How do you know a dashboard is stale before your team opens it?
A dashboard freshness AI Employee that runs early each morning, checks the data behind your dashboards against the freshness each one is supposed to have, and posts one alert listing the stale ones before the team opens them. Read only: it checks timestamps, never touches a dashboard.
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A stale dashboard is more dangerous than a broken one. A broken dashboard shows an error and people stop trusting it; a stale one shows yesterday's numbers, or last week's, looking exactly as authoritative as always. Someone opens it in the morning, reads a figure that hasn't refreshed, and makes a call on it, and there's nothing on the screen to say the underlying data never loaded. Freshness isn't one number, either. A daily revenue dashboard is stale if its data is a day behind; an hourly ops board is stale after a couple of hours. Knowing whether each dashboard is current means checking the last load time of the data behind it against the freshness that dashboard is actually supposed to have, and doing it before the team arrives, not after someone has already acted on an old number.
Early each morning the dashboard freshness AI Employee reads each dashboard's underlying data from the warehouse read only, checks its last load time against the freshness threshold you set for that dashboard, and posts one alert listing every dashboard that's stale, with how far behind each one is. It reads timestamps only. It never changes a dashboard or its data.
See exactly how the work gets done.
Runs before the team arrives
A scheduled run fires early each morning, ahead of the workday, so any stale dashboard is flagged before someone opens it and acts on an old number.
Carries each dashboard's freshness rule
How current each dashboard is supposed to be travels with it as a skill: a daily board is stale after a day, an hourly one after a couple of hours. It checks each against its own threshold, not one blanket rule.
Reads the BI tool and warehouse, with permissions you set
It reads each dashboard's data sources from the BI tool and the last load times from the warehouse, with read only roles you scope. Credentials stay in memory, injected at run time, never exposed to the model.
Read only, no exceptions
It only reads metadata and timestamps. It has no permission to refresh, edit, or touch a dashboard, a data source, or a warehouse table. Its single output is the alert.
Posts one alert
Each morning brings one post: the dashboards that are stale and how far behind each one's data is. If everything is fresh, the team knows that too. They read it and decide what to trust.
Runs in your environment
Every run is isolated inside your own infrastructure, reaching only the BI tool, the warehouse, and the channel it posts to. Your data never leaves.
Read only, no exceptions
It reads timestamps and metadata only. It never refreshes, edits, or alters a dashboard, a data source, or a warehouse table.
Permissions you set
The BI tool and warehouse credentials are read only roles that stay in memory, injected at run time, never exposed to the model or written to logs.
One alert, before the day starts
It posts a single morning alert and nothing else. No dashboard is changed on the strength of it; a person reads it and decides.
You own the rules
The freshness threshold for each dashboard and which ones it checks are yours, versioned and changed on your terms, not in a vendor dashboard.
The stale dashboard that someone used to discover by acting on an old number is now flagged before the workday starts, named alongside how far behind its data is. The AI Employee checks the freshness; your team decides what to trust.
Every morning
Dashboards checked for stale data before the team opens them
Read only
Timestamps checked, no dashboard or data ever touched
One alert
Every stale dashboard named, with how far behind it is
Our free AI audit shows you where AI fits, what your security risks are, and gets your first AI employee working.
How does YP have your weekly metrics report ready before Monday?
Queries your metrics on a weekly schedule, writes commentary on what moved, and posts the report to Slack before Monday. Read only, it never writes to the database.
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How do AI Employees catch warehouse problems within the hour?
Checks every monitored table hourly for freshness, row count anomalies, and schema drift, posts an alert to Slack, and drafts a GitHub issue with the likely cause. It writes nothing back to the warehouse.
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How do you answer a warehouse question from Slack?
Turns a plain language question in Slack into a warehouse query, runs it read only, and replies with the number and the SQL it ran, so anyone can check the work.
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