For music streaming platforms like HIO Music, providing personalized music recommendations and enhancing user engagement is critical. However, conventional recommendation engines often deliver generic suggestions that fail to reflect listener mood, activity, or preferences. HIO Music partnered with GoML to build an AI music assistant, “HIO Mode”, to provide conversational, context-aware music discovery and app support for both listeners and artists. GoML built an AI powered solution based on our proprietary accelerated AI delivery framework and LLM boilerplates.
The problem: limited personalization and fragmented music discovery
HIO Music users struggled to find songs that matched their current activity, mood, or listening preferences. Traditional recommendation systems offer limited personalization, often leaving users disengaged and making music discovery tedious.
Artists also lacked meaningful insights into their listener base, reducing their ability to connect with fans or drive engagement. Users could not intuitively ask, “Play relaxing jazz for my evening workout,” or receive suggestions based on prior listening history. The absence of a natural language interface, contextual awareness, and dynamic playlist generation hindered both user satisfaction and artist growth.
The solution: AI music assistant for conversational music discovery
GoML built HIO Mode, an AI music assistant using Claude models, integrated directly into the HIO Music app. The assistant allows both text and voice interactions to deliver personalized recommendations and contextual app support.
AI music assistant capabilities
- Conversational agent: Understands natural language requests and routes them to appropriate recommendation workflows.
- Music recommendation engine: Generates personalized playlists based on user behavior, mood, and context.
- LLM integration: Uses Claude models to provide context-aware suggestions with conversation memory.
- Database integration: Connects with PostgreSQL to access user preferences, listening history, and music metadata.
- Player integration: Supports playlist creation, modification, and playback with existing HIO Music player features.
Powered by Claude + GoML’s AI Matic framework
- Language intelligence: Claude LLM interprets queries and orchestrates personalized music recommendations.
- Database integration: PostgreSQL stores user preferences and music metadata for context-aware suggestions.
- Multi-modal interaction: Supports both text and voice inputs.
- Scalable architecture: Handles millions of songs and a growing listener base.
- Security: Robust measures protect user and music data privacy.
Scalable architecture across cloud-native components
- Data ingestion: Accesses HIO Music catalog, user preferences, and listening data.
- Context injection: Embedding session context, current track, and listening history for recommendations.
- Recommendation orchestration: LLM-powered system generates personalized, context-aware playlists.
- API integration: Seamless integration with existing music player and app infrastructure.
The impact: intuitive music discovery with AI
HIO Music listeners now experience:
- Increased engagement: Personalized playlists encourage users to spend more time exploring music.
- Better artist-audience connection: Artists gain visibility into listener behavior and preferences, enabling stronger engagement.
- Enhanced satisfaction: Conversational AI makes music discovery interactive, intuitive, and enjoyable.
Lessons for music streaming platforms
What HIO Music learned
- Personalization doesn’t just mean more recommendations; it means removing friction from music discovery.
- Conversational AI can unlock the value hidden in listener behavior and preferences.
- Real-time context-aware suggestions make the listening experience more engaging.
Advice for streaming teams
- Identify recurring recommendation patterns; these are prime candidates for automation.
- Use modular agents to scale intelligence across app features and user touchpoints.
- Pair LLMs with robust data governance and user context embeddings for precise, personalized outcomes.
Looking to make your music platform smarter?
Let GoML help you build an AI music assistant that turns music discovery into a personalized, conversational, and scalable experience.
