What this template does
Every new lead in your Airtable lead capture base triggers an automated qualification flow. The scenario sends the lead's data to GPT-4 along with your ICP definition, GPT-4 returns a structured score (0-100) and reasoning. Based on the score, leads are automatically routed to the right destination.
This eliminates the manual triage step that typically eats 30-45 minutes per day for a sales team.
Use cases
- B2B SaaS qualifying inbound demo requests
- Agencies filtering inbound contact forms
- E-commerce brands routing wholesale inquiries
- Coaches pre-qualifying discovery call bookings
How it works
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Watch new Airtable rows
The trigger module watches your Lead Capture base for new rows added in the last 5 minutes.
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Build the qualification prompt
Use a Set Variable module to construct the GPT-4 prompt with your ICP definition + the new lead's data.
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Call GPT-4 with structured output
Send to OpenAI with response_format: json_object. Ask for a JSON with score, reasoning, recommended_action.
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Parse JSON response
Use the JSON Parse module to extract the structured fields from GPT-4's response.
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Update Airtable with score
Write the score, reasoning, and recommended_action back to the lead's row.
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Router based on score
If score >= 80, route to sales. If 50-79, route to nurture. If < 50, mark as disqualified.
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Notify Slack for high-fit leads
Send a formatted message to #sales-alerts with the lead's name, company, and score.
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Add to nurture sequence
For medium-fit leads, add to your Mailchimp/Brevo nurture list with a tag for the recommended segment.
Setup time
Around 15 minutes if you already have Airtable, OpenAI, and Slack connected to Make.
Customization ideas
- Add multiple AI models for cross-validation (GPT-4 + Claude) and average the scores
- Include LinkedIn enrichment via Clearbit before the AI scoring step
- Adapt the scoring rubric per product line (different ICPs for different products)
- Add a feedback loop: when sales close a deal, log the original score for accuracy tracking