Automated Lead Intelligence Pipeline Development

System 03 — Case Study

Automatically discovers pain signals and produces actionable outreach.

Finding good leads meant manually scanning channels every day and copy-pasting anything promising into a spreadsheet — slow, easy to skip when busy, and warm leads went cold in the gap between spotting and acting.

An end-to-end lead-intelligence pipeline that runs without supervision: it pulls fresh posts from target channels, qualifies each one with an AI enrichment step, stores structured records, and delivers a ranked daily digest — so the team starts the day with a prioritized list instead of a blank search bar.

RSS sources feed a Python pipeline where a Claude-powered step scores relevance and extracts key detail; qualified leads are written to Airtable and compiled into a scheduled Gmail digest. Verification and error handling run throughout, so the pipeline flags problems rather than failing silently.

Lead discovery shifted from a daily chore to a hands-off system. Qualification is consistent because the same logic runs every time, and nothing slips through because the pipeline never skips a day.

AI Lead Intelligence Engine screenshot 1

AI Lead Intelligence Engine screenshot 2

AI Lead Intelligence Engine screenshot 3

I'm actively looking for remote AI engineering and operations roles where I can ship real automation, not just talk about it.

Built for impact, not just looks. · Michael Alusa Limisi · Nairobi, Kenya · © 2026