u/BronzeTA

I'm building a multi-agent AI workflow for a local marketing agency. They handle around 50 new leads per month and need the following automated:

Auto-responding to new leads

Booking discovery calls

Handling FAQs and client inquiries

Competitive analysis and client strength/weakness breakdown

A personal AI assistant for the agency owner

Built with n8n + OpenAI. Hosting is handled. My monthly running cost comes out to roughly $10.

I'm thinking of pricing it at:

8,000 EGP setup fee (one-time)

1,500 EGP/month for maintenance and updates

Is this too low? Too high? I want to be fair but also not undersell something that took real time to build.

For context — I'm not a big agency, I'm an independent builder. But the system is solid and covers a real operational gap for them.

Anyone with experience pricing automation work in Egypt or similar markets, I'd appreciate the input.

I'll try to keep this structured, but fair warning — it's a long one. I genuinely need perspective from people with real-world experience.

How it started

I got into Data Analysis through a Udacity scholarship — not because it was my field, but because I had a problem at work and needed to solve it. I solved it. Then I solved a few more like it. That curiosity pulled me toward Python, specifically for data work and automation — all of this was before the LLM wave hit.

The path got wider

Around that time, I landed a role as an Educational Consultant at a programming courses company. I figured it would bridge the gap between what I was learning and actual industry exposure. That role eventually led to an opportunity as a Python Instructor — I didn't hesitate for a second.

Meanwhile, I'd gone deeper into Data Science and ML on my own. I built my first automated personal assistant — voice-command WhatsApp messaging, web search, and eventually hooked it up to a SQLite database with Python package docs so I could query it when I got stuck. I also built a simple web app where you'd feed it a dataset, pick your parameters and target variable, and it would evaluate multiple ML models, return the best one with accuracy scores, and let you download the generated .ipynb file.

Then things got... wide

A client's father reached out. He was opening a new branch and wanted me involved. What I thought would be a focused role turned into: office manager, IT support, task delegator for developers, and marketing liaison. Then the CEO pitched an educational platform idea — suddenly I was also researching white-label software, building rollout plans, and coordinating everything simultaneously.

Then I was asked to travel to the parent company abroad. Things expanded even further. I was running ad campaigns, shooting and editing videos, handling sales, managing admissions, supporting instructors, resolving student issues, and troubleshooting technical problems — all at once, every single day.

Eventually things fell apart. I came back home and stepped away from the role.

Where I am now

I took some time to decompress and think. But when I came back to assess myself, I genuinely couldn't answer a simple question: what do I actually know how to do?

I've done data analysis, Python development, ML experimentation, automation workflows, educational consulting, instructing, office management, IT support, project coordination, marketing, sales, and video production — and yes, even some VFX and 3D hard surface modeling at some point. More recently I've been deep into AI tools, agents, and automation workflows.

I'm 30 and I feel like I have a résumé full of experiences and a brain full of half-expertise.

My honest theory

I think a big part of this feeling comes from never having had a senior person in my corner. Every single thing I learned, I figured out alone — through trial, error, and stubbornness. No mentor. No structured environment where someone could tell me "you're actually good at this" or "you need to focus here."

That makes self-assessment really hard. You don't have a reference point.

So to anyone who's been through something similar — how did you untangle it? How do you identify your actual strengths when you've worn too many hats and taught yourself everything?

Been learning n8n for a few months and finally built something

I'm actually proud of. But I need people who know what they're

doing to tell me what I'm missing.

**What it does:**

Form input (job title, location, industry)

→ Apify scrapes matching LinkedIn profiles

→ GPT-4o-mini writes a personalized cold email per lead

→ Gmail sends automatically

→ Google Sheets logs everything with status tracking

**Edge cases I handled (took me longer than I expected):**

- No email found → logs "No Email ⚠️" and skips

- Duplicate leads → checks Sheet before processing

- Gmail rate limits → 45 sec Wait node between sends

- Empty Apify output → IF node catches it

**What I'm NOT sure about:**

1. Is Apify the best scraper for this or is there something better?

2. GPT-4o-mini vs Haiku for email writing — which actually performs better?

3. Should I add a reply detection node? (check if lead replied → stop follow-ups)

4. Is 45 seconds between emails too slow / too fast for Gmail?

5. What would YOU add to this workflow if it were yours?

I feel like I'm missing something obvious that more experienced

people would catch immediately.

Be brutally honest — I'd rather fix it now than find out the hard way.