AI & Sonic Analysis:
From Sonic DNA to Global Impact

Groovie Perspective

At Groovie, we treat music as data.

Every track is analyzed with advanced AI libraries — Prophet for forecasting, Robyn for marketing mix modeling, H2O AutoML for scalable predictions, and CausalImpact for cultural insights.

On top of these, we are building custom LLM projects:

Mood Tagging LLM → detects the emotional DNA of tracks with cultural nuance.
Sync Readiness LLM → ranks music by how suitable it is for film, series, and ads.
Scouting LLM → filters global catalogues to discover unheard artists.
CRM/Brand Refactoring LLM → transforms retail & brand data into music-driven insights.

These pipelines allow us to scale beyond a label — creating a modular AI studio where sound, culture, and business strategy meet.

Sonic Analysis is how we decode music beyond genres. Using AI libraries like Prophet, Robyn, H2O AutoML, and CausalImpact, we turn raw sound into emotional DNA, ready for sync, branding, and global stages.
We are both a label and an AI studio. Instead of following industry formulas, we blend culture, data, and advanced modeling — building a modular system where rebel sounds become global movements.
We benchmark campaign music, measure emotional consistency, and offer Stereo IQ Scores to help brands stand out with a distinct sonic identity.
We provide artists with real-time insights during the first 24 hours and first week after release — showing how their music performs, where listeners connect, and what drives engagement. This helps them make faster, smarter creative and strategic decisions.
Our custom LLMs push beyond analysis. Mood Tagging LLM classifies emotion, Sync Readiness LLM ranks songs for film/ads, Scouting LLM discovers unheard artists, and Brand Refactoring LLM transforms CRM data into music-driven insights.
We track immediate reactions and engagement to adjust promotion instantly.
We measure momentum and reveal growth curves and listener retention.
We forecast streaming and campaign ROI with Prophet and Robyn.