Background
A global music data management company, Revelator, required a royalty attribution engine capable of collecting consumption data from multiple streaming platforms and automatically calculating and distributing revenue among artists and stakeholders.
Coaldev developed a fully automated royalty engine that aggregated play counts and revenue data from platforms such as Spotify and Apple Music using Azure APIs. It processed digital artist contracts and allocated revenue according to predefined percentages while managing trailing balance adjustments and multi-artist revenue splits.
Challenges
While building the royalty engine, we encountered several technical and operational complexities that required careful orchestration across data, contracts, and platform APIs. Below are the key challenges we addressed to ensure precision, scalability, and automation.
Integrating APIs from multiple streaming platforms.
Handling large datasets and differing contract structures.
Automating financial logic for varied royalty models.
Ensuring precision in contract-based percentage distribution.
Solution
Coaldev implemented automation pipelines that ingested data, validated contract logic, and executed revenue distribution accurately, including special handling for albums and multi-artist collaborations.
Coaldev engineered an end-to-end data and automation pipeline that unified ingestion, validation, and royalty computation into a single cloud-based ecosystem.
Key solution elements included:
1. Automated Data Aggregation
Azure-based pipelines continuously collected consumption metrics and revenue reports from multiple music platforms.
2. Contract Processing
Scripts dynamically parsed artist agreements to determine accurate split percentages, deductions, and distribution rules.
3. Revenue Calculation Engine
Python-based modules executed automated payout computations, accounting for multi-artist collaborations and complex album structures.
4. Data Validation Layer
Quality checks ensured consistency in currency conversion, contract logic, and final payout accuracy.
5. Reporting and Tracking
A centralized dashboard provided stakeholders with real-time visibility into earnings, distributions, and performance metrics.
This architecture allowed for high-volume data processing while maintaining financial transparency and compliance with contractual obligations.
Results
The royalty attribution system significantly improved payout speed, transparency, and financial accuracy for the client’s internal operations.
Key results
Automated collection and processing of music consumption data.
Contract-based revenue distribution without manual calculation.
Streamlined reporting and payment tracking via Azure-based infrastructure.
Increased reliability in artist compensation across multiple platforms.
Technology Stack
- Python
- Azure APIs
- Data Processing Pipelines
- Cloud Automation
Live Link: https://www.revelator.com/
