VIP Milestone 12: Build Your EP Architecture, Version and Release-Control System

Gary Whittaker

AI Artist Academy · Milestone 12 · VIP Build

Build Your EP Architecture, Version and Release-Control System

Move beyond a one-time track list. Build the authority, evidence and change controls needed to keep the EP coherent from candidate review through release and future editions.

System outcome

The completed system produces an approved EP Architecture and Release-Control Record, a canonical asset register, a rights-and-contribution evidence map, a defect log, release authority and a bounded handoff into Milestone 13.

What AI may support

Candidate comparison, theme clustering, sequence alternatives, inconsistency detection, checklist drafting and risk questions.

What AI cannot decide

Copyright ownership, permission sufficiency, legal risk, factual contributor claims, platform compliance, final artistic authority or whether evidence is genuine.

Core Squared control

Govern the EP through five operating lenses

Lens Required control
Flame Purpose, intended listener and the human reason this project deserves to exist.
Rock Stable project thesis, artist signals, four track roles and boundaries.
Cycle Candidate intake, comparison, sequence testing, revision, approval and release handoff.
House Canonical files, metadata, evidence, artwork, rights records, backups and access.
Operator Named authority for track selection, master approval, credits, release and incident decisions.

1. Establish authority before comparison

  • Name the project owner and final creative approver.
  • Identify who may replace masters, alter sequence, change titles or approve artwork.
  • Record where authoritative files and records live.
  • Set the evidence date after which late changes require formal review.
  • Separate suggestions from approved decisions.

2. Build the candidate evidence register

For every candidate, record source, generation or production history, human contributions, known collaborators, master status, release history, performance evidence, artist fit and proposed role. Mark every claim as verified, unverified or not applicable.

Do not use engagement numbers as proof of artistic quality. Do not treat absence of objections as permission. Escalate rights, contract and platform questions to the appropriate human source.

3. Use a weighted track-role scorecard

Create weights before scoring to prevent preference from silently changing the rules. Suggested dimensions include artist fit, unique project job, listener movement, production readiness, rights-record completeness and transition value. Document all overrides.

AI AUDIT 1 — ROLE DUPLICATION Review the candidate summaries and proposed roles. Identify where two tracks perform substantially the same project job. Explain the evidence, the uncertainty and what a human should test before choosing. Do not make legal, ownership or final creative claims.

4. Govern sequence experiments

Give each sequence an ID, date, track order, test context and reviewer notes. Compare opening clarity, momentum, contrast, transition defects, emotional movement and ending strength. Keep rejected sequences so later decisions can be traced.

AI AUDIT 2 — SEQUENCE STRESS TEST Compare three proposed four-track sequences against the written listener promise. Flag weak openings, duplicated energy, abrupt transitions and unresolved endings. Separate observations from hypotheses and list the listening tests a human must perform.

5. Control canonical and derivative versions

Every approved track needs one canonical master ID. Record derivative relationships for instrumentals, clean versions, remixes, stems and alternate masters. No replacement becomes authoritative until the project owner approves it and updates every affected record.

Control Minimum record
Master authority Track, version ID, filename, date, checksum or other verification where available, approver.
Metadata authority Approved title, artist, contributors, sequence, edition and source of truth.
Derivative relationship Parent master, derivative type, permitted use and status.
Replacement Reason, impact, approval, affected platforms and rollback file.

6. Build the contribution and rights evidence map

Record what each person and tool contributed, what permissions or agreements exist, what remains uncertain and who must verify it. This system is an evidence organizer, not legal advice or a declaration that an AI-assisted output qualifies for copyright protection.

AI AUDIT 3 — EVIDENCE GAPS Using only the supplied contribution and permission records, list missing evidence, conflicting claims and questions requiring human or professional review. Do not infer consent, ownership or legal status.

7. Set quality gates and defect classes

Blocker

Missing master, unresolved permission, incorrect identity, broken file or issue that prevents responsible release.

Major

Sequence, metadata, mix, artwork or continuity issue that materially weakens the project.

Minor

Non-blocking issue with a named owner and correction date.

Accepted exception

Documented deviation approved by the authorized operator with rationale and risk.

8. Approve the release package and handoff

The final gate confirms four canonical masters, sequence, project identity, metadata, artwork, credits, evidence, backups, release owner, verification plan and rollback assets. The Milestone 13 handoff should state what the extended edition may add, what it must not repair and which canonical elements cannot change without approval.

Advanced AI audit set

AI AUDIT 4 — ARTIST CONTINUITY Compare the four approved track records with the Artist Identity Record and signature-sound rules. Flag deviations and state whether each appears intentional, unexplained or unsupported.
AI AUDIT 5 — LISTENER PROMISE Test whether each track and the final sequence materially support the written listener movement. Identify any claim that lacks evidence.
AI AUDIT 6 — METADATA CONSISTENCY Compare titles, artist names, contributor names, sequence numbers, edition labels and durations across the supplied records. Return discrepancies only.
AI AUDIT 7 — LATE-CHANGE RISK For each proposed late change, list affected files, records, artwork, credits, distribution fields, campaign assets and rollback needs.
AI AUDIT 8 — RELEASE READINESS Audit the checklist for missing evidence and unverified assumptions. Do not state that platform, legal or rights requirements are satisfied.

Final approval record

  • Project owner and final approvers are named.
  • The thesis and track-role architecture are frozen.
  • Sequence tests and rejected alternatives are retained.
  • Canonical masters and derivative relationships are recorded.
  • Metadata and contributor evidence have human verification.
  • All blockers are closed or the release is paused.
  • Backup and rollback materials exist.
  • Milestone 13 boundaries are explicit.

Decision options: approve, revise, pause or escalate for verification.

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