Make AI dashboards traceable to source data
An AI-generated dashboard can look complete while answering the wrong question. A useful dashboard exposes where the numbers came from, which records count, and whether the data is fully available.
Show the meaning of each number
A count of customers is ambiguous without the source table, filter, and grouping rule. A monthly total needs a time field and a defined window. Separate source facts from model-derived categories, and make it possible to inspect representative records behind a total. Partial coverage should remain visible rather than appearing as a final result.
- 1Name the source, filter, group key, and…Name the source, filter, group key, and time field.
- 2Expose freshness and coverage alongside the displayed…Expose freshness and coverage alongside the displayed value.
- 3Reconcile a sample total against the underlying…Reconcile a sample total against the underlying records.
Try it on a small example
- Name the source, filter, group key, and time field.
- Expose freshness and coverage alongside the displayed value.
- Reconcile a sample total against the underlying records.
What to verify
Baleybots live reports expose coverage states including filling and stale, and report windows use defined time boundaries. Source-change events are signals to reread data, not proof that a displayed filtered value changed. When building a board, preserve those distinctions in the reader's view. A generated title should not imply a stronger conclusion than the source and aggregation actually support.
Product details checked against the Public API reference on October 4, 2026. These guides describe documented behavior; availability depends on your account and the deployed service. Baleybots is in invite-only beta.