// Case study · Music & Media
One codebase that put a social music discovery platform on the web, iOS, and Android
TownWave is a social music discovery platform built to give control back to up-and-coming artists. We took it from design concepts and wireframes to a single-codebase product that launched on the web, iOS, and Android.
- Client
- TownWave
- Platforms
- Web App · iOS · Android
The project
The challenge
TownWave set out to build a social music discovery platform that works in favor of up-and-coming artists and gives control back to them, rather than to the traditional music industry. It needed help taking the idea through design concepts and wireframes to a finished product.
To reach listeners, the platform had to run on the web, iOS, and Android. Building and maintaining three separate apps would multiply the cost of every feature, so it needed to be efficient to build and to run.
What we built
We worked with TownWave from early design concepts through wireframing to the full product build.
We built the back end on a relational database, a natural fit for a social product where much of the value lives in the connections between listeners, artists, and songs.
The front end was built with hybrid application technology, so the entire platform shared one codebase, optimized for efficiency. A single codebase meant one team could ship each feature to the web, iOS, and Android, and TownWave launched on all three.
The result
- TownWave launched on the web, the iOS App Store, and the Google Play Store.
- The entire platform ran on a single codebase, optimized for efficiency.
- We took the product from design concepts and wireframes through the finished build.
Built with
- Relational database (back end)
- Hybrid application front end
- One codebase for web, iOS, and Android
Where AI takes it next
How we'd extend this product with today's AI — the same thinking we bring to every client roadmap.
- 01
Discovery by sound, not popularity: audio embeddings (numeric fingerprints of how a track sounds) match songs by tempo, mood, and texture, so a new artist can surface next to a listener's favorites before building a following.
- 02
Natural-language search: a listener types "moody late-night R&B with live drums" and gets real matches from audio features, tags, and artist profiles.
- 03
An artist copilot that turns plays, shares, and follower data into plain-English advice: which track to push, when to release, and which listeners are closest to becoming fans.
- 04
AI moderation and rights protection: screen uploads and comments for spam and abuse, and use audio fingerprinting to catch re-uploaded tracks, so artists keep control of their work.
Services on this project
What we brought to the table.
Next step
Have a product or process like this?
Tell us what you're trying to build or fix. We'll come back with an approach, a rough timeline and the AI opportunities hiding in it.
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