Traditional attribution answers one question:
Did this output match a known work?
Generative systems require a different perspective:
Which licensed material influenced this output, and by how much?
Musical AI measures proportional influence at the output level
Training pipelines and model internals are proprietary, volatile and unnecessary for licensing decisions.
We operate at the output boundary where content becomes real and attribution actually matters.
Attribution is tied to each generated result.
For every AI generated output, Musical AI computes proportional attribution across all contributing sources.
The total always sums to 100%, reflecting how licensing agreements actually work.
Music rights aren't monolithic.
Musical AI computes attribution for sound recordings and underlying compositions independently. Both are produced within the same system and reported separately. No abstraction, just alignment with how real contracts work.
Every attribution record is repeatable, evidence-linked, and traceable to concrete output segments. All records are retained for audit and inquiry. Not a probabilistic guess. A defensible system of record.
Designed for reality
Musical AI prioritizes repeatability over novelty, auditability over theory, deployment over research purity. That's what makes it work in real licensing environments—not just in demos.
Minimal surface area
Musical AI integrates once, after generation. Send outputs or output representations, get attribution results back. Sensitive data can stay fully in-house. No model access. No training data exposure. No pipeline changes.
Musical AI doesn't inspect model weights or gradients, train or fine-tune models, determine infringement, or enforce policy and takedowns. We measure influence. You decide what happens next.
Consent proves permission; attribution proves influence. SEE CONSENT →
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