How to Build a Custom GPT to Support Your Solo Business

I've built 17 AI systems using a framework and philosophy I developed bootstrapping to $5M / yr. Some of them even generated six figures for me. 

That's not bragging. That's proof of what happens when you stop playing with artificial intelligence and start building with it.

Consultants keep treating ChatGPT like Google with better grammar. Fire off a question, get an answer, move on. 

The founders pulling away? They're building custom GPTs trained on their own data, their own frameworks, their own thinking. They’re creating what I call a "Fleet of Me," which are AI systems that multiply time without adding payroll.

The difference between a GPT that sits unused and one that becomes indispensable? Behavior change. If your GPT requires motivation, it will fail. If it replaces friction, it will stick.

This guide shows you how to build a custom GPT that becomes a genuine business tool—not another forgotten experiment.

Why you should build custom GPTs for your solo business

You're using AI like a search box instead of a system

You need the discipline to stop throwing AI at everything. Start focusing on specialized GPTs. Start focusing more on data. Start focusing on context.

The superficial approach looks like this: open ChatGPT Plus, ask a question, copy the answer, close the tab. Repeat tomorrow with zero memory of what you discussed yesterday.

That's not leverage. That's a faster way to do research.

Real leverage comes when AI becomes part of your infrastructure. When large language models remember your frameworks, your positioning, and your client language. When they can make business decisions based on how you think and not generic best practices scraped from the internet.

The shift from search box to system changes everything about what AI can do for your business.

Your business is full of repeatable patterns that GPTs can learn

Think about what you actually do each week.

You write proposals that follow a similar structure. You onboard clients with the same questions. You deliver frameworks you've explained dozens of times. You respond to common objections in sales conversations.

Every one of these patterns can be encoded into a custom GPT.

Yesterday, I had to do something in my business that would take 30-45 minutes when done manually. I could have just done it. But I spent a few hours building a system I can use forever. It was painful, and I worked with different GPTs, and they made mistakes. But now I have something that takes seconds.

Do this 3-5 times, and your business operations will start running on systems rather than your constant attention.

Custom GPTs let you grow without hiring

This is the core thesis behind everything I teach.

I have different custom GPTs helping across my podcast and newsletter. I'm producing the same amount of content—if not more—than when I had an entire marketing team that cost around $30k per month.

At my revenue level, I have very little help. The limited help I do have is focused on building AI systems in different parts of the business. Not crossing t's or dotting i's.

An "Agency 3.0" model might be you, your LLMs and AI models, and maybe one or two expert partners. A lean team doing the work of 5-10-20 people.

That's not a fantasy. That's how I operate today.

First-party data is your biggest moat

GPT builders and AI tools are everywhere now. Anyone can spin up a basic AI agent in an afternoon.

So what creates differentiation?

Your data. Your transcripts. Your past proposals. Your sales call recordings. Your client results documentation. Your internal processes are captured in Google Docs and Notion.

Training GPTs on your own materials creates a competitive advantage no one else can replicate. Generic AI models give generic outputs. A GPT trained on your knowledge base sounds like you, thinks like you, and makes recommendations based on what's actually worked in your business.

This is the moat. Not the technology itself, but the first-party data you feed into it.

Specialized systems outperform general assistants

I call this the Specialist Era of AI.

A generative pre-trained transformer built for "anything" will always lose to one designed for a specific job. General assistants spread attention thin. Specialized systems go deep.

My Offers GPT doesn't try to help with customer service, content writing, and business plans. It does one thing: help consultants build high-ticket offers. That focus is why it performs.

When you build a custom GPT for a narrow use case, such as client onboarding, proposal generation, or discovery call prep, you can optimize every prompt, every instruction, every piece of context for that single outcome.

One powerful GPT beats ten mediocre automations—every time.

How to create a custom GPT in 6 steps

Step 1: Choose a high-leverage use case

Don't try to automate everything at once.

Pick one task you do repeatedly that meets these criteria:

• It happens at least weekly
• It follows a predictable pattern
• It takes meaningful time or mental energy

Good starting points include drafting client proposals, preparing for discovery calls, creating content outlines, generating onboarding emails, or summarizing meeting notes into action items.

Bad starting points include anything that requires heavy judgment calls you haven't systematized yet, or tasks so varied that no pattern exists.

The discipline here matters. Most consultants fail because they try to build a GPT that "helps with everything." That's just ChatGPT with extra steps. Start narrow, prove value, then expand.

Step 2: Collect and prepare your source material

Your GPT is only as good as what you feed it.

Gather the artifacts that show how you actually think and work:

• Past proposals and contracts
• Email templates you reuse
• Framework documents and methodology descriptions
• Call transcripts or meeting notes
• Client intake questionnaires
• Your best content pieces

This becomes your knowledge base—the first-party data that transforms a generic AI tool into something that sounds like you.

Don't worry about perfect organization yet. The goal is to get your intellectual property out of your head and into files that GPT can reference.

Step 3: Use a GPT builder that supports memory or files

OpenAI's custom GPT builder works well for most consultants. You can upload documents, set custom instructions, and create something shareable in a few hours.

Claude's project feature offers similar capabilities with strong document handling.

The key requirement: whatever platform you choose must let you embed your business context either through uploaded files, custom instructions, or both.

If you're on ChatGPT Plus, you already have access to create a GPT. 

For more complex use cases involving customer support automation or AI automation across multiple systems, you might need something like Zapier or Make. But start simple. Most consultants overcomplicate this.

Step 4: Train the GPT to match your voice and decision-making

This is where most people rush and regret it later.

Your custom instructions should include:

• How you talk (formal vs. conversational, industry jargon you use)
• Your frameworks and methodologies by name
• Decision criteria you apply (what makes a good client, what makes a strong proposal)
• Examples of outputs you consider excellent

Feed it sample Q&A pairs. Show it a question a client might ask and the answer you would give. Do this for 5-10 common scenarios.

The more specific your instructions, the less you'll need to correct outputs later. Generic instructions produce generic results.

Step 5: Test with real workflows

Theory means nothing until you run it through actual business processes.

Take a real client situation and use your GPT to draft the proposal. Record your next discovery call and have it summarize the key points. Generate next week's content outline.

Compare what it produces to what you would have created manually.

You'll find gaps. Maybe it misses your pricing approach. Maybe it sounds too formal. Maybe it forgets a framework you always reference. Good. That's user feedback you can act on.

Refine the instructions based on real output.

Step 6: Document how and when to use it

A GPT without a trigger is a GPT that gets forgotten.

Create a simple document—even a few bullets—that answers: 

• When do I use this? 
• What do I feed it? 
• What do I do with the output?

Better yet, embed the GPT into an existing workflow. If you always prep for discovery calls on Monday morning, that's when the GPT runs. If you draft proposals within 24 hours of a sales conversation, GPT becomes the first step in that process.

The goal is to make the GPT the default behavior, not an optional extra. Systems that require you to remember to use them eventually get abandoned.

6 tips to build high-leverage custom GPTs

Tip 1: Don't optimize for novelty, but rather optimize for ROI

Cool prompts are fun to share on LinkedIn. They don't pay your bills.

The GPTs worth building solve recurring, high-value problems. Before you start, ask: How often does this task happen? What's it costing me in time or money? Will this still matter in six months?

If you can't point to a clear return, you're building a toy. Toys get abandoned. Business tools get used.

Tip 2: One powerful GPT beats ten one-off automations

I see this constantly—consultants with a dozen half-built automations scattered across their business. None of them work particularly well. All of them require maintenance.

Consolidate. One well-trained GPT with deep context on your business will outperform a collection of shallow tools every time.

Go deep before you go wide.

Tip 3: Build for outcomes and not outputs

There's a difference between "this GPT writes faster" and "this GPT closes deals."

Outputs are documents, emails, and summaries. Outcomes include faster sales cycles, higher close rates, greater capacity for premium clients, and better client results.

When you evaluate whether a GPT is working, measure the outcome. Did it actually move the needle on something that matters to your business?

Tip 4: Use your actual business data

Generic prompt templates from Twitter threads will give you generic results.

Your GPT should be trained on your proposals, frameworks, client language, and past wins. The more specific the input, the more useful the output.

This is also your protection against commoditization. Anyone can copy a prompt. Nobody can copy your accumulated intellectual property and first-party data.

Tip 5: Align your GPT with your service or offer

The highest-leverage GPTs directly support how you make money.

If you sell strategy engagements, build a GPT that helps you prepare sharper recommendations. If you run workshops, build one that generates customized pre-work for participants. If you do fractional work, build one that drafts status updates and executive summaries.

The closer the GPT sits to revenue, the more it matters.

Tip 6: Review and refine after every few uses

Even my GPTs make mistakes. That's fine. What matters is whether you're improving them.

After using a GPT 5-10 times, review what's working and what isn't. Add examples of good outputs to the instructions. Clarify areas where it keeps getting things wrong. Remove instructions that aren't helping.

This isn't "set and forget" technology. The consultants getting real results treat their GPTs like living systems that improve over time.

Your GPT is only as good as the behavior it creates

I started this guide with a simple claim: I've built 17 AI systems, and one generated six figures this year.

That didn't happen because the GPT was clever. It happened because it replaced friction in a process I run constantly. It became the default, not an option.

Building a custom GPT isn't about playing with new technology. It's about creating infrastructure that extends your expertise without requiring your constant attention.

Start with one high-value workflow. Train it on your data. Test it with real work. Refine based on what you learn.

Stop experimenting. Start building.