Give the CEO a reviewable answer in minutes.
"Why did ad revenue growth slow in March?"
What the agent must prove
✓Metric definition comes from dbt docs.
✓SQL runs locally against DuckDB in read-only mode.
✓Every claim cites a source query and caveat.
First, Boardroom Analyst checks whether the datamart is safe to reason over.
dbt projectDiscovery martvisual_discovery_boardroom_mart loaded
model grainmonth × surfacedocumented in metadata
warnings0ready for executive analysis
The agent plans claims against query IDs before writing the narrative.
q001_surface_revenue select month, surface, net_revenue_millions from ad_revenue_by_surface order by month, surface
q002_revenue_growth_waterfall
with totals as (...)
select month, total_revenue_millions,
revenue_change_millions
from changes
order by month
The evidence shows shopping and visual search grew while brand video dragged.
Jan
Feb
Mar
The output is executive-readable and audit-ready.
Total ad revenue increased from $925M to $973M, but monthly growth slowed from +$69M to +$48M.
source q002_revenue_growth_waterfall
Brand Video Ads declined by $14M while Shopping and Visual Search added $62M combined.
source q001_surface_revenue
Includes SQL appendix, result hashes, chart CSVs, and caveats.
Follow-up questions reuse the same governed context.
"Which surface should I ask the ads team about first?"
Brand Video Ads: -$14M in March, while shopping-intent surfaces kept compounding.
The answer stays tied to `q001_surface_revenue`, the documented grain, and the same caveats.