Strawberry

Spend less time translating requests and more time improving the data work behind them.

Strawberry helps data teams collect the business question, source context, definitions, examples, and supporting files that a useful request actually needs. It can prepare a clear intake, documentation draft, or analysis brief across the browser and connected tools, while data teams retain control over access, modeling, validation, and production changes.

A data request needs a decision and a definition before it needs SQL.

“Can you pull retention?” leaves out the cohort, event, time horizon, segment, and business decision. The resulting query may be technically correct and still fail the stakeholder who asked for it.

Strawberry can capture those missing questions in a structured intake and bring together the relevant source links, screenshots, and examples. That gives the data team a request they can estimate and validate rather than a vague message to decode.

The metric definition belongs beside every decision that uses it.

A shared metric can drift when teams change filters, grain, source logic, or time zone without a visible record. The result is not merely confusion; it is decisions made from numbers that look identical but mean different things.

Use Strawberry to prepare a metric-definition page from the approved materials, including owner, source, calculation assumptions, exclusions, refresh behavior, and known limitations. A data owner should verify it before it becomes canonical documentation.

Validation should explain why a number is trustworthy enough to use.

A dashboard value is not self-validating.

Teams need to know the freshness of the data, expected ranges, reconciliation checks, source changes, and whether an unusual movement has been investigated.

Strawberry can prepare a validation checklist and collect relevant context from the run, documentation, and browser-based investigation. It should surface failed checks clearly instead of treating a completed report as proof of quality.

Recurring data work benefits from a durable intake contract.

A saved skill can preserve the questions, validation steps, and output format for a recurring metrics brief or stakeholder request. A routine can gather the inputs and prepare the next review cycle without losing the definition every month.

It must not change schemas, edit production data, publish a report, or expand access by itself. Those actions need the team’s established engineering and governance controls.

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Frequently asked questions

AI can structure data requests, prepare metric documentation, organize validation context, and draft recurring reporting workflows for review.

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