Joonhyeok Ahn, an AI consultant for AI first company and founder of Threadsight, recently posted something that made me stop scrolling:

"In 2025, Claude became my co-founder."
"Not an assistant. A system that runs 40% of my agency operations."
"Here's the blueprint I wish existed 6 months ago."
"Most people ask Claude questions."
"I built Claude into my business infrastructure."
"So I packaged everything into one resource."

That short update captures a shift many businesses haven't made yet.

Most teams still treat AI like a better search engine or a smarter intern: you ask questions, it responds, you move on. Joonhyeok Ahn is pointing to something very different — treating Claude as an operational system that quietly runs a large slice of the business.

In this post, I want to unpack the blueprint he hinted at, and explore what it really means to make an AI model like Claude your "co-founder" rather than just your assistant.

From asking questions to designing systems

As Joonhyeok Ahn explained, "Most people ask Claude questions. I built Claude into my business infrastructure."

That one contrast is the heart of the idea.

Asking questions is transactional. You get an answer, then the interaction ends.
Building infrastructure is structural. You design recurring workflows, connect tools, and let the system run without you.

When you treat Claude as infrastructure, you stop asking, "What can Claude do for me right now?" and start asking, "Which repeatable processes can Claude own end-to-end?"

That is exactly what his blueprint is about.

Inside Joonhyeok Ahn's Claude blueprint

In the post, he lists out what is inside his system:

Claude Projects Architecture
Claude Code Setup
Claude + n8n MCP
Claude Skills Blueprint
Query MCP + SEO MCPs

Each of these pieces is a building block for turning a chat interface into a real operational layer.

Let's walk through them, and why they matter.

1. Claude Projects Architecture: turning chaos into compound knowledge

"Structure that turns chaos into compound knowledge" is how he describes the Projects layer.

In practice, this means:

Defining clear projects for each major area of your business (lead generation, client onboarding, content production, reporting, internal training, etc.)
Attaching the correct documents, SOPs, briefs, and examples to each project so Claude has persistent context
Letting knowledge accumulate instead of repeat-uploading the same files every time you chat

Over time, this turns Claude from a "blank slate" into a specialized operator that understands how your specific agency works. The more you use it, the more each project becomes a living knowledge system.

Instead of a thousand ad-hoc chats, you get a handful of well-structured workspaces that compound in value.

2. Claude Code Setup: agents that write and execute code

The next layer is "Claude Code Setup – Deploy agents that write and execute code."

This is where Claude stops being just a text generator and starts acting more like a developer or automation engineer:

Claude writes scripts, small tools, or helper functions for your workflows
Through tools and integrations, those scripts can actually be executed, tested, and iterated on
You standardize patterns (for example, transforming raw CRM exports into clean dashboards, or generating reports from analytics data)

For an agency, this means repetitive technical work — data cleaning, report generation, formatting, glue code between tools — can be delegated to AI-powered agents instead of human developers.

You still review and approve, but Claude does the heavy lifting.

3. Claude + n8n MCP: plain-English automation

"Claude + n8n MCP – Build automations by describing them in plain English."

This is a big deal for non-technical operators.

n8n is a workflow automation platform. MCP (Model Context Protocol) is a way to connect Claude to external tools in a controlled, structured way. Put them together, and you get something like this:

You describe the automation you want: "When a new lead fills out this form, enrich them, score them, add them to our CRM, and create a personalized outreach sequence."
Claude translates that description into a concrete n8n workflow using the MCP integration.
The workflow runs in the background whenever the trigger conditions are met.

Instead of hiring an automation engineer for every new process, your operators can "speak" automations into existence — and Claude does the translation work.

4. Claude Skills Blueprint: domain expertise on demand

Next, Joonhyeok Ahn mentions a "Claude Skills Blueprint – Custom instructions that make Claude an expert in your domain."

This is where you encode:

Your agency's positioning, offers, and ICPs
Your writing style, tone, and brand voice
Your frameworks for strategy, copy, creative, and reporting
Your internal quality standards and checklists

Instead of re-explaining "how we do things here" every time, you design a skills layer once and reuse it across projects and use cases.

Done well, this makes Claude feel less like a generic AI model and more like a trained team member who "gets" your clients, your niche, and your way of working.

5. Query MCP + SEO MCPs: live research at scale

Finally, there is "Query MCP + SEO MCPs – Connect Claude to live data sources for research at scale."

Static prompts are powerful, but real leverage comes when Claude can:

Access fresh data from your analytics, CRM, or data warehouse
Pull live search data, SERP information, and keyword clusters
Synthesize and summarize that information into strategies, briefs, and reports

For SEO and content-driven agencies, this means Claude can:

Generate research-backed content outlines
Identify gaps and opportunities in your topical map
Monitor competitors and search trends without manual digging

Research stops being a separate, manual phase and becomes a continuous, automated capability.

Why agencies charge $3K–$8K for this

In the post, Joonhyeok Ahn notes that agencies charge "$3K–8K to configure systems like these" and that it took him "200+ hours to test and document" the blueprint he's now giving away for free.

That price range often surprises people, but it makes sense when you look at what is really being sold:

Thinking: understanding a business well enough to identify which processes to automate
Architecture: designing how projects, skills, tools, and automations fit together
Implementation: wiring up Claude, MCPs, n8n, data sources, and quality checks
Iteration: debugging, refining prompts, and tightening feedback loops over time

You're not paying for a single prompt or a one-off integration. You're paying for an AI-powered operating system that will quietly run in the background every day.

Seen through that lens, $3K–$8K to save dozens of hours every month — and to de-risk your AI experiments — is not expensive at all.

How to start treating AI as your co-founder

You don't need Joonhyeok Ahn's full blueprint to begin making this shift, but you can borrow his principles.

Here is a simple way to get started:

Identify one process you run at least weekly that is mostly digital and rules-based (for example, reporting, basic outreach, or content prep).
Create a dedicated Claude project just for that process and load it with real examples, SOPs, and context.
Write a mini skills blueprint for that project: who you serve, what "good" looks like, and what constraints Claude must respect.
Add one tool or integration — even a simple one, like connecting to a data source or a template — so Claude can act, not just write.
Run the workflow repeatedly, improve the prompts, and track how much time you're saving.

Once the first process is stable, add a second. Over time, you'll look up and realize Claude is running a meaningful share of your operations — not because you asked better questions, but because you designed better systems.

Make 2025 the year you stop using AI like search

Joonhyeok Ahn ends his post with a challenge: "Happy New Year. Make 2025 the year you stop using AI like a search engine."

I think that's the real invitation here.

We are moving from a world where AI is something you "use" occasionally, to a world where AI is something you "work with" every single day — a co-founder-level system that holds knowledge, executes processes, and scales your operations far beyond what a human-only team could manage.

Whether you adopt his exact blueprint or not, the shift in mindset is worth copying: stop thinking in prompts, start thinking in projects, skills, and systems.

This blog post expands on a viral LinkedIn post by Joonhyeok Ahn, AI consultant for AI first company | I automate 80% of marketing & sales ops with AI systems | Founder, Threadsight. View the original LinkedIn post →