Why AI Integration Is Almost Always Domain-Specific

The main contradiction that often kills products of the type — AI generic integration in business:

**Universality vs. domain specificity**

Often people see a product as a "ready-made skill in a store."

But if you look at it from an engineer's perspective, the real value lies not in the skill itself, but in the process of fine-tuning it for a specific domain, stack, and tools.

Let's break this down through decomposition.

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## What can actually be standardized

There are layers that **barely depend on the project**:

### 1. Basic AI operating modes (meta-skills)

- research mode

- task definition mode

- decomposition mode

- critical review mode

- planning mode

- refactoring mode

- review mode

However, even these stages may contain specific requirements used in a particular company and domain-specific.

"Skill instructions per stage." They are universal in structure but empty inside — without project context.

### 2. `agents.md` structure

A template for the orchestrator agent:

- how to determine the stage;

- how to load the necessary skills;

- how to store project state;

- how to switch between roles;

- how to create artifacts.

This is already a framework that can be reused.

### 3. Integration patterns

- "how AI reads Jira tickets"

- "how AI commits to git with proper messages"

- "how AI maintains ADR (Architecture Decision Records)"

- "how AI writes tests for Jest/pytest/..."

These are not the integrations themselves, but **interaction templates**. Already domain-specific due to being tied to a specific tool.

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## What will always have to be written manually

- business logic and domain;

- specific architectural rules of the project;

- specifics of the stack and library versions;

- internal tools (internal APIs, admin panels);

- the team and its processes.

**No skill store will sell this.**

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## So where is the real product?

In our view, the minimum viable product is not a "ready-made agent" but a **constructor + methodology**:

### Product Level 1: "AI architecture for developers"

A person receives:

- a ready `agents.md` (orchestrator);

- a library of meta-skills (work stages);

- instructions on how to add their own project context;

- integration templates (git, jira, confluence);

- examples filled in for a real project.

This is what is usually called an MVP.

### Product Level 2: "Domain-specific packages"

For example:

- "AI for Python backend with FastAPI";

- "AI for React frontend";

- "AI for data projects with pandas and DBT";

- "AI for embedded on C/C++".

Each package is basic skills plus rules already configured for the domain, test patterns, typical errors.

They are written and configured on the fly if the company providing services does not have a standard domain-specific solution.

### Product Level 3: "Fine-tuning service"

This is consulting — people come to your company, look at the stack, processes, tasks, and build a personalized agent with ongoing support, improvements, and enhancements.

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## Key takeaway

**A minimum viable product is necessary**, because otherwise there will be no entry into the funnel. However, this is not a classic generic mass-produced factory product.

**There are no ready-made universal solutions for engineering tasks**, and someone has to be honest with clients about that.

Our compromise:

> We sell the **framework + methodology**, and the domain configuration — either as part of a paid service, or as "packages per stack", or left to the user with instructions.

It's like selling not a ready house, but:

- an architectural design,

- a set of blueprints,

- a materials specification,

- assembly instructions,

- and a "turnkey build" option.

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## Important note about "memory and folders (directories) in local AIs"

This is a genuinely strong aspect of the product. Most users:

- don't know about custom instructions;

- don't know about local memory (Claude projects, ChatGPT memory, GPTs);

- can't structure context into files;

- don't understand the difference between a system prompt and project knowledge.

Therefore, even a basic product like:

```

📁 my-ai-workflow/

├── agents.md          — orchestrator

├── skills/

│   ├── research.md

│   ├── task-definition.md

│   ├── architecture.md

│   ├── implementation.md

│   ├── review.md

│   └── refactoring.md

├── context/

│   ├── project.md     — filled in by the user

│   ├── stack.md

│   └── decisions.md

└── README.md          — how to load this into your AI

```

already has value **in itself**. Because it saves dozens of hours on structuring.

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## Conclusion

**Most of it for a specific project will have to be written by hand**. This is not a bug, it's a feature — this is where the value of domain expertise lies.

But this does not negate the product. On the contrary:

1. **Base framework** — as an MVP.

2. **Domain packages** — as extensions.

3. **Project fine-tuning** — as a premium service.

4. **Methodology** — as content and training.

Our "project architecture" skill from the knowledge base becomes a key product: We are selling not code and not prompts, but the **correct way to work with AI as an engineer and business architect**.

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