming datasets.
RevStar partnered with Word Collections to productionize an AI-powered audio waveform identification platform capable of continuously analyzing newly released music and detecting royalty-eligible content at scale.
Summary
AI-Powered Audio Identification Engine
Music Publishing & Royalty Management
AWS SageMaker + Machine Learning + Cloud-Native Architecture + Data Lake Infrastructure
Solution Architecture, Delivery Management, AI/Data Engineering, DevOps Engineering, Quality Assurance
The solution exceeded 90% accuracy benchmarks, reduced projected manual review effort by more than 50%, and established a scalable foundation for continuous AI-driven royalty recovery operations.
Enabled faster and more scalable royalty recovery operations through automated licensed content detection and AI-powered audio analysis.
The Challenge
The rapid growth of global music streaming created a significant operational challenge for Word Collections’ royalty management workflows.
Manually identifying derivative works, licensed recordings, covers, and reused audio content across monthly streaming reports became increasingly time-consuming, inconsistent, and difficult to scale. As new releases accelerated globally, human review processes could no longer keep pace with the growing volume of audio requiring evaluation.
Without an automated system for continuous analysis, delayed identification of royalty-eligible recordings increased the risk of missed claims, delayed revenue recovery, and operational inefficiencies.
Word Collections needed a scalable, production-grade solution capable of continuously analyzing newly released music with high accuracy while minimizing manual intervention.
The Problem
As global music releases continued to grow, Word Collections needed a more scalable and automated way to continuously identify licensed audio content across large streaming datasets while reducing reliance on manual review workflows.
The Solution
We designed and deployed a secure, AWS-native licensed content detection platform powered by machine learning and scalable cloud infrastructure.
The solution automated the analysis of newly released audio content, helping streamline royalty identification workflows while reducing the operational burden of manual review. Built to support rapidly growing streaming volumes, the platform was designed with scalability, reliability, and long-term operational efficiency in mind.
To support ongoing growth and continuous improvement, we implemented cloud-native infrastructure, automated processing workflows, monitoring systems, and operational tooling aligned to AWS best practices for security and performance.
The Team
