This quarter, we’re excited to unveil TunedIQ: our intelligent recommendation engine that learns directly from real listening behaviour. Forget static tags and stale playlists. TunedIQ adapts to evolving habits, delivering discovery moments that feel fresh, personal, and effortless. While you focus on growing your audience, we’ve been perfecting smarter personalisation, seamless integration, and features designed to keep users listening longer. This isn’t just another algorithm; it’s music intelligence that performs.
If you want to get in touch about TunedIQ or see it in action, contact us here.
Why TunedIQ?
Most recommendation systems still depend on metadata and genre tags. These approaches are often inconsistent, incomplete, or simply too rigid to capture the nuances of listener behaviour. They struggle with diverse catalogues, cold starts, and scaling across markets, which often leads to flat, impersonal experiences that fail to keep users engaged.
TunedIQ is different. By analysing behavioural patterns and listening habits, it continually adjusts recommendations to suit each user. This means more meaningful discovery, more reasons to return, and fewer drop-offs over time.
Built for scale, designed for music
TunedIQ is purpose-built for music services, from DSPs to fitness, medtech, and gaming platforms. It works without static metadata, making it especially effective for catalogues with limited or inconsistent tagging. Delivered as a plug-and-play solution, TunedIQ is API-ready, integrates seamlessly into white-label apps, and requires minimal engineering lift.
What TunedIQ delivers
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Daily discovery mixes: curated playlists blending familiar favourites with new tracks to inspire daily engagement. |
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Your trending tracks: Put the spotlight on the tracks that truly fit each user’s habits. |
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Your trending artists: Stay in the loop with trending artists, surfaced uniquely for each listener. |
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Continuous play: automatically extends listening sessions from seed tracks when a playlist ends. |
The tech behind TunedIQ
Under the hood, TunedIQ uses collaborative filtering and machine learning models to understand how users engage with music. Instead of relying on static metadata, it looks at patterns in listening behaviour and adapts suggestions accordingly.
• Collaborative filtering core: generates 1:1 recommendations based on behaviour, not tags.
• Cohort-based logic: groups similar user behaviours to surface smarter discovery, even with smaller datasets.
• Adaptive learning: constantly updates based on the latest listening activity, ensuring recommendations evolve alongside user habits.
• Client-specific models: every implementation is isolated and secure, with no cross-service data sharing.
• Seamless UI integration: recommendations appear as dynamic shelves in-app, with empty shelves hidden for a clean experience.
This combination makes TunedIQ both powerful and flexible, easy to deploy via APIs or white-label apps, but sophisticated enough to rival the biggest DSP recommendation engines.
With TunedIQ, your platform doesn’t just play music — it delivers dynamic, engaging experiences that turn passive listeners into loyal fans.
Get in touch if you want a deeper demo.
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