The data layer for music AI.
Your model is only as musical as its data. Wolftone designs, validates, and enriches music datasets — built by the operators who shipped fifty production AI/ML algorithms and enriched 500K+ artist products inside Universal Music Group and EMI. Musical judgment plus data engineering, in one team.
Where music expertise meets the AI stack.
Music AI is still early in its infrastructure phase. These are the layers where a musically-literate data partner changes model quality.
Synthetic Data Validation
Musicality scoring, originality and copyright-risk assessment, and style-consistency audits for synthetic training data — for generators and research labs.
Multimodal Dataset Architecture
Audio × video × lyrics × metadata: cross-modal alignment, taxonomy design, and synchronization QA for platforms training multimodal models.
Knowledge Graphs & RAG
Structured music knowledge — artists, genres, influences, samples — as retrieval corpora for generation systems, with copyright-safe reference curation.
Spatial Audio Intelligence
Classification, mix-quality assessment, and dataset curation for immersive audio — Atmos, spatial, VR/AR, and gaming.
Agentic Curation QA
Evaluation frameworks and ground-truth data for autonomous curation, A&R scouting, and catalog-optimization agents.
Labeling & Enrichment
Human-expert labeling and metadata enrichment at catalog scale — the discipline that made 6M products discoverable across nine languages.
Built for the people building music AI.
PLATFORMS ........ multimodal & spatial datasets
RESEARCH LABS .... world-model & influence-graph data
LABELS ........... catalog enrichment & AI-readiness
Scope a dataset engagement.
Most engagements start with a fixed-scope pilot: one dataset, one validation pass, or one knowledge-graph slice — so you can judge the quality difference before committing to scale.