AI-led professional critique
TapeCoach uses AI analysis to produce structured self-tape critique. The report is generated from supplied brief/context, selected performer level, observable video/audio evidence, role/material context where available, and internal report-quality checks.
The report is designed to help you decide whether to submit, retake or review carefully. It is not a promise of casting, callback, booking, job, employment, agent, school or audition outcome.
What the report can assess
- Observable performance, voice/singing, movement/dance, musical-theatre package, commercial/screen task and self-tape presentation where evidence is visible or audible.
- Supplied-brief requirements, upload instructions and role/material context where the source basis is clear.
- Selected performer-level readiness and score language where scores are visible.
- Professional 90+ competitive calibration where applicable.
What the report cannot safely assess
- Hidden casting preferences, callback likelihood, marketability, bookability or guaranteed outcome.
- Protected characteristics, body or appearance judgement, medical diagnosis or vocal-health diagnosis.
- Brief, deadline, upload, file naming, page/side or role-specific compliance where no brief/context was supplied.
- Anything that is not visible, audible, supplied or reliably inferred from the available evidence.
No-brief baseline mode
If no brief is supplied, the report can only assess baseline observable performance, setup and selected-level readiness. It cannot claim brief achievement, page/side compliance, role-specific fit, deadline compliance or submission-package completeness.
Repair, red-line and QA handling
TapeCoach may validate AI output, ask targeted repair questions for missing/thin/contradictory modules, and apply narrow red-line filtering for unsafe claims. Internal QA artefacts prove the report process and are not performer-facing critique.