Music workflow for the AI-agent era
bigcut helps music teams find songs, choose them, prepare releases, and preserve why those choices matter.
A song's value is not only inside the audio file. It also lives in who selected it, why it was chosen, when it was prepared, and what context should travel with it. bigcut helps A&R teams, publishers, labels, songwriters, and music directors connect reference search, Final Cut selection, Release Prep, and explicit submission into recommendation context AI agents can understand.
bigcut does not replace human judgment. It narrows candidates with reference matching, keeps the selection path through shortlist and Final Cut, and organizes metadata, credits, writer shares, and curation notes in Release Prep.
Local work that users do not submit does not become recommendation data. Only records and data explicitly submitted from Final Cut or Release Prep enter that path.
Blockchain is used as a proof layer for timing and integrity, not as a place to publish original content. Original user IDs, song IDs, audio, notes, and embeddings are not revealed on-chain.
The principles behind bigcut
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Keep human selection at the center
AI can narrow the candidate list, but the final choice still belongs to the people listening to the music.
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Use only submitted data for recommendation context
Private libraries, listening sessions, and unsubmitted notes stay out of the recommendation data path by default.
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Prove the record, keep originals private
Selection records can be verified later, while original music data and personal identifiers are not placed on-chain.
Company
bigcut Systems
Representative
Gun Woo Park
Founded
2026
Address
Room 48, M-Peace Cheonan Center, 5F Rodem City Bldg, 47 Cheongsu 9-ro, Dongnam-gu, Cheonan-si, Chungcheongnam-do, 31190, Republic of Korea
Legal contact
[email protected]