How do you catch broken data models before the dashboards are wrong?
A model triage AI Employee that checks your transformation builds every hour: when a model fails, it reads the error, traces the cause, drafts a fix as a pull request, and alerts the team. Alert plus draft PR: a named human reviews and merges; it never merges on its own.
×
A dashboard rarely breaks by showing an error. It breaks by showing a number, a wrong number, because a transformation model upstream failed or silently changed its output, and nobody noticed until someone asked why revenue dropped. A renamed source column, a type that stopped casting, a test that started failing: the build breaks, the model goes stale or wrong, and everything downstream keeps rendering as if nothing happened. This is a narrower problem than general warehouse or pipeline health. The pipeline can be running fine and the warehouse fully up while a single transformation model is quietly producing garbage. Catching it means watching the model builds themselves, reading the actual failure, and knowing which model it is and which downstream dashboards it feeds. And the fix usually isn't a mystery; it's a small, mechanical change that someone has to notice, diagnose, and write before the wrong numbers spread.
Each hour the model triage AI Employee checks your transformation build results. When a model fails, it reads the error and the model's definition from Git, traces the likely cause, and opens a draft pull request with a proposed fix, then posts an alert naming the failed model and the dashboards it feeds. A named human reviews and merges; it never merges on its own.
See exactly how the work gets done.
Runs hourly against build results
A scheduled run fires every hour and reads the latest transformation build results, so a failed model is caught within the hour instead of when someone spots a wrong dashboard.
Triages to a root cause
For a failed model it reads the build error and the model's SQL from Git, and traces the failure to a cause, whether a renamed source column, a broken reference, or a failing test, rather than just reporting that something is red.
Reads Git and the warehouse, with permissions you set
It reads model definitions from the repository and build metadata from the warehouse with roles you scope. Credentials stay in memory, injected at run time, never exposed to the model.
Drafts a fix as a pull request
It writes the proposed change onto a new branch and opens a draft PR against your repo, with the diff, the failed model, and its reasoning in the description. It has no permission to push to your default branch or merge.
Alerts with the blast radius
It posts one alert per failure naming the model, the proposed PR, and the downstream dashboards that model feeds, so the team knows what's at risk before anyone opens them.
A named human merges
The draft PR waits for a reviewer. A named engineer reads the diff, approves or edits, and merges. Nothing reaches your models without that signature.
Runs in your environment
Every run is isolated inside your own infrastructure, reaching only the warehouse, the repository, and the channel it posts to. Your data and code never leave.
Never merges
It can open a draft pull request on a new branch and nothing more. It has no permission to push to your default branch or merge its own change.
A named human signs off
Every proposed fix waits for a named reviewer to read the diff and merge. The irreversible step is always a person's signature, never the AI Employee's.
Permissions you set
The Git and warehouse credentials are scoped roles that stay in memory, injected at run time, never exposed to the model or written to logs.
You own the rules
Which models it watches, how it triages, and what a fix may touch are yours, versioned and changed on your terms, not in a vendor dashboard.
The broken model that used to surface as a wrong dashboard days later now surfaces within the hour, triaged to a cause with a fix already drafted and the blast radius named. The AI Employee catches it and proposes the fix; a named engineer reviews and merges.
Every hour
Transformation builds triaged before wrong numbers spread
Draft PR
A proposed fix, never a merge. A human decides
Per failure
One alert naming the model and the dashboards it feeds
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.
×
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.
×
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.
×