saascode
saascode

Sponsortrace

A creator-income radar that records when a catalog is cited by AI answers, checks supported dataset-presence registries, and matches eligible works to open sync-licensing briefs — while keeping citation, training evidence, ownership, licensing, and payment as separate states.

Genesis score7.05/10
Make Sponsortrace real.0/500
500 more votes and Sponsortrace is authorized for build.
0%500 to authorize
Backing is the vote. When an idea crosses 500, we pull it into the build pipeline and ship it for real — the votes decide what gets built next, not an editor.
The opportunity
24%Projected musician income loss
120/minObserved sync API limit
3Jobs combined
The case

A mid-tier musician, illustrator, or voice performer may see traditional income erode while new opportunities and rights signals scatter across unrelated systems. Sponsortrace brings three queues together: observed AI citations of the creator's catalog, registry evidence that a work may appear in a training dataset, and sync briefs whose requirements match eligible catalog items. It does not claim a citation creates compensation, that dataset presence proves infringement, or that a brief creates a license. The product helps a creator decide where to investigate, assert rights, or pitch work, with source evidence preserved.

Who pays — and why

A working mid-tier musician, illustrator, or voice performer with a meaningful catalog and shrinking or fragmented income, plus the manager or rights organization helping them pursue attribution and licensing opportunities.

What it unlocks
A longitudinal per-creator corpus connecting catalog identifiers, citation observations, registry matches, briefs, pitches, placements, licenses, and outcomes without collapsing one state into another
A source-linked evidence packet that makes possible rights follow-up faster while preserving uncertainty and ownership boundaries
A matching layer for high-touch sync work that automated generation does not itself satisfy: custom voice, narrative scoring, and live-event composition
How Genesis scored it
7.05across seven criteria
tension 6temporal 8blindspot 5buyer 8leverage 8convergence 5why-not 8
8
Temporal window

A current AI-licensing settlement wave and a reported 24% musician-income-loss signal create urgency around creator rights and replacement income.

8
Buyer persona

Mid-tier creators and their managers have a direct income-replacement job and an identifiable catalog to monitor.

5
Convergence

One cross-reference, four inbound links, and three direct connections show a useful local cluster without broad convergence.

Why it scored well

The displaced mid-tier creator is a specific buyer, a current licensing-settlement wave supplies timing, and all three component data paths were confirmed as real. The combined catalog and outcome history can compound per creator.

What's holding it back

Convergence is modest, no incumbent copying cost is established, and each signal has serious epistemic limits. Citations do not guarantee payment, dataset matches do not prove legal claims, and sync opportunities require rights clearance and human relationships.

Signals detected4 sources crossed
SignalUNESCO signal cited in the source run

SignalSongtradr API client research

SignalSoundcharts and Spawning / Have I Been Trained research

Signal2026 competitor scan

Direction briefsponsortrace.md
sponsortrace.md
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