This workflow is an AI-assisted lead qualification automation built in n8n. It captures inbound leads from a Typeform submission, normalizes the form data, scores each lead using a combination of rule-based logic and AI analysis, classifies the lead as Hot, Warm, or Cold, stores the qualified lead in Airtable CRM, sends personalized follow-up emails, and notifies the sales team on Slack when a high-value lead is detected.
The goal of this workflow is to automate the early-stage sales qualification process so that sales teams can prioritize the most promising leads faster, while still maintaining structured CRM records and personalized outreach.
When a user submits the lead qualification form, the Typeform Trigger captures their details, including name, email, company, job title, company size, industry, budget, project timeline, buying role, intent, and main business challenge.
The workflow then passes this data into a normalization step, where all form responses are converted into clean internal fields such as name, email, company, budget, timeline, intent, and main_challenge. This makes the rest of the automation easier to process reliably.
Next, the workflow applies a rule-based scoring model. It scores the lead across six structured dimensions:
Each dimension receives a score, and the workflow calculates an overall rule-based score using weighted logic. For example, strong budget, urgent timelines, high buying authority, and clear demo/pricing intent contribute to a higher score.
After the rule-based scoring step, the lead is analyzed by an AI Lead Quality Analysis Agent. This AI agent evaluates the lead across qualitative sales dimensions such as pain severity, budget signal, urgency signal, buying intent, and industry fit. It also generates a short reasoning summary, a recommended next best action, and a personalization angle that can be used in follow-up outreach.
The workflow then parses the AI output and combines it with the rule-based score. A final scoring node calculates the overall lead score using a weighted blend of rule-based and AI-based scoring:
Final Score = 60% Rule Score + 40% AI Score
Based on the final score, the lead is classified into one of three tiers: