The Problem: You run a B2B SaaS. You have a "Request Demo" form. 80% of the submissions are students, competitors, or "gmail.com" leads that will never convert.
Your sales team (or you) wastes hours manually checking LinkedIn profiles before booking calls. This kills momentum.
In this engineering log, I'll breakdown exactly how I build automated qualification engines for clients using n8n, OpenAI, and HubSpot.
The Architecture
We are moving away from Zapier for this because of cost and flexibility. We use n8n (self-hosted or cloud) to act as the traffic controller.
- Trigger: New Webhook from Landing Page Form
- Step 1: Enrichment: Clearbit or Apollo API to fetch company data.
- Step 2: AI Analysis: OpenAI (GPT-4o) to score the fit.
- Step 3: Routing: Slack notification or Direct Booking Link.
The "Judge" Prompt
The secret sauce isn't the code; it's the prompt. We don't ask AI to "write an email." We ask it to act as a Senior SDR.
TASK: Analyze the incoming lead JSON.
RULES:
1. Reject if email provider is generic (gmail, yahoo) UNLESS the 'message' field clearly indicates high intent.
2. Reject if employee count is < 10 (we only sell to mid-market).
3. Flag as "Competitor" if industry is "Marketing Agency".
OUTPUT JSON:
{
"qualification_status": "QUALIFIED" | "REJECTED" | "MANUAL_REVIEW",
"reason": "Short explanation",
"suggested_tier": "Enterprise" | "Standard"
}
Why handling this in code matters
Most "No-Code" builders try to do this in 20 messy Zapier steps. By treating lead qualification as an engineering problem, we get:
- Logs: We can see exactly why a lead was rejected.
- Latency: This entire check happens in < 2 seconds.
- Cost: n8n runs for a fraction of the price of Zapier at scale.
Want to deploy this?
I build this exact Lead Qualification Engine as part of my 7-Day Sprint. I hand over the n8n workflows and API keys to you.
The Payoff
Clients who implement this typically see a 300% increase in lead response speed because they only focus on the qualified 20%. The "junk" takes care of itself.