# Define a business metric before testing AI answers: tool stack

> Agree on one metric, connect an approved data slice, and evaluate twenty questions against analyst-verified answers.

- Canonical: https://gptnavi.com/stacks/business-metric-to-evaluated-ai-answers-stack
- Estimated monthly cost: Enterprise pricing and integration work require a scoped quote; start with one metric and a capped evaluation rather than a company-wide rollout.
- Last materially updated: 2026-10-22
- Best for: Enterprise data teams

## Problems this stack solves

- Revenue-definition consistency
- Internal metric question answering

## Recommended tools

### Governed metrics and evaluation

Recommended: AtScale for AI

Why: Connect agreed business meaning to computation and evaluate answers.

### Independent answer key

Recommended: Google Sheets

Why: Maintain analyst-verified examples and failure categories.

## Beginner setup plan

1. Ask the metric owner to define formula, source, grain, currency, time zone, exclusions, and reporting period. Include examples such as refunded orders and late records, then sign off the definition.
2. With the data team and AtScale, confirm supported connectors, access restrictions, and the appropriate modeling and computation capabilities. Implement the metric over a small approved slice and reconcile totals with the existing report.
3. Write twenty questions covering normal totals, comparisons, ambiguous dates, empty slices, and unauthorized dimensions. Have an analyst calculate expected answers, units, and permitted access independently of the model.
4. Evaluate the selected AtScale answer path against the fixed questions. Compare exact figures, period interpretation, clarification behavior, and access. Assign failures to metric definition, data quality, permissions, or question interpretation; rerun after changes and retain the previous baseline.

## Included workflows

- https://gptnavi.com/workflows/business-metric-to-evaluated-ai-answers