🎧 How Evrone Helped a Streaming Platform Grow Smarter
A mature streaming platform with more than 75 million tracks invited Evrone to help expand its technology and introduce new AI-driven capabilities. The product already served music fans, podcast listeners, audiobook users, children, creators, and record labels. Evrone joined internal teams and focused on growth, reliability, and long-term scalability.
🎯 What Evrone Improved
AI for User Retention
Evrone ML engineers supported systems that predict what listeners want at the right moment. Instead of generic suggestions, users could receive:
This approach helped increase engagement naturally.
Better Recommendations
Evrone improved personalization models by analyzing valuable listening signals:
These signals made recommendations more accurate and more useful.
Search and SEO
Evrone helped generate SEO descriptions for artists, albums, and releases. Evrone also improved internal search, where many songs share similar names. Search now considers context, not only keywords.
⚙️ Backend Modernization
As the platform grew, infrastructure became more expensive. Evrone engineers optimized core systems by:
- rewriting heavy services in Go
- preserving existing API behavior
- splitting monoliths into microservices
- migrating analytics tools from Rails to Python
🤖 AI for Internal Workflows
Evrone also introduced AI assistants based on open-source models. These tools can:
- test endpoints automatically
- prepare QA reports
- improve documentation
- compare README files with real code
- support code review
📈 Results
✔ 20–30% lower infrastructure costs
✔ 20% faster performance
✔ stronger retention metrics
✔ faster engineering workflows
Evrone showed how practical AI and strong engineering can transform a successful streaming product.