Data Processing Pipelines

One of the most valuable AI automations is processing incoming data automatically. In this lesson, you will build a pipeline that takes data from a Google Sheet, processes it through AI, and sends the results to Slack.

What You Will Build

Template 3: Data Processing Pipeline

Trigger: New row added to Google Sheets (e.g., customer feedback form) → AI: Analyze sentiment, extract key topics, generate summary → Action: Post structured results to a Slack channel

This template works for any data source: form submissions, survey responses, support tickets, or CRM updates.

Real-World Use Cases

Data Source	AI Processing	Output
Customer feedback form	Sentiment analysis + topic extraction	Slack alert with summary
Job applications	Qualification screening + summary	Slack notification to hiring manager
Product reviews	Categorize by feature + sentiment	Google Sheet with enriched data
Sales inquiry form	Lead scoring + response draft	Slack message + CRM update

Building the Pipeline in Zapier

Step 1: Trigger from Google Sheets

1. Create a new Zap
2. Trigger: Google Sheets → "New Spreadsheet Row"
3. Select your spreadsheet (e.g., "Customer Feedback")
4. Test to pull in a sample row

Example spreadsheet structure:

Timestamp	Name	Email	Feedback	Rating
2026-03-01 10:30	Jane Smith	jane@email.com	Love the new dashboard but the export feature is broken	3

Step 2: Process with AI

1. Add OpenAI → "Conversation"
2. Model: gpt-4o-mini
3. System prompt:

You are a customer feedback analyst. Analyze the following feedback and provide a structured analysis.

Respond in this exact format:

SENTIMENT: [Positive/Negative/Mixed]
URGENCY: [Low/Medium/High]
TOPICS: [comma-separated list of topics mentioned]
SUMMARY: [1-2 sentence summary]
ACTION: [Recommended next step in 1 sentence]

4. User message:

Customer: {Name}
Rating: {Rating}/5
Feedback: {Feedback}

5. Temperature: 0.1 (low for consistent, structured output)
6. Max Tokens: 200

Step 3: Post to Slack

1. Add Slack → "Send Channel Message"
2. Channel: #customer-feedback (or your preferred channel)
3. Message:

📊 *New Customer Feedback*

*From:* {Name} ({Email})
*Rating:* {Rating}/5

*AI Analysis:*
{OpenAI Response}

_Submitted: {Timestamp}_

The Slack message will display the structured AI analysis, making it easy for your team to quickly scan and prioritize feedback.

Building the Pipeline in Make

Step 1: Watch for New Rows

1. Add Google Sheets → "Watch Rows"
2. Connect your Google account
3. Select your spreadsheet and worksheet
4. Set Maximum number of returned rows to 10

Step 2: AI Analysis

1. Add OpenAI → "Create a Completion"
2. Use the same system prompt as the Zapier example
3. Map the customer data from Google Sheets into the user message
4. Temperature: 0.1
5. Max Tokens: 200

Step 3: Write Results Back to Google Sheets (Optional)

Before posting to Slack, you can enrich your original spreadsheet:

1. Add Google Sheets → "Update a Row"
2. Map the AI response fields to new columns in your spreadsheet (Sentiment, Urgency, Topics)
3. This creates a searchable, filterable record of all analyzed feedback

Step 4: Route by Urgency

Use Make's router to handle feedback differently based on urgency:

1. Add a Router after the AI module
2. Path 1 (High Urgency): Add a filter: AI response contains "URGENCY: High" → Slack → Send to #urgent-feedback channel → Gmail → Send email alert to the support manager
3. Path 2 (Normal): No filter (catches everything else) → Slack → Send to #customer-feedback channel

This ensures critical feedback gets immediate attention while routine feedback is logged normally.

Scaling the Pipeline

Processing Multiple Rows

Both Make and Zapier can process multiple rows in a single run:

• Zapier: Each new row triggers a separate Zap run automatically
• Make: Use the "Watch Rows" module to process batches. Combine with an Iterator if you need to process an array of data within each row.

Adding More AI Steps

You can chain multiple AI calls for deeper analysis:

1. Step 1 (AI): Classify the feedback (Sentiment + Urgency)
2. Step 2 (AI): Generate a suggested response based on the classification
3. Step 3: Post the analysis AND the suggested response to Slack

Each AI step adds a small API cost, so only add steps that provide clear value.

Connecting Other Data Sources

The same pipeline pattern works with any data source:

• Typeform/Google Forms → AI → Slack (process form submissions)
• Airtable → AI → Email (enrich database records)
• Webhooks → AI → Google Sheets (process API events)
• Email → AI → Notion (organize incoming information)

Replace the trigger module with your data source, keep the AI processing step, and adjust the output action.

Key Takeaways

• Data processing pipelines follow a simple pattern: Data In → AI Processing → Results Out
• Use low temperature (0.1) and structured output formats for consistent analysis
• Make's router lets you handle different results differently (e.g., urgent vs. normal feedback)
• You can enrich your original spreadsheet with AI analysis before sending to Slack
• The same pipeline pattern works with any data source, not just Google Sheets