Bad data will kill your AI startup faster than bad models

Don’t build an AI agent on top of a pipeline you can’t trust.

Your pipeline — the path your data takes from input to output — has to be solid.

If you’re still piecing things together with Sheets or Supabase, this helps you find data problems before users do.

Here’s exactly how to make your data pipelines more reliable when you don’t have a big team or budget.

1. Map how your data moves

To fix problems, you first need to see how your data moves.

Here’s how to do it:

1. Go to Excalidraw. It’s a free drawing tool.

2. Draw 4 boxes:

   • Box 1: Where your data comes from. For example, forms, files people upload, or APIs.

   • Box 2: What happens to the data. Is it cleaned, changed, or fixed?

   • Box 3: Where your data is saved. For example: Google Sheets, Airtable, Supabase.

   • Box 4: Where your data is used. For example, your AI, app, or a report.

3. Draw arrows to connect the boxes, showing the path the data takes.

This drawing helps you see where things might break, so you can fix problems faster.

2. Get alerts when your data stops updating

If your AI product uses Google Sheets as a data source, you want to know right away if your data stops updating. Here’s a simple way to set that up using Make:

How to set it up (step by step)

Step 1 — Prepare your Google Sheet

• Open your sheet.

• Add a column called UpdatedAt.

• Please ensure that your pipeline records the current timestamp in this column whenever new data is added or updated. _(T_ip: If your pipeline doesn’t add timestamps automatically, you can add them using your data source or Google Sheets formulas, but it’s better to have your pipeline set it directly.)

Step 2 — Create a scenario in Make

• Sign up at make.com (free plan works).

• Click “Create a new scenario.”

Step 3 — Add a Google Sheets module

• Choose Google Sheets → Search Rows.

• Connect your Google account.

• Select your spreadsheet and worksheet.

• Under “Order By”, choose UpdatedAt → set to Descending.

• Set “Maximum number of results” to 1 → this grabs the most recently updated row instantly.

Step 4 — Add a filter to detect stale data

• Click the small wrench icon between modules to add a Filter.

• Set the condition: Now - Latest UpdatedAt > 2 hours

• In Make, you can do this using the built-in Date & Time functions:

  • First operand → choose “Current time” (now).

  • Operator → greater than.

  • Second operand → Latest UpdatedAt + 2 hours (from the Sheets data).

Step 5 — Send an alert if data is stale

• If the filter passes, i.e., if the data hasn’t updated in 2+ hours, add a module to send yourself a message:

  • Gmail: Send an email

  • Slack: Send a message

• Write something like: “Heads up: data hasn’t updated in 2+ hours. Check your pipeline.”

Step 6 — Schedule and test

• Go to the top panel in Make → click “Schedule.”

• Set it to run every 15 or 30 minutes.

• Click “Run once” to test it.

Optional: If you prefer staying inside Google Sheets, you can write a little Apps Script to email you when UpdatedAt is too old — but that requires some code.

Using Make is easier for no-code users.

3. Monitor your APIs for free

If your product depends on APIs, you need to know when they go down.

How to set this up using UptimeRobot:

1. Go to UptimeRobot → Sign up for a free account.

2. Click “Add New Monitor.”

3. Set Monitor Type → “HTTP(s).”

4. Paste your API link (URL)

5. Set it to check every 5 minutes

6. Add your email or Slack so it can send you alerts

7. Click Save

That’s it. Now you’ll get a message the moment your API goes down.

4. Automate data quality check

Sometimes your data flows without errors — but the content is wrong or incomplete. For example:

• Emails missing

• Wrong data types

• Unexpected drops in record counts

You can catch these problems using Make.com (no code needed).

Here's how to set it up:

Step 1 – Start a new scenario

• Go to make.com

• Sign up (the free plan is enough)

• Click “Create a new scenario”

Step 2 – Connect Google Sheets

• Add a Google Sheets module

• Choose Search Rows

• Pick your spreadsheet

• Sort by UpdatedAt in descending order

• Set it to check the last 10 rows

Step 3 – Add checks for common issues

• Click the wrench icon to add a Filter You can check for things like:

• Missing email: If the “User Email” field is empty

• Low data count: If today’s row count is less than 100

• Wrong values: If a status is not “active,” “paused,” or “cancelled”

Step 4 – Send an alert if something’s wrong

• Add a step to send yourself a message You can choose:

• Gmail: Send an email

• Slack: Send a message

• Write something like: “Alert: New rows are missing required fields. Check your data pipeline.”

Step 5 – Set a schedule and test

• At the top, click “Schedule”

• Run it every 15 minutes (or once an hour)

• Click “Run once” to make sure it works

Using Airtable instead of Sheets?

• Use the “Watch Records” trigger in Make

• It works the same way — just with Airtable instead of Google Sheets

5. Have one source of truth

If you’re pulling the same data from five different places, you’re inviting silent bugs.

How to fix this:

• Pick one “master” database — Airtable, Supabase, Google Sheets, or Notion

• Validate and clean data before it lands there

• Point every part of your product — dashboards, AI features, outputs — to read from this single database

This one change alone makes debugging faster.

6. Test your output like a real user

Finally, just because nothing is broken doesn’t mean everything is working.

Here’s what to do:

• Take 5 to 10 real examples from your users

• Try them in your app yourself

• Check if the result looks right

• If something’s wrong, it’s usually because the data is bad — not the AI

Do this once a week. It’s a quick check that saves hours later.

A quick note for developers

If you know Python, you can take things further:

• Use Pandas to validate data automatically

• Run checks on GitHub Actions or Cron jobs

• Send Slack or email alerts when anything fails

But if you’re no-code, the steps above cover a lot of common cases.