Attribution is possible.
Frictionless, trusted, and predictable attribution infrastructure for AI-generated music. Built for scale. Generates attribution splits on licensed AI content without touching model internals.
Trusted by Industry Leaders
Attribution that doesn’t slow you down.
Musical AI operates entirely downstream of generation, with a single integration point at the output boundary. No access to model internals. No changes to training pipelines. No exposure of proprietary IP.
The passport to do business.
By integrating Musical AI, companies inherit a level of trust already established with rights holders and publishers. Our attribution approach is auditable, repeatable, and aligned with how music rights actually work.
Compliance without financial surprises.
Musical AI is priced per attribution event. Costs scale linearly with generation volume. Finance teams can model attribution as a known unit economic, not an open-ended risk.
Don't let attribution and compliance slow down your shipping process. Musical AI integrates at the output boundary, post-generation, enabling teams to:
Ship licensed generative music without slowing down
Preserve full control
over your models and training data
Continue rapid iteration without costly
re-architecture
Turn attribution on as infrastructure, not as a research project
Meet your licensing requirements without disrupting your roadmap.
Turn rights holders into partners
Generative music companies are collaborating with rights holders at scale. Converting training data and influence into compensation is driving high-stakes deals.
Per-output attribution records
Proportional pro-rata influence aligned with licensing deal requirements
Evidence-linked results that stand up to audit
Become a trusted counterparty and signal industry growth.
When attribution becomes a system constraint, we should talk.
Your teams will want to engage Musical AI when:
Attribution requirements surface late in the pipeline
Existing tooling can’t explain influence at output scale
Internal builds start competing with core ML roadmap work