Multiple legacy platforms limited visibility across the full royalty process. Internal teams relied on significant manual effort for reconciliation and reporting. Artists lacked clear, reliable insight into how their earnings were calculated and distributed. And as the organization expanded into more diverse markets, the existing architecture couldn’t keep pace with accessibility requirements or multilingual support needs.
Performance limitations created processing delays and slowed the delivery of royalty statements. The underlying architecture made it difficult to introduce new features, adapt to new business models, or integrate with an increasingly complex ecosystem of partners and distribution platforms. Trust, both from artists and from internal stakeholders, was under pressure. The organization needed a new foundation.
Solution
Our capabilities in AI-native engineering gave Brillio an edge during the client’s selection process for a transformation partner. Our deep understanding of media and entertainment ecosystems, and our ADAM (Agentic Data and Application Management) platform, proved to be an added advantage. Additionally, our established partnership with AWS was a further differentiator.
The approach began with joint workshops, architectural assessments, and co-innovation sessions spanning business, technology, and security stakeholders. Pilot implementations validated design decisions before the platform scaled across all territories, allowing the teams to confirm architectural choices and optimize service configurations early.
The new platform was built on AWS using a serverless, microservices-driven, API-first architecture. Amazon EKS provided the Kubernetes-based deployment foundation, with GitHub Actions, Flyway, and Istio Service Mesh orchestrating microservice delivery and environment provisioning. This standardized approach enabled new environments to go live within minutes rather than weeks.
Amazon Redshift served as the core data model, delivering faster data access, more consistent reporting, and improved performance across territories. Machine learning models were integrated to generate more accurate royalty forecasts and provide artists and business teams with forward-looking visibility into revenue patterns and content performance.
A redesigned, device-agnostic user interface introduced multilingual support and gave artists intuitive access to payment information, statements, and analytics. Delivery was managed through a product-centric model using autonomous PODs that scaled as the program expanded, ensuring continuous development, standardized DevOps practices, and reduced time-to-market for new features.
