How do AI Employees catch warehouse problems within the hour?
A pipeline health AI Employee connected to your warehouse and GitHub. Every hour it checks table freshness, row count anomalies, and schema drift, alerts Slack, and drafts a GitHub issue with the likely cause. It never touches the data.
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Warehouse health doesn't announce itself. A load job can fail without an exception if it's built to skip a bad batch and move on. A table can go stale because an upstream schedule got disabled, not because anything crashed. A schema can drift because a source API added a field, and the load still 'succeeds' but just drops or nulls what it doesn't recognize. Every one of these looks fine from the outside until a downstream query returns something wrong, and usually the first person to notice is whoever's report looks off in a stakeholder meeting. The common approaches don't hold up. Dashboards built on the data can't tell you the data itself is broken. They render whatever's there, wrong or not. A 'did the job run' check confirms the process exited zero, not that the table it wrote actually looks right. Someone eyeballing row counts catches it eventually, usually after a downstream report has already gone out wrong. This is a different failure mode from an application throwing an error mid request: the pipeline can succeed and the warehouse can still be unhealthy.
Every hour the pipeline health AI Employee reads the warehouse read only and checks every monitored table against its freshness SLA, compares today's row count to the table's own trailing baseline, diffs the current schema against the last seen shape, and looks for loads that failed or silently stalled. When it finds something out of bounds, it posts an alert to the data team's Slack channel with the affected table, the anomaly, and the most likely cause, and drafts a GitHub issue with the same diagnosis so the incident is tracked. It never writes to the warehouse. The alert and the drafted issue are its only outputs.
See exactly how the work gets done.
Runs hourly, from current state
A scheduled run fires once an hour and recomputes every check from the warehouse's current state. Nothing carries over between runs, so freshness, row counts, and schema are always measured against what's live.
Carries your health rules
What counts as stale, what a normal row count trend looks like, and the shape each schema is expected to hold travel with it as skills and memory: per table freshness SLAs, the tables that grow in bursts versus steadily, and past incidents worth checking new anomalies against. Tighten an SLA or note a legitimate weekly gap and the checks adjust.
Reads the warehouse, with permissions you set
It reads table freshness (the most recent load timestamp per table, checked against its SLA), row counts (today's count against the table's trailing baseline), schema state (the current columns and types, diffed against the last seen shape), and load history (recent job runs, to catch a failed or stalled load). Credentials stay in memory, never written to disk, never exposed to the model.
Alerts and drafts, never fixes
When a check comes back out of bounds it posts to Slack with the table, the anomaly, and its best guess at the cause, and drafts a GitHub issue with the same diagnosis so the incident lands as a trackable draft, not a merged change. It does not touch the pipeline, the schema, or the data.
Read only, no exceptions
It has no write access to any table, schema, or pipeline configuration. It can only observe and report. Its outputs are exactly two: the Slack alert and the drafted GitHub issue. The data team decides what to fix; nothing changes on its own.
Runs in your environment
Every run is isolated inside your own infrastructure, reaching only the warehouse it's scoped to; your data never leaves it, and only the Slack alert and the drafted issue leave the run.
Permissions you set
The warehouse credential and the GitHub token connect with permissions you set, held in memory, injected at run time, never written to disk or exposed to the model.
Read only, no exceptions
The connection into the warehouse is read only. It cannot modify a row, alter a schema, or touch a pipeline. It can only alert and draft.
Draft, not decide
The GitHub issue is a draft with a diagnosis attached, never closed or assigned automatically. A named human owns the incident from there.
You own the rules
The SLAs, the baselines, and the per system permissions are yours, versioned and changed on your terms, not in a vendor dashboard.
Warehouse problems that used to surface as a wrong number in someone's dashboard now show up as an hourly health check with a specific table, a specific anomaly, and a likely cause, already in Slack and already drafted as an issue. The AI Employee reads and reports; the data team decides what to fix.
Every hour
Freshness, row counts, and schema rechecked against the live warehouse
Read only
Nothing written to any table, schema, or pipeline
2 outputs
A Slack alert and a drafted GitHub issue, nothing else
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 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.
×How do you catch broken data models before the dashboards are wrong?
Watches transformation model builds hourly, triages the failure to a root cause, opens a draft PR with a proposed fix, and alerts the team, so a broken model gets caught before it feeds a wrong dashboard, and a human still merges.
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