AI finds the structure
The analysis identifies dates, measures, categories, identifiers, business keys, time granularity, and useful KPIs from the evidence in the file.
blueberry. puts an AI analyst on your file — it investigates the whole thing in an isolated workspace: column names, values, data types, quality issues, and likely business meaning — before building a dashboard.
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The analysis identifies dates, measures, categories, identifiers, business keys, time granularity, and useful KPIs from the evidence in the file.
AI suggestions are checked against the full file with numeric and date parsing before the pipeline creates typed dashboard data.
When users adjust the AI suggestion, blueberry. can store scoped correction hints so future analyses become more useful without changing your private data rules.
An AI analyst opens the whole file in an isolated workspace and works through every column.
The AI proposes columns, roles, KPIs, measures, and categories.
Server checks the full dataset before creating the dashboard.