AI as it will be used tomorrow, built with the tools of today.

We don't treat "AI" as magic. We treat it as advanced interfaces the next layer between humans and computers. PAI is a framework for building those interfaces and upgrading the tools and assistants you already use.

We built PAI with PAI. The entire PAI stack CLI, pai-socket, and this very site was created using the same scoped, structured workflows that PAI gives you. See the case study →

AI means Advanced Interfaces

Fifty years ago, "AI" meant variables, hashmaps, and early high-level languages. They felt intelligent because they abstracted away complexity. Now they're just called programming.

Today, "AI" means large language models and tools. In five to ten years, those will be seen the same way: ordinary infrastructure, just how computers work.

PAI is built for that future.

We assume LLMs are a given. The real innovation is in the interfaces: how you scope problems, expose tools, shape context, and connect everything to the systems you already run.

What is PAI?

PAI stands for Penny Advanced Interfaces.

It isn't a new LLM, and it isn't trying to replace your favorite coding assistant. PAI is a supplement to them.

PAI gives you a way to:

  • Define precise scopes and domain-specific outlines for new tools, services, and workflows.

  • Capture that knowledge in a structured, reusable form.

  • Plug that structure into any assistant or model you already use.

Instead of one more "do-everything" assistant, PAI focuses on the boring but powerful parts:

  • Programmatically defined prompt structures

  • Strict, vetted tool exposure

  • Clear contracts about how your domain works

You keep ChatGPT, Claude, Gemini, or whatever else you love. PAI makes them behave like they've been working inside your codebase for years.

And we're not speaking hypothetically. The entire PAI stack CLI, pai-socket, and paicodes was designed and built using the same scoping, structuring, and domain-modeling techniques that PAI delivers to you.

How PAI fits into your stack

Whatever your process is today, PAI doesn't ask you to throw it away. It slides in next to it and makes it sharper.

Scoping for assistants

Need a domain-specific outline for a new tool or service?

  • Run pai scope in a code sandbox, prompted with your real constraints.

  • PAI produces a clear, domain-aware outline and interface.

  • Feed that result into your favorite LLM assistant to generate code, tests, or documentation.

PAI handles the shape of the problem. Your assistant handles the content.

PAI as an MCP-style service

Want your assistant to "just know" your domain?

  • Add PAI as an MCP-style tool or service.

  • Your assistant gains controlled access to PAI's domain knowledge, scopes, and interfaces.

  • No scraping, no guesswork just explicit, versioned definitions.

Now your assistant can ask PAI: "What tools exist here?" "What does this service expect?" "How should I call this interface?"

Keep your workflow, add clarity

Already have a workflow you trust? Keep it.

Introduce PAI where ambiguity lives:

  • Requirements

  • Internal standards

  • Integration edges

  • New tools and services

PAI crystallizes those into something any model can consume reliably.

PAI doesn't try to be the star. It's the interface layer that lets your LLMs act like specialists instead of autocomplete.

Want to dive deeper? See how PAI works →

Try it live on paicodes

On this site, you can talk to PAI through a ChatGPT-style interface powered by pai-socket.

pai-socket is our streaming WebSocket engine. It already integrates with ChatGPT and OpenRouter, so when you open the chat:

  • Your messages travel through pai-socket.

  • PAI's structured prompts and domain tools shape the request.

  • Responses stream back to you in real time.

For the public web interface, we keep things safe and simple:

  • Only public knowledgebases are accessible.

  • No file write capabilities.

  • Interactions stay within a constrained, read-only domain.

It's the same infrastructure we use internally just presented in a way that anyone can explore.

Who PAI is for

PAI is designed for people who already build with AI:

  • Developers gluing LLMs into real systems.

  • Teams who want structure around their assistants instead of one-off prompts.

  • Organizations that need domain-aware behavior without retraining their own model.

If you already rely on LLMs or coding assistants, PAI is there to enhance them, not replace them.

Build the interfaces that outlast today's AI buzzwords

LLMs will eventually just be "how computers work." What will still matter are the interfaces you design around them.

PAI helps you design those interfaces today.