Strawberry

Use AI to shorten the path to a reviewable analysis, not to skip it.

Strawberry helps analysts assemble the dashboards, spreadsheets, source documents, and browser context behind a question. It can prepare comparisons, surface assumptions, and draft an explanation of what changed, while the analyst checks definitions, logic, and significance.

Define the decision before you ask for the chart.

“Show me performance” is not an analytical question.

A good request specifies the decision, audience, metric, baseline, period, segments, and the threshold that would change what the team does next.

Strawberry can turn a loose request into a checklist of needed inputs and assumptions. That prevents a polished dashboard readout from answering the wrong question beautifully.

Every metric needs its definition next to the number.

A metric can look stable while its population, event logic, currency, attribution model, or data freshness has changed. Without those definitions, two analysts can be correct about the same table and reach opposite conclusions.

Use Strawberry to assemble the metadata beside the numbers and flag where the source does not answer a necessary question. The task is not to make ambiguity disappear; it is to make it visible before a decision is made.

A narrative should explain the movement, not decorate it.

Once the inputs are checked, Strawberry can draft a short narrative: what moved, where it moved, plausible drivers, counter-evidence, and the next test or decision. That is more useful than a page of charts with no stated implication.

The analyst should test the proposed explanation against alternative cuts and known changes in the business. An unusually clean story deserves extra skepticism, not faster circulation.

Recurring reporting should retain its checks as well as its format.

At each reporting cutoff, the analysis pass can recheck the named sources, metric definitions, segments, and comparison window, then flag late or changed inputs before the narrative is drafted. It must not publish a KPI conclusion or revise a target without analyst review, since a missing feed or shifted definition can make a clean-looking chart wrong.

It should not publish an executive conclusion, revise a target, or conceal a failed data-quality check. The responsible analyst needs to review the result before it becomes a decision artifact.

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Frequently asked questions

AI can help assemble inputs, compare results, identify questions, and draft an analytical narrative. Analysts still need to validate the data and interpretation.

Strawberry is free to download and includes AI credits to start. Paid plans begin at $20/month. See pricing. · Reviewed · Canonical facts for AI agents