ML Model Profit Bias Corrector

A tool that fixes the over-optimism bias in ML models trained on historical business decisions (like lending or wholesale). Many models fail in production because they're trained on cherry-picked historical deals rather than true market data.

Survivorship bias in ML training data is a documented, well-understood failure mode in credit scoring, wholesale buying, and insurance underwriting, and regulatory pressure around model fairness and explainability (EU AI Act, US fair lending guidelines) is forcing more scrutiny on exactly how training sets are constructed. No clear incumbent owns this specific corrective tooling layer, though MLflow and some AutoML platforms touch adjacent validation concerns without solving the root data selection problem. The $5k–20k/month revenue band is plausible only if buyers are enterprise risk or data science teams with real P&L exposure to bad models — a hard sale to land without deep domain credibility in a specific vertical like fintech or commodities. The most likely failure mode is that the problem gets absorbed internally by data engineering teams or delegated to general-purpose data validation tools, leaving the addressable market too thin to sustain a standalone product.