vibeflow-n8n/recipes/ai-lead-enrichment.md

Recipe: AI Lead Enrichment

Goal

Capture a new inbound lead, enrich the company context, generate a short structured summary, score the lead using simple rules, and write the result to the CRM.

Typical trigger

• new CRM lead
• form submission
• webhook from a landing page

Systems involved

• CRM such as HubSpot or Pipedrive
• enrichment source such as Clearbit-like data or internal lookup
• optional LLM step for summarization
• Slack for alerts

Core steps

1. Receive the lead payload.
2. Normalize fields such as company name, email domain, and source.
3. Query enrichment data.
4. Generate a concise structured summary.
5. Apply a scoring rule based on company size, geography, and source.
6. Update the CRM record.
7. Notify Slack if the lead crosses a threshold.

Important assumptions

• scoring starts with safe defaults
• enrichment can fail without blocking the whole workflow
• missing optional fields should not break CRM updates

What makes this recipe good for demos

• shows practical AI usage
• stays grounded in business logic
• produces an output people immediately understand

Validation checklist

• lead payload is parsed correctly
• enrichment failures are captured clearly
• summary format is stable
• score thresholds are visible and editable
• CRM update succeeds even when optional data is missing