How to Make Millions with AI: A Step-by-Step Guide

What if building a profitable startup didn’t require a team—but just you and the right AI tools? This guide walks through exactly how one founder replaced developers, marketers, and even a product manager with AI and built a revenue engine in days.

Step 1: Discover the Right Idea with AI

Every blockbuster business starts with one thing: a powerful idea rooted in a real trend.

Instead of waiting for inspiration to strike, I use Idea Browser—a tool built to surface daily startup ideas based on emerging trends. It’s like having a private McKinsey analyst, trend forecaster, and copywriter rolled into one. For example, one suggestion was “LLM Boost,” an agency offering SEO services for large language models (LLMs). With hundreds of millions now querying tools like ChatGPT and Claude, LLM SEO is the new organic search—and most companies don’t even know they’re invisible in it.

Idea Browser doesn’t stop at naming ideas. It evaluates the market opportunity, suggests business models, and even estimates founder fit. You plug in your skills, background, and ambition, and it tells you if the idea aligns with your strengths. For LLM Boost, I scored a 6.5 out of 10. Not perfect—but promising enough to pursue.

And here’s what makes this special: every idea is bundled with real data. It scrapes Facebook groups, Reddit threads, YouTube channels, and other signals to tell you where the market is already talking about the problem. You’re not starting from zero—you’re tapping into an under-served need, backed by demand.

Step 2: Sketch the Idea to Sharpen It

You don’t need to be a designer to map out your concept.

I used TLDraw, a visual sketching tool similar to FigJam. With it, I mapped out the user flow for the LLM Boost quiz: how a company enters its name, how our agents assess visibility in various LLMs, and how a score gets generated. This process took seven minutes—and the diagram proved invaluable.

Here’s why: when you feed that image into your AI stack later (like when using Manis), the quality of the results skyrockets. It’s like giving your assistant a blueprint instead of a vague napkin sketch.

A practical tip: when working with LLMs, always upload diagrams, screenshots, or relevant PDFs. They boost comprehension and output quality dramatically.

You’ll also get better prompts when you use visuals. In my case, simply uploading the TLDraw sketch helped Manus generate sharper, more structured product specs—ones I would have missed had I just written it out.

Step 3: Scope the MVP with AI Agents

How do you turn a sketch into specs—without a product manager?

That’s where Manus comes in. This general-purpose AI agent is like hiring a PM, researcher, and operations manager—all at once. I uploaded my TLDraw image and used Wispr Flow to narrate my idea verbally. The transcription became the prompt.

The result? Manus asked clarifying questions (target audience, differentiators, goals), then returned a detailed multi-phase project plan:

• Phase 1: Clarify quiz goals and business logic
• Phase 2: Develop questions and prompts
• Phase 3: Validate structure with users
• Phase 4: Deliver results and reports

It even suggested the data models, outlined the user experience, and created initial drafts of the LLM quiz questions. This saved me weeks of work—and, more importantly, gave me a clear path forward.

Manus also helped with strategy. When I told it my goal—“benchmark businesses on LLM SEO and upsell optimization”—it generated insights, business risks, and positioning strategies I hadn’t considered. It was like having a senior operator join the team for free.

Step 4: Build the Prototype Without Coding

You don’t need to write code to ship a SaaS product today.

Using Bolt, I copied the long-form prompt generated by Manus and pasted it in. That’s it. Within minutes, Bolt generated a working landing page for LLM Boost. It was clean, modern, and conversion-optimized. The headline?

“Your customers are searching for you on LLMs, but you’re not there. Take the free LLM SEO quiz now.”

It even fabricated a testimonial from a “CEO of a major internet company.” Honestly? It read like something real, and I kept it. This kind of AI-generated front-end is good enough to launch and start collecting user feedback on day one.

The quiz itself—built with multi-step logic—collected data like business type, content strategy, SEO importance, and familiarity with AI. Once completed, it promised a personalized SEO visibility score within 24 hours. All of it, generated with a single AI prompt.

If you’ve ever hired a dev team to build a marketing page and form logic from scratch, you’ll understand how insane this is. Bolt replaces weeks of work with minutes.

Step 5: Automate Marketing with Vibe Flows

Customer acquisition doesn’t have to be manual anymore.

For marketing, I rely on Lindy, an AI workflow engine. One of my most effective flows starts with a simple LinkedIn post. When someone comments or likes it, Lindy scrapes their profile, scores them as a lead (using custom rules), and—if qualified—grabs their contact info via Prospect.

Here’s what happens next:

• The lead is added to a CRM.
• A Slack notification pings our sales team.
• If a salesperson reacts with a ❤️, Lindy auto-sends a personalized follow-up email or text.

We used to hire people to manually scrape LinkedIn and do this by hand. With Lindy, we scaled our outreach, automated lead scoring, and closed million-dollar deals—all with zero human input.

And it’s not just useful for my main business. For LLM Boost, the same workflow applies: post about the quiz, monitor interactions, qualify leads, and follow up—all in real time.

What makes it magical isn’t just the automation—it’s the speed. Minutes after someone shows interest, they get a relevant message. Not hours. Not days. That speed changes the conversion game.

Step 6: Assign an AI Product Manager

Who handles client requests, manages specs, and refines your product?

Your AI product manager does. Manus stepped in again here. I uploaded a paid course on LLM SEO from The Vibe Marketer, and the agent used that data to refine the quiz questions and prompt templates further. It even reviewed the quiz length, suggested improvements, and rewrote it to improve completion rates.

The AI gave updates like:

“I’ll analyze the transcript to extract insights relevant to LLM SEO. I’ll then consolidate and revise both quiz questions and the prompt template.”

It was like getting daily progress updates from a project lead—except it never takes sick days and runs 24/7.

It even improved UX. Manus caught that the original quiz was too long and auto-suggested changes. It proposed trimming fields, clarifying questions, and injecting persuasive microcopy—all without my asking.

Bonus: Let AI Negotiate for You

Can you train AI to close deals on your behalf? Yes.

Using Lindy again, we built an email negotiator agent. When a lead clicks “Contact Us” on your pricing page, it kicks off an automated negotiation flow:

• Checks internal docs or knowledge base (e.g., acceptable discount ranges).
• Sends an initial personalized reply.
• Keeps the thread going with 1–2 sentence messages until a decision is made.

This negotiation agent respects guardrails (e.g., never discounting below 10%) and speaks like a human. It’s shockingly effective and has closed multiple high-ticket deals while I slept.

Want to go further? You can even deploy a feedback hotline using Twilio and Claude. When someone calls, their message is transcribed, summarized, and posted to Slack. It’s anonymous, continuous product research.

And we’re still early. Tools like Kubera (for AI-assisted finance), WhisperFlow (for voice-driven prompts), and Cursor (for technical builds) add even more depth.

From Service to Software Business

Once the service side is humming, how do you scale into a real tech business?

Here’s the trick: go back to Idea Browser and ask how to turn your solution into a product like Ahrefs or SEMrush. These companies make $100M+ annually by offering SEO tools. LLM Boost could evolve into an AI-native version of those platforms.

You don’t need 300 engineers. Tools like Cursor, Replit, or Windsurf let 10x devs ship scalable infrastructure quickly. The initial steps—sketching, prompting, validating—are the same. Only now, you're adding crawling logic, real-time dashboards, and integrations.

Yes, it’s more complex. But with the AI stack laid out in this post, it’s doable for a small team—or even a solo founder.

• You don’t need a team to start a business anymore—you just need the right AI workflows and the discipline to use them.

Which part of the workflow feels most exciting to try—and what idea are you going to test next?