I just shipped an automated lead qualification system for a B2B SaaS client. The whole build took 7 days from kickoff to production deployment.
The Problem
The sales team was manually reviewing 50+ demo requests per week. About half were unqualified (students, competitors, tire-kickers). They needed automation that could:
- Enrich leads with company data
- Score based on ICP fit
- Route qualified leads to sales, others to nurture campaigns
The Stack
- n8n for workflow orchestration
- Clearbit for company enrichment
- Custom scoring logic (revenue, employee count, tech stack)
- HubSpot for CRM integration
The Results
- 70% of leads auto-qualified (no human touch needed)
- Sales team saves 15 hours/week
- 2x faster response time to hot leads
- $0 in ongoing costs (self-hosted n8n)
Key Architecture Decisions
1. Enrich Before You Score
Don't try to score leads with just email and name. Enrich first with:
- Company size
- Revenue estimates
- Tech stack
- Funding status
2. Simple Rules Win
Started with complex ML scoring. Scrapped it. Went with simple if/then rules:
- Company size > 50 employees? +10 points
- Uses Salesforce? +5 points
- Has funding? +5 points
3. Always Have a Human Review Loop
Edge cases will break your automation. Build in a "needs review" queue for:
- Scores between 40-60 (gray area)
- Missing enrichment data
- VIP domains (investors, partners)
Full Technical Breakdown
I wrote a detailed walkthrough with code snippets and n8n workflow screenshots here:
👉 Lead Qualification System Breakdown
Questions?
Happy to answer questions about the build, n8n workflows, or automation architecture in general!
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