Dashboards that explain themselves,
in plain language, before you ask
A dashboard answers what happened; it rarely answers why, and the person who could explain the dip in last week's numbers is busy doing something else. A BI and dashboard agent keeps your dashboards current and adds the missing layer: a short, plain-language note on what moved, why, and whether it is worth a closer look, sourced from the same warehouse the dashboard already pulls from, never a guess.
The role today
A dashboard shows that revenue dropped eight percent last week, and that fact alone does not tell anyone what to do about it. Finding the why means someone pulling up ad spend, checking for a stockout, cross-referencing a competitor’s promotion, work that takes real time and usually falls to whoever happens to notice the dip first.
The second cost is that most dashboards get checked only when someone remembers to, which means a genuine anomaly, a metric moving well outside its normal range, can sit unnoticed for days until it shows up in a monthly report instead of a same-week alert.
The third is that building the habit of writing weekly commentary by hand rarely survives contact with a busy week. It gets done thoroughly for a month, then skipped, then the dashboard goes back to being numbers without context.
What the agent takes over
The agent reads your warehouse on the schedule you set and writes a short, plain-language note on what changed: which metrics moved, by how much, and what in the data correlates with the move, a drop in traffic from one channel, a price change, a stockout. It flags anomalies, a metric outside its normal range, the moment they appear rather than waiting for a scheduled report, the same discipline behind one of our monitors catching 184 of 218 real price-undercut events with zero false alarms.
It also answers simple follow-up questions about a chart in plain language, “why did this spike,” pulling the explanation from the same data rather than guessing.
Typical scope: recurring commentary on existing dashboards, anomaly flags, and simple ad hoc questions about what is already in the warehouse. Building the warehouse itself, if it does not exist yet, is a separate step handled by a data engineering and ETL agent.
What stays with humans
Deciding which metrics actually matter to your business, and what to do about a flagged anomaly, stays with your team. The agent explains what the data shows; acting on it is a business decision informed by more than the dashboard.
Guards
Every number is pulled live from the warehouse, never approximated or remembered from a previous run. Access is read-only, the agent cannot alter a dashboard’s structure or the data behind it. Commentary is always clearly labeled as agent-written, so a reader knows it is a starting point to verify, not a final word.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Agency runs it | from $2,200 + support plan | Agent built, tuned and supervised by us, monthly commentary quality review | 2 to 3 weeks |
| Full control, handover-ready | from $3,700 | Same agent on your own warehouse and dashboard tool, your team runs it | 3 to 4 weeks |
Running cost is usually $15 to $60 a month in model and warehouse query usage.
Related
See the analytics service page and AI agents service page for the surrounding build. Within this group: SQL analyst agent answers ad hoc questions the same way, and data engineering and ETL agent builds the warehouse this agent reads from. For a related one-time setup, see automate dashboard commentary. Real commentary discipline behind this page: the two-brand analytics hub case study, where an AI analyst answers business questions with real SQL instead of guesses.
Dashboard nobody checks until month end? Get in touch and we will look at what you already track first.
FAQ
How much does a BI and dashboard agent cost?
From $2,200 to wire into one existing warehouse and dashboard tool, live in 2 to 3 weeks. A build that includes setting up the warehouse itself usually runs alongside our data engineering and ETL agent, from $5,000 combined.
How long before it is writing real commentary?
2 to 3 weeks: it needs a few weeks of your actual metric history to learn what a normal range looks like before its anomaly flags and commentary are trustworthy.
Which tools does it work with?
Your existing warehouse (PostgreSQL or similar) and dashboard tool (Looker, Metabase, a custom dashboard, or similar), delivered as commentary attached to the dashboard or sent to Telegram or Slack on a schedule.
What if its commentary is wrong or misleading?
Every number it cites is pulled live from the warehouse, not estimated, so a wrong commentary is a reasoning error, not a fabricated figure, and it is always clearly labeled as agent-written so a reader knows to check before acting on it.
Does it have write access to our data or dashboards?
No. It has read-only access to the warehouse and writes only commentary text, never a figure in the underlying data. It cannot alter a dashboard's structure or the numbers behind it.