Run automated data quality checks and alert your team to problems

Checks your database against rules you define, scores data health, and alerts your team only when something's actually wrong.

How the work actually flows

It branches. Exactly one path is taken; runs once per each data quality rule.

Pattern: Sequence (1) · Exclusive Choice (4)

flowchart TD trig(["scheduled data quality check"]):::trigtime s0["read quality rules"]:::task s1[["run checks against database"]]:::mi s2["score data health"]:::task trig --> s0 s0 -->|"one per each data quality rule"| s1 s1 --> s2 gx{"× issues found"}:::gate s2 --> gx p00["write ai diagnosis"]:::task gx -->|"issues found"| p00 p01["alert team"]:::task p00 --> p01 p10["stay silent"]:::task gx -->|"no issues"| p10 p01 --> out p10 --> out out[/"data health score and diagnostic alert"/]:::out pay{{"catch bad data before it spreads"}}:::pay out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff

Starts itA stepRuns once per itemOne path onlyResultPayoff

Build size

Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions

Email AutomationMessaging & NotificationsSpreadsheet & Database OpsProject & Task Management

Connects

NotionPostgreSQLOpenAISlackGmailJira

The problem it solves

Catching bad or broken data before it spreads through your reports means someone has to keep manually checking tables and fields. Without a system watching constantly, data problems often surface only after they've already caused damage downstream.

Who it fits

Best suited for data, analytics, and platform teams responsible for keeping databases clean and reliable.

How it works

On a set schedule, it reads your data quality rules from Notion
It converts each rule into a live check against your database
It scores overall data health and flags any anomalies found
AI writes a root cause explanation and suggested fix for each issue
If anything is wrong, you get an alert with the diagnosis; otherwise it stays silent

What you get

Data problems flagged before they spread

Your team gets a clear health score and root-cause diagnosis whenever your data quality slips.

What you get

A data health score and, when needed, an alert with an AI-written diagnosis and fix recommendation.

What you need

A Notion account, a database such as PostgreSQL, an OpenAI account, and Slack or Gmail for alerts.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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Back to the AI Playbook

Run automated data quality checks and alert your team to problems

How the work actually flows

The problem it solves

Who it fits

How it works

Data problems flagged before they spread

What you get

What you need

We can build this. But should you?

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