Supercharging the Music Label A&R Process with AI

We created multiple trained LLMs that help identify and deliver a list of trending, new artists/bands in specific genres, based on music streaming platforms, Fast-tracking the A&R process.

The challenge

A renowned indie music label, known for its influential role in the punk music scene, faced the challenge of updating its artist discovery process. In a sector traditionally dependent on conventional scouting methods, the label aimed to leverage cutting-edge technology to improve its ability to unearth new musical talents. The objective was to replicate their historical success in adopting new technologies, this time by integrating the latest advancements in AI.

The solution

A sophisticated A&R AI assistant (ARnie), specifically tailored to the label's unique requirements. Distinguishing itself from standard tools, ARnie integrates advanced Large Language Models (LLMs), social media data, and key music APIs. This specialized AI assistant is equipped with:

Integrated Chat Interface: Employing LLMs for intuitive and engaging data analysis.
Interactive Conversational Flow: Moving beyond traditional metrics to a dynamic, user-centric interface.
Contextual Memory System: Enabling the A&R team to store and compare data seamlessly, leading to more strategic decisions.

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The Innovation

ARnie marks a significant advancement in A&R technology by leveraging bespoke, fine-tuned AI models, based on specific data insights, delivers exceptional value. More than a mere data aggregator, ARnie redefines the interaction between the music industry and information, steering strategic and informed decision-making.

The results

ARnie is still in Beta testing, however the label has significantly expanded its capacity for discovering new artists, increasing the rate of artist discovery. This advancement demonstrates ARnie's effectiveness in empowering the label's teams at the onset of the creative process. It offers enhanced intelligence and insights, particularly from social media data, streamlining the early stages of artist discovery.

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