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Case study · Automation

Lead Signal Engine

A lead-signal engine that monitors hiring, expansion, and job-posting signals, then qualifies them with AI before delivery. Sales teams receive context-rich leads in Gmail instead of manually scanning the web.

Project type
Automation
Best for
Teams replacing manual work
Outcome
A faster, more reliable workflow
Core stack
n8n, Google API, RapidAPI
Lead Signal Engine overview

Technology stack

  • n8n
  • Google API
  • RapidAPI
  • OpenAI API
  • Gmail
  • LinkedIn

01 — The challenge

Client challenge

Sales teams waste hours manually scanning LinkedIn, Google alerts, and news sources for buying signals — new hires, office expansions, and job postings that indicate a company is in a growth phase and ready to buy. No automation existed to surface and qualify these signals at scale.

02 — What I built

What I built

I built an automated signal detection pipeline using n8n that continuously monitors Google and RapidAPI LinkedIn for new hire announcements, new location openings, and strategic job postings. Each signal is passed through an AI qualification layer that scores relevance against defined ICP criteria — and only warm, qualified leads are delivered directly to Gmail for immediate outreach.

  • Google and RapidAPI LinkedIn signal monitoring for new hires, locations, and job postings
  • AI-powered ICP qualification layer scoring each signal against defined criteria
  • Automated filtering to eliminate noise and surface only high-intent leads
  • Enriched lead records with context on the trigger signal
  • Direct Gmail delivery of qualified leads ready for outreach

Inside the build

01 / 02
Lead Signal Engine — screenshot 1 of 2

03 — The outcome

Measured outcomes

The pipeline surfaces warm, signal-triggered leads on autopilot — eliminating manual prospecting entirely. Sales teams wake up to a Gmail inbox filled with qualified, context-rich leads that have already shown a buying signal, compressing the prospecting cycle from days to minutes.

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