Why I Built a Team of 7 AIs

The Question Nobody Was Asking

Sometime in late 2024, I was sitting in front of three different browser tabs — Claude, ChatGPT, Perplexity — cross-referencing answers to the same business question. Not because any single one was bad. Because each one was giving me something different. Claude nailed the architecture. ChatGPT made the language sing. Perplexity actually cited its sources so I could verify what the other two were claiming.

And it hit me: I was already running a team. I just had not built it yet.

After forty years of operating businesses — manufacturing, Harley-Davidson dealership management, services, technology — I know what a good crew looks like. You do not hire seven generalists and hope for the best. You hire specialists, give each one a clear role, and build the workflow that lets them do what they are best at while someone coordinates the whole operation.

That is exactly what I did with AI. And it changed everything about how StackFast runs.

The Team Is Called AI6

The team brand is AI6. Not because there are six models — there are seven. The name is the name. If you are the kind of person who needs the number in the brand to match the headcount, we are probably not going to get along on much else either.

Here are the seven:

Claude — the architect. When I need something structured, reasoned through, or built with real depth, Claude is the first call. Code architecture, decision frameworks, long-form reasoning. Claude does not try to impress you with flair. It tries to be right. That is the kind of crew member every operator wants.

Manus — the orchestrator. When a task has fifteen moving parts and I need an AI that can manage the workflow across them, Manus runs the show. Think of it as the project manager who does not need to be managed.

Grok — the contrarian researcher. Grok pulls from real-time data and has a disposition toward telling you what you might not want to hear. In a room full of agreeable assistants, that matters. I have been in enough boardrooms to know that the person who disagrees with you thoughtfully is more valuable than the five who nod along.

ChatGPT — the polisher. Nobody makes language as clean and accessible as GPT when you give it something solid to work with. I do not start with ChatGPT. I finish with it. The difference matters.

Gemini — the deep researcher. When I need synthesis across massive datasets or long documents, Gemini handles volume that would choke other models. It processes context at scale, which makes it ideal for research-heavy tasks where you need broad coverage, not just a quick answer.

Perplexity — the fact-checker. Every team needs someone whose job is to say "Are we sure about that?" Perplexity searches, cites, and verifies. It is the sanity layer that keeps the rest of the team honest.

ChatLLM by Abacus — the enterprise bridge. When the work needs to connect to business systems, data pipelines, or enterprise workflows, ChatLLM handles the integration layer. It is the crew member who speaks both AI and business infrastructure.

This Is Not How Most People Use AI

Most people I talk to — smart operators, experienced founders — use one AI for everything. They pick their favorite, ask it every question, and judge the entire technology by whether that single model gives them a good answer on any given Tuesday.

You are probably doing the same thing right now — tab-switching between AIs, cross-referencing answers, not quite satisfied with any single one. That is not a workflow problem. It is operator instinct emerging without architecture. The question is not which AI you should use. It is whether you are going to build the team you have already started assembling.

That is like hiring one person and asking them to do sales, engineering, accounting, legal, and customer support. Even the best employee in the world will be mediocre at most of those jobs. But nobody would run a business that way. So why do people run their AI that way?

The answer is that most people still think of AI as a tool. You pick up a tool, use it, put it down. A hammer does not need teammates. A calculator does not have a role on a crew.

But AI is not a hammer. Not anymore. The models I work with reason, generate, verify, and create. They have strengths and weaknesses. They have blind spots and specialties. They are, functionally, crew members — and they perform best when they are used that way.

The REPS Pipeline

Once I had the team, I needed the workflow. What emerged is something I call the REPS pipeline. It is not complicated. But it works.

Research — Grok and Gemini. They run the opening research in parallel. Grok pulls real-time data, challenges assumptions, and flags things the other models might miss. Gemini does the deep, broad synthesis — the kind of thorough coverage you need when you are making a decision that will cost real money.

Expression — Claude. Once the research is solid, Claude takes the raw material and gives it structure. This is where the thinking happens. Not the data gathering. The architecture. Claude builds the framework that turns information into something you can actually use.

Polish — ChatGPT. With the structure in place, ChatGPT refines the language, smooths the edges, and makes it accessible. Good thinking that nobody can understand is useless. ChatGPT turns Claude's architecture into communication.

Sanity — Perplexity. Before anything goes out the door, Perplexity checks the claims, verifies the data, and makes sure we are not publishing something that sounds good but falls apart under scrutiny. This is the quality gate.

Research, Expression, Polish, Sanity. REPS. Every piece of content, every strategy document, every major decision framework runs through some version of this pipeline. Not always all four stages — sometimes a quick task only needs two. But the structure is always there.

One to Two Humans Run Everything

Here is the part that makes people uncomfortable: StackFast runs with one to two humans and a team of seven AIs. I am the pilot. The AIs are my crew. That is the operating model.

I do not say this to brag about efficiency. I say it because it is the honest truth about what is possible right now — not in some theoretical future, but today, with tools that already exist.

"Most operators are trying to figure out which AI to use," explains Robert Trupe, founder of StackFast Technologies and decision architecture pioneer. "I stopped asking that question two years ago. The right question is not which AI. It is which team of AIs, doing which jobs, in which order. That is the difference between using a tool and building an operation."

When you build a specialized team, you do not need a large headcount to produce at scale. You need the right crew members in the right roles with the right workflow connecting them. That is true whether your crew is human, AI, or — as in my case — both.

The Proof of Concept Is the Concept

StackFast is building cognitive twin infrastructure — systems that capture how operators think and make that judgment available at scale. The AI6 team is not separate from that mission. It is the proof of concept.

Every day, I am demonstrating that a single operator with a well-built AI team can produce the output of a much larger organization. Not by working harder. By architecting the crew correctly.

The cognitive twin is not some distant product we are building in a lab. I am living inside it. My AI6 team is the first implementation of the infrastructure we are building for other operators. The extraction of how I think, the architecture of how that thinking gets distributed, the validation that the output matches the intent — all of it is happening in real time, through the daily operation of this team.

That is not a pitch. That is Tuesday.

What Operators Should Take From This

If you are an operator reading this, here is what I want you to walk away with:

Stop looking for the best AI. There is no best AI. There is a best team. And you are the one who has to build it.

Assign roles. Do not ask one model to do everything. Figure out what each model does better than the others and give it that job. Your results will improve immediately.

Build the pipeline. The magic is not in any single model. It is in the sequence. Research feeds expression. Expression feeds polish. Polish feeds sanity. The pipeline is the product.

Stay in the pilot seat. I am not suggesting you hand your business to AI. I am suggesting you run your AI the way you would run a crew — with you at the helm, making the calls that require judgment, experience, and the kind of pattern recognition that only comes from decades of doing the work.

I have been building teams for forty years. The AI6 team is the best team I have ever assembled. Not because the individual members are perfect. Because they are in the right roles, running the right workflow, with an operator at the helm who knows what good output looks like.

That is not the future of work. That is how I work right now. And if you are an operator who has been wondering how to get more leverage without adding more people, this is where you start.

The last piece in the architecture is ExecuTwin — the execution layer that takes what the AI6 team produces and deploys it into actual operations. Research, expression, polish, sanity — and then execution. The pipeline does not end at output. It ends at action. ExecuTwin is where the thinking becomes doing.

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