🎼 How Evrone Scaled a Streaming Platform with AI + Go

#videostreaming #machinelearning #ai #devops

Scaling a streaming platform means far more than adding new songs. A modern product must process millions of user actions, generate relevant recommendations, deliver fast search results, and control infrastructure costs. That was the challenge Evrone helped solve for a large streaming platform with 75M+ tracks.

The service already combined music streaming, podcasts, audiobooks, offline listening, recognition tools, and voice assistant integrations. Evrone joined internal teams to improve both product metrics and engineering efficiency.

🚀 Where Evrone Added Value

1. Recommendation Systems

Evrone ML engineers improved models that understand user behavior. Great recommendations depend on more than genre labels. Systems also analyze:

repeated plays
skips after a few seconds
time of day
content type
current mood signals

This created more relevant playlists and better discovery.

2. Retention Without Dark Patterns

Instead of pushing aggressive notifications, Evrone supported habit-based personalization. Users could receive:

🎵 a gym playlist
🎙️ a commute podcast
📚 an evening audiobook

3. LLM Search and SEO

Many tracks share identical names. Traditional search often struggles in that environment. Evrone improved internal search with contextual LLM logic. Evrone also supported automated SEO content for artist and release pages.

🛠 Engineering Work

The platform had legacy services consuming too many resources. Evrone modernized critical systems by rewriting heavy components in Go while preserving expected behavior.

Additional upgrades included: