A Personalized Video Recommendation PWA
When the WePlay team came to us, they weren’t trying to build “just another PWA.” They wanted something bold: a platform that could understand a listener’s music taste on Spotify and instantly translate it into the kind of short-form videos they’d enjoy on TikTok and Instagram.
A simple idea on paper. A complex engineering challenge in reality.
But exactly the kind of challenge Capital Compute loves, whether it’s a startup concept, a boutique engineering idea, or a studio-grade product that needs a custom backend.
The Real Goal Behind WePlay
The vision was clear: “If a user loves a certain artist or mood on Spotify, why should their video recommendations feel random?”
WePlay wanted a bridge between audio preferences and visual discovery, between the music you vibe with and the creators you’d connect with, and between a user’s streaming identity and their social video journey.
And that’s what we built together.
What Made This Project So Interesting
Unlike typical PWAs, WePlay had three tricky layers:
- Understanding Spotify at a deep level—not just liked songs, but tempo, energy, genre, and mood patterns.
- Finding videos that match those patterns in real time from Instagram and TikTok.
- Making everything work offline so even users in low-connectivity zones could browse smoothly.
This case study highlights how Capital Compute built WePlay, a unique fusion of music and social video recommendations, powered by data extraction and seamless social media integration.