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Multi-LLM Ensemble Deliberation

Unlike single-model mastering services, WhitePrint uses three independent AI models that analyze your track and vote on optimal parameters. You see every recommendation and the reasoning behind the consensus.

OpenAI

GPT-series model providing parameter recommendations with detailed rationale.

Anthropic

Claude-series model offering alternative perspective on mastering decisions.

Google

Gemini-series model contributing third independent analysis for ensemble voting.

Ensemble Voting Process

1

Independent Analysis

Each model receives the BS.1770-4 analysis data and independently determines mastering parameters.

2

Consensus Voting

Parameters are compared and consensus values are computed with confidence scores reflecting agreement level.

3

Transparent Output

You see each model's individual recommendations, the final consensus, and rationale for every decision.

Deliberation Output

Gain staging recommendations
Multi-band EQ curves (5-8 bands)
Compression ratio, threshold, attack, release
Limiter ceiling and release
Saturation amount and character
Stereo width adjustments
Per-section dynamic mastering
Confidence scores per parameter