Forem: Mohamed El-Moumeny

Building an AI-Powered Lead Gen Workflow with n8n, Apify, and Gemini

Mohamed El-Moumeny — Fri, 10 Oct 2025 17:48:19 +0000

I spent a weekend building a fun proof-of-concept: a fully automated workflow that scrapes leads from Google Maps, uses an AI to score and personalize messages for them, and then saves it all to a spreadsheet.
Here’s a technical breakdown of the stack and the workflow I built in n8n.

The Data Flow

The logic is pretty linear: Scrape -> Normalize -> Enrich with AI -> Parse -> Format -> Store.

Apify for Scraping: The workflow kicks off with an HTTP Request to an Apify actor. Nothing too complex, just a POST request with my search queries in the JSON body. The free tier gave me enough credits to pull a few hundred records for testing.
AI Agent with Gemini: The core of the workflow is the n8n AI Agent node connected to the Google Gemini API. The prompt engineering was key here. I gave it a detailed system prompt defining its role, the input data structure, and—most importantly—the exact JSON format I needed for the output. This included fields for an outreach_score and a communication object with an email and WhatsApp message.
The Gotcha: Parsing AI Output: As many of you know, getting consistently clean JSON from an LLM can be a challenge. The Gemini model would often wrap its valid JSON in a `json markdown block. My first attempts with a simple JSON.parse() failed.

The fix was an "Edit Fields" node running a bit of JavaScript to clean the string before parsing:
`javascript JSON.parse(rawOutput.replace(/^`json\n/, '').replace(/\n`$/, '')) `
A simple solution, but it made the workflow 100x more reliable.
Humanizing the Date: Another small but important transformation. The scrapedAt timestamp was in ISO 8601 format (...Z). I used n8n's built-in Luxon library within an expression to format it for the spreadsheet:
`javascript DateTime.fromISO($json.scrapedAt...).toFormat('dd MMMM yyyy, HH:mm') `
This is way cleaner than handling new Date() objects in a full Code node.
Append to Sheet: The final step is a simple Google Sheets "Append" node that maps the flattened JSON object to the correct columns.

This was a great way to see how powerful the new AI nodes in n8n are. The ability to call an LLM as a simple, integrated step in a workflow, just like any other API, is a total game-changer. Next up, I'll be adding nodes to actually send the outreach.

Anyone else building cool stuff with AI agents in their workflows?

Automating My Pinterest Content Pipeline with n8n + AI (Free Setup)

Mohamed El-Moumeny — Fri, 03 Oct 2025 21:00:14 +0000

Automating My Pinterest Content Pipeline with n8n + AI (Free Setup)

I was spending way too much time digging for content on Pinterest, so I decided to automate the process.

With a bit of tinkering in n8n, I ended up building a workflow that turns viral TikToks into Pinterest-ready pins — completely free to run.

⚡ What the workflow does

I just send a keyword like healthy recipes, 10, and the workflow:

Searches TikTok using Apify
Filters results for high engagement (1M+ views, 1K+ shares, 20–60 sec)
Downloads the top 3 videos (without watermarks — tricky part 😅)
Uploads them to Google Drive
Generates Pinterest titles & descriptions with a free LLM (DeepSeek via OpenRouter)
Logs everything in a Google Sheet with status = Pending for review

💡 Why it matters

Saves hours of manual searching
Runs on free tiers:
- n8n (self-hosted)
- Apify free credits
- OpenRouter free tier
- Google Drive + Sheets

And because I still review before posting, Pinterest doesn’t flag low-quality content.

🛠️ Challenges I ran into

Getting AI to output clean JSON (regex gymnastics to strip markdown)
TikTok CDN requires Referer headers or it throws 403 errors
Deduplication to avoid grabbing the same video twice

✅ Final thoughts

This workflow is lightweight, flexible, and basically zero-cost.

It also reminded me how powerful tools like n8n can be for automating entire content pipelines with just APIs + logic.

💬 Would you like me to share the full workflow JSON in another post?

8 N8N Fundamentals That Will Make You Build Workflows Like a Pro

Mohamed El-Moumeny — Sun, 28 Sep 2025 17:27:15 +0000

6 Months of Client Work Condensed into Essential Principles

After six months of building AI automation workflows for paying clients, I’ve learned some hard truths about what separates beginners from pros.

Most tutorials teach you what individual nodes do. They show you where to click. But they never talk about the mindset and principles that allow you to build complex, reliable automations independently.

I’ve condensed my most valuable lessons — the ones that stop workflows from turning into “spaghetti monsters” and save you hundreds of dollars in testing fees — into 8 core fundamentals.

Part I: The Pre-Build Phase (Foundation First)

A professional workflow is planned, not stumbled upon. Master these two principles before you ever open the n8n canvas.

1. Use Case First, Workflow Second

This is the biggest mistake I see beginners make.

What Amateurs Do:

Open n8n, stare at a blank canvas, get overwhelmed by the node options, and give up.

What Pros Do:

Start with the problem, not the tool. The workflow must bend to fit your use case, not the other way around.

✅ My 4-Step Professional Process:

Write the business problem in plain English.
Define the exact input (e.g. new row in Google Sheet) and desired output (e.g. formatted Slack message).
Break the process into 3–5 high-level logical steps.
Then worry about which nodes to use.

💡 Real Example:

Instead of “I want to build an AI workflow,” try:

“I need to automatically categorize incoming customer support emails and route urgent, high-value ones to my phone via SMS.”

2. Don’t Reinvent the Wheel (Template Hunting is a Core Skill)

Starting from scratch each time is slow and error-prone.

What Amateurs Do:

Start from scratch every single time.

What Pros Do:

Always search for existing templates first.

🔍 My Template Hunting Process:

Search n8n’s community templates (official library)
Browse Reddit (r/n8n, r/automation, r/aiagents)
Watch YouTube use-case videos
Search X / Twitter with #n8n

This helps you:

Build faster starting from reliable bases
Discover powerful nodes you didn’t know
Learn from other people’s mistakes

Part II: The Build Phase (The Core Skills)

This is where you execute the plan. Professional builders have a streamlined process for data handling and testing.

3. Master the Data Flow Principle

Every n8n workflow essentially follows:

Input → Transform → Output

If you can track data through these three stages, you can build anything.

🔑 Primary Data Sources (used ≈ 90%):

Your own databases (Airtable, Google Sheets, Supabase)
Public APIs (HTTP Request node or dedicated nodes)

Where many beginners struggle: the HTTP Request node.

💡 The Secret Weapon: Postman

Most advanced users use Postman—even though it’s external to n8n.

🚀 My API Testing Process:

Copy the cURL from API docs.
Import into Postman; test with real parameters.
Ensure it works perfectly.
Only then port the working request into your n8n HTTP Request node.

This ensures your workflow logic isn’t blocked by an incorrect request.

4. The 6 Nodes That Handle 80% of Your Work

After over 100 client workflows, I found that mastery of a few nodes covers most needs:

Data Shaping → Set / Edit Fields — extract, rename, convert data types
Data Cleaning → Filter — drop invalid rows (nulls, duplicates, empties)
Data Integration → Merge — combine datasets or enrich with extra fields
Logic → IF — basic conditional branching
The Emergency Button → Code — for very specific transformations
AI Processing → Basic LLM Chain / AI Agent — handles ~90% of AI tasks

💡 Code Node Hack:

Don’t code it yourself—describe input & output to ChatGPT. This trick carried me through my first three months of client work.

5. Pin Your Node Output (Save Real Money & Time)

This is a pro technique that separates novices from efficient builders.

What Beginners Do:

Re-run the whole, expensive workflow every time they change one node.

What Pros Do:

Run once, pin the output, and reuse it for downstream testing.

✅ How to Pin Like a Pro:

Run the workflow once to get real data in every node.
Click the “pin” icon on an upstream node’s output (e.g. HTTP or AI nodes).
Edit pinned data to simulate edge cases.
Test downstream nodes without invoking API again.

💸 Why It Matters:

One AI node test can cost $0.10+ if rerun each time. Across projects, that’s real money. Don’t re-pay for what’s already computed.

Part III: The Post-Build Phase (Professional Polish)

The difference between a hobbyist and production-grade system is how you handle complexity, errors, and cost.

6. Create Sub-Workflows (Keep It Clean)

Before, my workflows turned into spaghetti with 50+ nodes. Debugging was a nightmare.

Now: Main workflows stay simple (4–6 nodes). Everything else is abstracted into sub-workflows.

🔧 Puzzle Piece Principle:

Each sub-workflow is a reusable, isolated component.

Create a Components folder in n8n
Build reusable sub-workflows (error handling, notifications, data cleaning)
Invoke them from your main workflow

✅ Debugging Benefit:

If something breaks, you immediately isolate which component failed.

7. Implement Error Logging (Be the First to Know)

Amateurs wait for complaints. Professionals get alerted with full context before users notice.

What Amateurs Do:

Hope workflows never fail.

What Pros Do:

Log failures (and successes), and notify immediately.

📊 What My Error System Logs:

What went wrong (error message)
Where it failed (node name, execution ID)
Input data that triggered the error
Automatic retry attempts

💡 Pro Tip:

Also log successful runs. Clients like seeing:

“Your automation processed 47 new leads today.”

8. Track Your AI Costs (Avoid Bill Shock)

Worst case: your AI agent runs wild and charges $500 overnight. Trust evaporates.

✅ Solution: Use n8n’s built-in cost tracking in every AI node.

📊 I Always Track:

Tokens used per run
Cost per execution
Daily/monthly spending limits
Model performance metrics

In proposals, I include a cost breakdown. Surprises kill trust. Own the budget from day one.

Final Thoughts

Mastering these eight fundamentals will put you ahead of 90% of people trying to piece things together by trial and error.

The secret to building like a pro isn’t a flashy node—it’s planning, discipline, and smart testing.

Now, go build something great. 🚀