What Analytics Model is
A conversational analytics layer. Connect your data sources, then ask questions in ordinary language and get answers, charts and insights back instead of writing queries or waiting on a report.
It is also embeddable, so product teams can put the same capability inside their own application.
What you can do with it
- Ask for a metric, comparison or breakdown and get a visualisation
- Set smart alerts when something moves
- Give different roles different views of the same data
- Embed analytics into a product or internal platform through the API
Who it is for
- Business analysts and management reporting
- Product teams embedding analytics
- Non-technical users who want direct access to numbers
What to watch out for
- Natural language analytics is only as trustworthy as the metric definitions behind it
- Agree calibres, handle sensitive fields and audit key metrics before anyone acts on output
- A fluent explanation of wrong data is worse than no answer
- Complex metrics still need a data team to define them, and access control has to be set deliberately
Pros & cons
✓ What we like
- No SQL needed for common questions
- Broad data source connectivity
- Alerts and role-based views
- Embeddable into products via API
! What to watch out for
- Depends entirely on metric definitions and data quality
- Complex metrics still need data team involvement
FAQ
Do I need SQL?
Not for routine questions, though complex metrics still need a data team to define how they are calculated.
Can it be embedded?
Yes. There is an embedded API for putting analytics into a product or internal platform.
Can insights drive decisions directly?
Use them as reference. Check data sources, time frames, definitions and outliers before acting on anything material.
Last reviewed: 2026-09-15
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