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Use case

Analytics

From a spreadsheet to a published dataset.

The gap between a file someone exported and a number a team will act on is mostly unglamorous work: reading the columns, deciding what to group by, agreeing what the measure means. That work is where the errors live, so it is the part worth making visible.

Analytics

What makes this hard

  • The shape of a file, what is groupable and what is really an identifier, has to be understood before anything can be charted.
  • Aggregations done in a spreadsheet are almost impossible to reproduce six months later.
  • The same question gets answered slightly differently by each person who answers it.

How we approach it

  • Profile first

    See every column's type, cardinality, nulls and range before deciding what the file can answer.

  • Deterministic aggregation

    Group-bys run server-side against the stored file, so the result is the same every time it is asked for.

  • Publish as a dataset

    Turn the summary into a named dataset that reports and stories read from, rather than a number pasted into a slide.

What you should expect

  • One definition of a measure, in one place, that everything else references.
  • A path from raw file to published figure that another person can retrace.

Where this shows up

Guido

The agentic orchestrator the other products run on.

Guido is the workspace where the agents, tools and data behind every Trazup product live. Discovery, analysis, planning and reporting sit in one place, with the same permissions, datasets and audit trail underneath.