Clean and validate messy CSV uploads before they hit your database

AI checks every uploaded CSV for errors and formatting problems, then loads clean data into your database automatically.

How the work actually flows

It branches. Exactly one path is taken; runs once per each row in the file.

Pattern: Exclusive Choice (4) · Multiple Instances with a priori Design-Time Knowledge (13)

flowchart TD trig>"CSV file uploaded"]:::trig s0["detect and fix formatting"]:::task s1[["validate each row"]]:::mi s2[("save valid rows to database")]:::store trig --> s0 s0 -->|"one per each row in the file"| s1 gx{"× is the row valid"}:::gate s1 --> gx p00["save to database"]:::task gx -->|"valid rows"| p00 p10["log error and alert"]:::task gx -->|"invalid rows"| p10 p00 --> s2 p10 --> s2 out[/"clean records with error log"/]:::out pay{{"reliable data without manual review"}}:::pay s2 --> out out --> pay s1 -. "failures recorded, run continues" .-> out 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 itemA record or sheetOne path onlyResultPayoff
Build size
Advanced

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

Business functions
AI Agents & Autonomous SystemsMessaging & NotificationsDocument Processing & OCRSpreadsheet & Database Ops
Connects
OpenAIAnthropicPostgresSlackGoogle Sheets

The problem it solves

Bad spreadsheet data breaks reports and dashboards downstream, but nobody has time to eyeball every upload for typos, missing fields, or wrong formats. By the time you notice, the bad data is already causing problems elsewhere.

Who it fits

An operations or data team that regularly receives CSV files from customers, partners, or other systems.

How it works

  1. A CSV file is uploaded for processing
  2. AI reviews the data to detect column types and fix inconsistent formatting
  3. Each row is checked for missing values, wrong types, and outliers
  4. Clean data is saved into your database automatically
  5. Errors are logged to a spreadsheet and your team gets a Slack alert
What you get

Clean data loaded without manual fixes

You get every uploaded CSV checked, corrected, and loaded cleanly into your database, with any errors flagged to your team right away.

What you get

Clean, validated records in your database, an error log for anything rejected, and a Slack summary of the results.

What you need

A database, an OpenAI or Anthropic account, a Slack workspace, and a Google account for error logging.

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

Related automations

Back to the AI Playbook