How Evrone Scaled a Streaming Platform with AI + Go

Evrone helped a large music streaming platform with 75M+ tracks improve product metrics and engineering efficiency through AI-powered recommendation systems, retention strategies, and LLM-based search.

Details

The streaming platform already offered a range of music, podcast, and audiobook services, but needed to scale its systems to handle millions of user actions, generate relevant recommendations, deliver fast search results, and control infrastructure costs. Evrone's ML engineers improved the recommendation models by analyzing signals like repeated plays, skips, time of day, content type, and mood to create more personalized playlists and better content discovery. Instead of pushing aggressive notifications, Evrone supported habit-based personalization, delivering tailored content like gym playlists, commute podcasts, and evening audiobooks. The platform also struggled with search due to many tracks sharing identical names, so Evrone implemented LLM-powered contextual search logic and automated SEO content for artist and release pages. On the engineering side, Evrone modernized critical systems by rewriting legacy components in Go, splitting monoliths into services, migrating analytics tools, reducing compute waste, and improving maintainability. The results included 20-30% lower costs, 20% faster performance, improved retention, and faster development cycles.