AI Lead Qualification & Routing System (n8n + Gemini + Apollo)
A B2B sales team was struggling with slow lead response, manual triage, and missed opportunities due to inefficient workflows.
I designed and implemented a fully automated AI-powered lead management system that captures, enriches, qualifies, and routes leads in real time — eliminating manual intervention.
This system ensures every lead is processed instantly and assigned to the right sales representative with complete context.
SYSTEM WORKFLOW
• Lead Capture & Storage
All incoming leads are captured via webhook and instantly stored to ensure no data is lost.
• Data Enrichment (Apollo API)
Automatically enriches lead data including company details, role, industry, and contact information.
• AI Qualification (Google Gemini)
Leads are analyzed and scored (Hot / Warm / Cold) using AI-based structured output.
• Smart Routing & Scheduling
Leads are automatically assigned to the right sales rep based on logic and availability.
• Automated Notifications
Gmail notifications are sent with full lead context, and CRM is updated instantly.
RESULTS
✔ 50% faster lead response time
✔ Zero manual triage
✔ 2x increase in qualified conversations
✔ Improved sales efficiency and pipeline clarity
TECH STACK
• n8n (workflow automation)
• Google Gemini (AI qualification)
• Apollo API (data enrichment)
• Gmail API (notifications)
• Webhooks & CRM integrations
USE CASE
Ideal for:
• B2B SaaS companies
• Sales teams (SDR / RevOps)
• Startups handling high lead volume
CALL TO ACTION
Looking to automate your lead pipeline and improve conversions?
Send me a message — I can help you build a similar system tailored to your business.
Normal work disappears. Genuine judgment surfaces.
I built Northline Flow for @Lovable’s #lovablechallenge — a fictional owner-operated home-services business in Surrey, BC.
I didn’t want to build another prettier scheduler. The problem I wanted to attack was the handling around the booking.
Routine requests can continue toward booking. Genuine ambiguity reaches the owner as one framed decision. Unsafe or wrong-fit requests stop instead of being forced into the normal booking path.
I also built a deterministic test harness and a playable Morning Shift proof mode: three fictional requests enter the same routing rules, but only one requires a human decision.
The part I’m most excited about is that the challenge itself became reusable. We captured the process from brief → working flow → adversarial testing → cinematic donor → bounded visual integration → playable proof → final freeze.