Everythingyouneedtofindtherightsong.

Built for A&Rs, publishers, music supervisors, and music directors who need to match sounds efficiently — without compromising privacy.

Privacy by design: We do not retain original audio files on our servers after processing. Audio sent for embedding extraction is discarded after processing.

bigcut workspace
ReferenceRankingFinal CutRelease Prep

References

Palette

Track_A.mp3
Track_B.mp3
Track_C.mp3
1,247 songs indexed

Search

Similarity ranking

1Summer_Vibe_Demo.mp396.2%
2Midnight_Groove.mp391.8%
3Neon_Lights_v2.mp387.4%
4Golden_Hour.mp382.1%

Final Cut

Decision

Summer_Vibe_Demo.mp3

submitted by user

Release Prep

Ready facts

Credits3 writers
SplitsReady
Curation noteAttached

Plain-language map

Product terms in plain language.

Shortlist, Final Cut, Release Prep, and Proof each map to a simple step in the workflow.

Shortlist

A temporary list of candidate songs worth hearing again.

Final Cut

The moment a candidate becomes a deliberate selection.

Shortlist Submission

A record that says this song was selected at this point in time.

Release Prep

The final workspace for release metadata, embeddings, and curation context.

Proof

A way to verify later that the submitted choice was not inserted or changed after the fact.

Connected workflow

From reference search to verifiable recommendation data.

The important product story is not one feature in isolation. It is the full path from discovery to a trusted signal that an AI agent can evaluate.

01

Search by reference

Drop reference tracks and rank your catalog by acoustic similarity.

02

Build the shortlist

Compare candidates, listen again, and keep the songs that deserve a closer pass.

03

Submit Final Cut

Only selected songs the user explicitly submits become verifiable submission records.

04

Prepare the release

Release Prep connects final embeddings, metadata, and curation context.

05

Serve trusted signals

AI agents can receive high-quality recommendation context backed by proof.

Proof without exposure

Blockchain anchoring is used to prove timing and integrity. Original user IDs, song IDs, audio, notes, and embeddings are not revealed on-chain.

Proof & Privacy

AI Reference Matching

Drop a reference. Find your match.

Our proprietary AI engine analyzes multiple dimensions of audio simultaneously — rhythm, harmony, texture, energy, and more. It ranks your entire catalog by similarity to your reference songs. Works across every genre: K-pop, R&B, hip-hop, EDM, rock, jazz, classical, game OST.

  • Analyzes sonic character, not just tempo or key
  • Cross-genre matching capability
  • Results ranked by similarity score
  • Process thousands of songs in your catalog
bigcut workspace
ReferenceRankingFinal CutRelease Prep

References

Palette

Track_A.mp3
Track_B.mp3
Track_C.mp3
1,247 songs indexed

Search

Similarity ranking

1Summer_Vibe_Demo.mp396.2%
2Midnight_Groove.mp391.8%
3Neon_Lights_v2.mp387.4%
4Golden_Hour.mp382.1%

Final Cut

Decision

Summer_Vibe_Demo.mp3

submitted by user

Release Prep

Ready facts

Credits3 writers
SplitsReady
Curation noteAttached

Release Preparation

From demo to release — metadata handled.

When a track is ready for release, bigcut's Release Prep workspace lets you finalize every detail. Import metadata directly from the original demo — no retyping. Even when a song has more than ten writers, you can auto-organize each writer's share and the connected publisher share in one place, then keep song fees and usage terms inside the metadata. Export release-ready metadata to Excel, plain text, or Google Sheets in one click. Add curation notes that explain why the track matters, then choose when that release-prep package is ready to submit.

Writer and publisher split organization

Auto-organize each writer's share and connected publisher share at once, even when a song has 10+ writers.

Song fees and usage terms in metadata

Keep song fees and usage terms in the metadata, reducing repeated price checks and sensitive follow-up.

  • Finalize release metadata in a dedicated workspace
  • Import metadata from demo files — zero duplicate entry
  • Auto-organize writer and publisher splits at once, even with 10+ writers
  • Keep song fees and usage terms in metadata to reduce repeated price checks
  • Export to Excel, text, or Google Sheets instantly
  • Prepare curation notes for explicit submission when the track is ready

Release Prep

Metadata package

Title

Summer Vibe

Writers

3 confirmed

Publisher split

100% matched

Curation note

Ready

AI Agent Recommendation Hub

Verifiable song recommendation signals for the AI OS and agent era.

As AI operating systems and autonomous agents become a key layer for music discovery and recommendation, bigcut processes only the tracks users explicitly submit into high-quality recommendation signals from Release Prep embeddings and curation data. Just as one expert opinion can carry more weight than many casual reactions, selection records from label A&R teams can become a trusted signal when they are preserved as tamper-resistant blockchain-based proof. Shortlist submissions are connected to the final song data submitted from Release Prep, creating verifiable context for why a song was chosen and when it was submitted. On-chain records are used as a verification layer that proves submissions were not later manipulated, not as a place to reveal the underlying content.

  • Only tracks explicitly submitted by the user are used for recommendation data
  • Shortlist submission records are preserved through blockchain-based proof
  • Final Release Prep embeddings and curation data are processed into recommendation signals
  • The story of why a song was selected is connected as verifiable data
  • Turns expert A&R selection records into trusted recommendation context for AI agents
  • Privacy-preserving on-chain proof: original user IDs, song IDs, audio, notes, and embeddings are not revealed on the blockchain
  • Gives AI agents a high-quality basis for trusted song recommendations
01

Final Cut

explicit step

02

Commitment

explicit step

03

Merkle batch

explicit step

04

Anchor

explicit step

05

Verified

private data hidden

Smart Embedded Files

Analysis travels with your music.

When bigcut analyzes a song, the results are saved directly inside the audio file itself. But it doesn't stop at analysis — every metadata edit you make in bigcut is written back to the file in real time. This means publishers can submit demos with all metadata already organized, and ownership splits are auto-formatted the moment you enter them. No spreadsheets, no back-and-forth. The file arrives ready.

  • Metadata edits saved directly to MP3 — the file is the source of truth
  • Publishers submit demos with clean, complete metadata from day one
  • Ownership splits auto-formatted on input — no manual paperwork
  • Supports MP3, WAV, FLAC with normal playback in any player
  • Zero re-analysis when sharing — less than 0.5% file size increase

Workspace

File-native context

01Audio file
02Comments
03Palette
04Review state

Timeline Comments

Your feedback, pinned to the moment.

Drop comments at any point on the timeline — every note is saved and carries over when you hand the file to a colleague. One click copies all comments in chronological order as plain text, making it effortless to communicate revisions with publishers and songwriters. Since comments are embedded in the file itself, collaboration travels wherever the MP3 goes.

  • Pin comments to any moment on the timeline
  • Comments inherited across handoffs — no context lost between colleagues
  • One-click export: all notes copied as chronological text for instant communication
  • Comments embedded in the MP3 — share the file, share the conversation

Workspace

File-native context

01Audio file
02Comments
03Palette
04Review state

Mood Board & Palettes

Organize references visually.

Group your reference songs into palettes — collections that define a specific sound direction. Use the mood board to visualize and compare different sonic directions before committing to a search.

  • Create multiple reference palettes
  • Visual mood board overview
  • Switch between directions instantly
  • Save palettes across sessions

Workspace

File-native context

01Audio file
02Comments
03Palette
04Review state

Library Management

Your catalog, fully organized.

Import your demo library and manage everything in one place. Auto-read metadata from audio files. Edit ID3 tags directly within the app — changes write back to the files. Save and restore your entire workspace with session files.

  • Auto-read ID3/FLAC metadata
  • Edit tags directly (writes back to file)
  • Session-based workspace (save/restore)
  • Batch import with drag-and-drop

Workspace

File-native context

01Audio file
02Comments
03Palette
04Review state

Video Screening

Every visual, reviewed in one place.

Music doesn't live in audio alone. With Screening, you can monitor and review every visual asset tied to your artists — music videos, promotional clips, event footage, and more. Drop timestamped comments directly on the video timeline, just like you do with audio. Keep all creative feedback centralized so nothing falls through the cracks.

  • Review music videos, promo clips, and event footage in-app
  • Timestamped comments on the video timeline
  • Centralized feedback — one place for all visual assets
  • Seamless workflow alongside audio screening

Workspace

File-native context

01Audio file
02Comments
03Palette
04Review state

Readytotryit?

Create a free account and test the desktop demo with 30 free analyses granted once at signup.

Download