Experiment design for future bets

Problem

Some of the most impactful potential profiler features — AI-generated column descriptions, automated data quality scoring, anomaly detection on profile drift — are high-risk bets where the value proposition is plausible but unproven. Building any of these to completion before validating the core hypothesis (e.g., "AI-generated descriptions are accurate enough that analysts trust and use them") would be a significant investment with uncertain return. Without pre-designed validation experiments — minimal prototypes, defined success criteria, and concrete evaluation protocols — the team has no way to cheaply test whether a future bet is worth pursuing, and decisions default to opinion rather than evidence.

Context

Possible Solutions

Plan

Implementation Progress

Review Feedback

• Review cleared