How I built a LinkedIn Automation SaaS using n8n

The internet wants you to believe that n8n is dead and everyone has moved on to pure Claude Code or custom scripts.

But real production environments tell a different story.

I recently launched my LinkedIn Automation SaaS (https://inboundy.app/). When planning the architecture, I chose to build the entire backend infrastructure using n8n workflows.

Why? Because of visibility.

When you scale outreach automation, you don't want to blindly hope an LLM handles the orchestration perfectly. When something breaks, I don’t want to jump through endless nested functions to find the bug. In n8n, I see it instantly. I can trace the flow, fix the node, and keep moving.

How it started:

I originally built this custom n8n setup just to market my GLB-Optimizer. It worked crazy well for client acquisition. It handled keyword-based connections and hyper-personalized the messages by matching the recipient's profile summary with my own context so it actually felt human.

It brought in so many clients that I decided to pack it into a clean, cloud-based SaaS platform.

Building in the AI space right now isn’t about chasing the latest hype tool. It’s about choosing the right architecture for the job. For us, n8n is still a production beast.

Curious to hear from the community: Who else is still running their core SaaS backends or heavy agency workflows on n8n? What’s your go-to stack right now?

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