Scan assumptions and model logic for soundness
Reads quantitative work and judges whether the model logic is coherent, segmentation is right, and implicit assumptions are reasonable and surfaced — flagging data gaps and silent assumptions.
In / outQuantitative model or analysis + data inputs → Flagged model logic issues, segmentation concerns, implicit assumptions, and data gaps with suggested surfacing or fixes
You might say…
“The numbers add up but I'm not sure the model is actually doing what they think it's doing — I need a read on the logic and the assumptions before I sign off on it.”
What it does
Read the quantitative work and judge whether the model logic is coherent, the segmentation is right, and the implicit assumptions are reasonable and made explicit — flagging data gaps and silent assumptions. Used when reviewing data-heavy work to catch errors of judgement that a numerical reconciliation alone would miss.
Trigger: Use when reviewing data-heavy analysis to catch judgement errors that numerical reconciliation alone would miss.
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