Anyone here built automated lead pipelines from EU business registries or cold outreach systems? DM me.

i was thinking of starting an agency a AI agency or any other agency i know how to do thing all i need is person who have to skill to make this thing possible what i mean is i get clients you make the automations. Intrested people DM

I'm 21 and started my own AI automation agency 2 months ago. My first client came through my network, and the next ones came from posting LinkedIn videos about what I was building. But I wanted something that scales without waiting for inbound. So I built a cold email automation pipeline from scratch. It handles the full flow: - Lead discovery using web scraping (Firecrawl + Google Maps data) - Email verification before any email goes out - AI-powered research per lead: website scrape, competitor analysis, needs analysis - Personalized 3-email sequences generated by AI with A/B testing on - subject lines, approaches and styles - Automated follow-up timing (day 1, day 3, day 7) - Reply detection via IMAP with AI classification into 6 categories - Human approval dashboard so nothing sends without review - Lead recycling for non-responders after 60 days The key insight: cold email is not about volume. It is about deep research per lead so the AI has enough context to write emails that actually feel personal. Every lead goes through a 7-step research process before a single word gets written. I put together a complete guide of 26 pages (PDF attached) that covers the full architecture, the actual AI prompts I use, SMTP setup, warmup strategy, deliverability tips, and the 10 mistakes I made building this. Hope it helps someone here!

My cold emails were failing. My “personalization” was too. “Loved your post…” = ignored. So I built a signal-based system instead: Hiring spikes Product launches Role changes Real problems across the internet Now it’s: Saw you’re hiring fast — scaling issue? That gets replies. Core is done. Still improving hiring signals. Would mind sharing what signals could get some replies in your niche?

AI tools change fast. New features, new interfaces, new releases — it’s easy to feel like you’re always catching up. But long-term knowledge in AI doesn’t come from tracking updates. It comes from understanding what stays consistent beneath them. Most tools are just different interfaces over the same ideas: input → processing → output. Prompts, data flow, decision logic, and system behavior — these are the parts that transfer across tools, even as they evolve. If you learn the tool, you keep restarting. If you learn the pattern, you keep progressing. The goal isn’t to master every update. It’s to understand how AI fits into workflows — where it adds judgment, where it reduces effort, and where it needs structure. That’s what makes your knowledge durable. When a new tool or update comes out, do you feel like you’re starting over — or just upgrading something you already understand?