Experience music that adapts to you — in real time.

Our GPU-accelerated recommendation engine blends audio signal, raga/time-of-day, mood/context, and listening behavior into a unified 211‑dimension profile. Enjoy smarter next‑song predictions, fewer skips, and an infinite Smart Radio that evolves with your feedback.

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Next‑Song Prediction

Bidirectional LSTM + attention over 50×211 feature sequences deliver fast, accurate recommendations — tailored to context.

Skip Prediction

Identify likely skips before they happen; pre‑filter queues and optimize shuffle for smoother sessions.

AI Audio Enhancement

Noise reduction, dynamic range compression, spectral enhancement, and normalization for consistent quality.

Projected Performance

50–100 ms
Prediction latency*

90%+
Top‑5 accuracy*

70–80%
GPU utilization*

~100 ms
50‑song batch*

* Internal benchmarks. Results vary with data, hardware, and tuning.

Smart Radio

Infinite, AI‑curated playback that respects mood and time‑of‑day — adapts to likes/skips in real time.

Developer‑Ready APIs

POST /predict/next
POST /predict/batch
POST /predict/skip/{song_id}
POST /enhance/{song_id}
POST /radio/start
GET /radio/{station_id}/next

Observability & Control

Model info, GPU status, health checks, and admin training endpoints keep ops confident.

AI Model Visualizations

A look under the hood at the clustering and feature engineering that powers our recommendation engine.

1. Cluster Distribution

Bar chart showing the distribution of songs across 40 clusters.

What it shows: How our vast music library is segmented into 40 distinct clusters. This reveals which musical styles are most common (large bars) and which are more niche (small bars), helping us balance variety in our recommendations.

2. Cluster Embeddings (PCA Plot)

PCA plot of 40 cluster centers, with cluster IDs labeled.

What it shows: A 2D map of the 40 music clusters. Clusters that are close together on this map are musically similar in the feature space, while distant clusters represent distinct musical genres or moods. This is how the AI groups similar songs.

3. Song-feature Prediction Space (PCA Explained Variance)

Line and bar chart showing individual and cumulative explained variance by Principal Component.

What it shows: Analyzes the 211-dimension song feature set. The plot shows that a small number of key features (Principal Components) capture most of the unique information (variance) in the music data. This confirms that the AI can make highly accurate predictions using a reduced, effective "prediction space."

Get Started

Deploy the Advanced Phase and bring adaptive listening to life. Contact us for a guided demo and integration plan.

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Disclaimer

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