Open NaraOps
NaraOps / Business examples
Explore before you sign up

Choose a business. See the data. See what NaraOps could find.

Every example below uses fictional synthetic operational data. The terminology changes radically between businesses so you can see how NaraOps applies the same schema-aware intelligence across different operating models — from raw rows to metrics, management briefs and decisions.

Explore examplesRequest your industry
Don't see your industry?

Request a demo for your business type.

Tell us the industry and the kind of operational data you already use. We can turn useful requests into new fictional synthetic examples so you can see the kinds of metrics, management brief, patterns and decisions NaraOps could surface before you upload anything.

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No confidential data needed — describe the fields or reports in general terms.
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The example will use synthetic data, not your real business information.
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Good requests can become permanent examples for other businesses in that sector too.
Describe columns, reports or systems at a high level. Please do not paste customer, employee or commercially sensitive data.
About these examples: every organisation, person, transaction, appointment, project, job and result shown here is fictional synthetic data created to demonstrate the kinds of patterns NaraOps can analyse.

The point is the variety, not a fixed template.

A shop has products and sales. A salon has treatments and therapists. A recruitment agency has vacancies and candidate stages. A manufacturer has production runs and defects. NaraOps profiles the structure it receives, identifies useful measures and dimensions, and lets the user correct any interpretation.

Create a workspace when you are ready to use your own structured operational data.

One engine, very different businesses

The examples deliberately use different terminology and operating models.

Retail, beauty, recruitment, field service, logistics, support, manufacturing and other examples all use different field names. The point is to show NaraOps learning the structure of the data rather than expecting one industry template.

Field intelligenceIdentifiers, dimensions, dates and measures are inferred from names, values, cardinality and examples.
Editable relationshipsUsers can inspect and change how related tables connect before evidence is used.
Dynamic findingsUseful unfamiliar fields can contribute to Explore, Briefs and Ask NaraOps when the evidence supports it.