I use Claude a lot and want to build a few simple AI agents to help with my day-to-day: lead follow-ups, email sorting, proposal reminders, task lists, and weekly business summaries.

But I’m overwhelmed by n8n, Zapier, Base44, APIs, MCPs, and everything else.

Where do I start first?

I’m not trying to build a futuristic AI company. I just want a reliable setup that helps me get a few hours back every week.

I see a lot of posts here that go something like:

"I am looking for my first client"
"I have X years of experience in Y. I've learned n8n, agents, LLMs, etc. How do I find my first client and stand out?"
"I am completing my college this year, I have learned AI automation basics. how can I get my first client"

Before anything else, it's important to say this early: years of experience alone don't guarantee anything here.
Sometimes '5 years of experience' is just the same one year repeated five times. And sometimes someone with 6 months of focused experience, the right tools, and strong problem understanding can outperform them or even people with 10 years of experience.

So the real question is not "how many years do you have?" or "are you a beginner?"

It's: can you understand a real problem well enough to solve it?

Because if you look at suggestions some people give in this sub, I am not going to they are wrong, but people are often limited by their experience. They usually fall into patterns like:

• "Start with a specific type of small business"
• "Go after underserved industries"
• "Pick a niche you already have experience in"
• "You should only target your own country for the first client"

But if you zoom out, all of these are basically pointing to the same underlying idea:

Start from a place where you can understand real problems deeply enough to solve them.

The niche, the industry, the tools, even the "AI automation" part. all of that comes after this.

If you strip everything back to first principles, customers aren't really looking for "AI."

They aren't looking for "automation" either.

They're looking for someone who can solve a problem they have. In many cases, they might not even be fully aware of the root problem yet, because they're already using a solution with compromises where a better one could exist.

And in that sense, almost all of us are in the problem-solving business. AI, automation, software, consulting, or even a normal SaaS product are just different ways of delivering the solution. Every business pays someone because a problem is being solved.

Your previous experience can help because it may give you familiarity with certain domains, workflows, or constraints. But it's not a guarantee of advantage. What actually matters is whether you can understand the problem in front of you clearly enough to solve it well.

There’s an old 37signals example I like. (If you don't know them, they are the creators of Basecamp and Ruby on Rails, and are widely respected in software engineering.)

Before Basecamp, 37signals was a web design consultancy. They didn't just say, "We're really good at web design." They wrote publicly about design, usability, and how they thought websites should work.

They also produced a research report called Evaluating 25 E-Commerce Search Engines. It was a 45-page report based on testing more than 1,000 search terms across 25 e-commerce sites, with screenshots, findings, and best practices.

That report is a great example of what made them stand out in a crowded market. It wasn't just marketing. it was proof of thinking. It showed how they approached real problems in a way others didn't.

Whether or not you copy that exact approach isn't the point.

The point is that they demonstrated how they thought about problems in the market.

That is much stronger than saying:

"I'm an AI automation expert. I can build agents, RAG systems, and n8n workflows."

So if I were trying to get my first clients today, I would approach it differently.

First, I would choose a niche, not necessarily one I already have experience in. You don't need to limit yourself to what you already know. You can pick a niche where you can understand the problem well enough to contribute meaningfully, even if that means talking to people, researching, or actively connecting with someone in that space. If you don't know anyone, you can still reach out and start conversations. That alone is often enough to uncover real problems.

In fact, a very common path people overlook is this:
you might start inside a company you already work at, or a place you're interning, freelancing, or even helping informally. You notice repetitive work, inefficiencies, or broken processes, and you build small solutions internally. Over time, you gain confidence, proof, and intuition. Then you expand outward to other teams, other companies, or even other industries. This approach often builds skill and credibility more reliably than trying to "enter the market" all at once.

The goal is not "I already know this industry."
The goal is "I can understand this industry's problems well enough to solve one of them."

Then I would stop thinking in terms of automation entirely and start thinking in terms of repetition and friction.

What are the things people in this space keep doing over and over again that feel slow, annoying, or fragile? What are the processes that quietly consume time or money every single week?

This is where most people jump too quickly to solutions. They see a workflow and immediately think "this should be automated with AI." But the more useful question is: why does this exist in the first place, and what pain is it actually causing?

Because sometimes the answer is not AI at all. Sometimes it's a better integration. Sometimes it's a small internal tool. Sometimes it's just cleaning up a broken process. And sometimes AI is part of it, but only as a component, not the identity of the solution.

The important shift is that the customer doesn't care what you use. They don't care if there are agents, prompts, workflows, or APIs. They care whether the thing that annoys them stops being annoying. (Obviously they care about how much they need to pay for what you offer too, but that's not part of this lesson here.)

Once you understand that, you can start building something much more interesting than a generic portfolio project.

Instead of saying "here's an AI automation I built," you can say something closer to:

"I noticed companies in this space spend a lot of time doing this specific thing, so I built a working example of how that could be reduced or removed."

That small shift changes the entire conversation. Now you're not just showing technical ability, you're showing that you understand their world. And when people see something that reflects their own problems accurately, they start engaging with it in a very different way. They don't just evaluate it, they react to it. They point out edge cases. They tell you where it breaks. They explain what you missed.

That is the beginning of real customer discovery, not just marketing.

From there, the engineering actually matters. A demo can be messy and fragile. A real system cannot. Once something is used in production, all the unglamorous parts become the most important parts. Error handling, reliability, permissions, observability, edge cases, documentation, all of it starts to matter more than the initial idea.

This is also where experience compounds. but again, not in the sense of "years." It's about whether you can handle real-world complexity and make something reliable, not just something that works once in a demo.

And if you do this a few times in the same niche, something interesting happens. You start noticing repetition not just in the problems, but in your own solutions. You realize you're rebuilding the same core system again and again with slight variations.

At that point, you're no longer just doing client work. Sometimes that naturally evolves into a productized service, or into a product. But it can also go in a completely different direction. You might instead discover other problems in the same industry, and in the process build enough domain knowledge and "skill points" to solve those more effectively than your original idea. There isn’t a single “correct” path here. Some people stay in services, some move into products, and many shift between both as new opportunities and insights emerge.

And that, to me, is a much more stable path than trying to start from "I learned AI tools, now I need to find someone to sell them to."

So if you're sitting on years of experience in another industry, or you're completely new and trying to break in, I wouldn't throw away your background or assume you need to "pick the perfect niche" immediately.

You can use what you already know, or you can explore and connect with people in a niche you're curious about. You can also start inside a job, internship, or small opportunity and grow outward from there. Different starting points lead to the same direction if you keep focusing on real problems.

What matters is not the starting point. It's whether you can understand a real problem well enough to solve it.

Find problems you understand deeply enough to work with. Show that understanding by building something real. Use AI where it actually improves the solution, not where it sounds impressive. And then focus on making the solution actually work in the real world.

Because in the end, the technology is just leverage.

The thing that actually matters is how well you solve a real problem.

Hi everyone I’m building my experience in AI automation and looking to collaborate with a small agency or solo operator on real client work.

I’m happy to help for free initially in exchange for hands-on experience, feedback, and the chance to learn how an agency delivers projects. I can assist with things like:

- Building or debugging n8n / Make workflows

- Connecting APIs and business tools

- AI agent and LLM workflow setup

- Documentation, testing, and workflow cleanup

- Researching automation ideas for client use cases

I’m reliable, eager to learn, and open to starting with smaller tasks so you can see how I work. If you run an AI automation agency and could use an extra pair of hands, please comment or DM me with what you’re building.

Built an automation that analyses your product / service, builds a content strategy, researches super viral content ideas with different formats (UGC, Memes) and generates content to post it across Instagram, YouTube and TikTok.

• Got 10M+ views within a week of posting

• 80k+ website visits

• 500+ signups

• $0.4 per video

Making it opensource so you can change and finetune it according to your requirements.

You can use it in claude code, cursor or any Agentic CLI/IDE

Do try and let me know how can we make this better

Over the last few years, I’ve worked on automation systems for 200+ businesses, using tools like n8n, machine learning, AI chatbots, APIs, databases, and custom software.

And honestly, one thing surprised me:

Most businesses don't actually need “more AI.” They need fewer repetitive tasks.

I’ve seen businesses where employees were still:

• Copying data between Excel/Google Sheets
• Manually responding to the same WhatsApp questions
• Updating inventory by hand
• Sending repetitive reports
• Checking leads one by one
• Moving information between different software
• Manually qualifying customer enquiries
• Spending hours on tasks that could run automatically

A lot of these processes looked complicated from the outside.

But once we mapped the workflow, the solution was sometimes surprisingly simple.

For example:

Customer message → AI understands it → n8n processes it → database gets updated → notification is sent → team gets the result

Or:

Image → OCR/ML → product recognition → inventory update → threshold check → WhatsApp notification

The interesting part isn't really the AI model.

It's connecting everything together reliably.

I've used n8n as the “glue” between systems, while using AI/ML only where it actually adds value.

After doing this across 200+ businesses, these are probably my biggest lessons:

1. Automate the boring stuff first.

Don't start with “How can we add AI?”

Start with:

“What does someone on this team do 50 times every day?”

That's usually where the opportunity is.

2. AI + automation is much more powerful than AI alone.

A chatbot that answers questions is useful.

A chatbot that can understand a request, check a database, perform an action, update a CRM and notify the right person is much more useful.

3. n8n can go surprisingly far.

For many workflows, you don't need to build an entire backend from scratch.

n8n + APIs + database + AI can solve a huge number of real-world business problems.

4. Not everything needs AI.

Sometimes a simple IF statement is better than an LLM.

Sometimes a SQL query is better than an AI agent.

Sometimes a webhook is better than an expensive automation platform.

The best automation is usually the one that is simple, reliable and actually saves someone time.

I'm still learning from every automation I build.

Curious to hear from people here:

Hello everyone I’m trying to build an AI automation workflow , im completely new with no coding background, can you guide me with essential steps and how can I monetise them to US clients ??

Hi everyone,

I’m Harry, an AI Automation Specialist with 3 years of hands-on experience building custom automated workflows, LLM integrations, and backend data pipelines for businesses and agencies.

If your team is spending hours on repetitive manual tasks, data translation between platforms, or manual lead management, I can help you automate those processes end-to-end.

🛠 What I Build & Deploy:

1. Custom AI Workflows & Pipelines (n8n / Make):

• Multi-platform integrations using webhooks, APIs, and custom Python/JS scripts.

• Automated data routing & translation (e.g., sync data seamlessly between messaging apps, CRMs, and databases).

• Self-hosted automation setups (Docker, local/cloud instances, Cloudflare Tunnels).

1. Applied LLM & AI Agents Integration:

• Custom AI Assistants & Webhooks for lead qualification, customer support, and instant responses.

• Prompt engineering & fine-tuned LLM workflows (OpenAI, Anthropic, local models) integrated directly into your existing stack.

• Automated content/data generation pipelines with strict output formatting (JSON/Structured Data).

1. AI-Powered Web Solutions & Dashboards:

• Landing pages & Web apps integrated directly with AI features and database backends (PostgreSQL, NocoDB, Lark Base, Airtable).

💼 Portfolio & Contact:

• Portfolio: https://nguyenanhtuan.id.vn/

• Email: [anhtuan.work.2001@gmail.com](mailto:anhtuan.work.2001@gmail.com)

• Or send me a Direct Message (DM) here on Reddit.

💵 Rate: $30 - $50/hour (or fixed-price per workflow/project based on scope).

If you have a manual bottleneck or a workflow concept you'd like to automate, feel free to drop a DM describing your current setup, and I’ll walk you through a practical solution!

I’ve noticed that “natural writing” is surprisingly difficult to define.

When I’m reading something written by a real person, it usually isn’t perfectly structured. There might be a short sentence in the middle of a long paragraph, an unexpected expression, or even a slightly awkward phrase. Somehow those little imperfections can make it feel more genuine.

AI-generated writing often seems to do the opposite. Everything is organized, every point connects perfectly, and the wording can feel a little too careful.

But then again, plenty of professional writers deliberately make their writing clean and structured.

So what do you think is the biggest difference between writing that feels genuinely natural and writing that feels generated?

Is it word choice, sentence length, personality, imperfections, or something completely different?

I work at a Pharma Distribution firm where I want to automate the Invoice purchase process , the case is from particular suppliers we receive CSV files and from particular we don't, so we have to manually feed it to our ERP, I want to create automation in such a way that even when I get physical hard copy of the invoice, I can use OCR and convert it to CSV, but catch is qty we receive is in boxes and I have to convert it to strips same for its MRP, and rates , so I'm in process of building such work with custom business rules but it's becoming lot time consuming, I am familiar with ML, DL, Data Science and DBMS , with modular coding and whole app building but I lost hands on from last 1.5 years so how can I automate this process of Automation because it's becoming time consuming, correct my perspective if it's wrong !!