The Vision

I've been thinking about this for a while now.

What if you could build a software company where AI does most of the work? Not as a gimmick. Not as a demo. As the actual way the company operates.

I'm not talking about using ChatGPT to write emails. I'm talking about AI agents that monitor your servers, fix bugs, talk to customers, write documentation, manage releases. The whole thing.

Here's what I keep seeing

Every week there's a new AI tool. A new copilot. A new assistant. And they're all amazing in isolation. Claude can write code at the same level as the developers I've worked with. It can debug problems I'd spend hours on. It can explain complex systems like it built them.

But then what?

You close the chat. You copy-paste the code. You go back to your normal workflow. The AI helped, sure. But it didn't do the work. You still did.

That's the gap I want to close.

The bet I'm making

What if instead of AI as a tool, you had AI as a team?

Not replacing humans. Augmenting them. A founder who sets the vision, makes the hard calls, handles the truly novel problems. And an AI team that handles everything else.

• An AI CTO that monitors the codebase, catches errors before users do, reviews every deploy
• An AI CFO that tracks revenue, manages billing, spots anomalies
• An AI CMO that writes content, manages campaigns, talks to the market
• An AI support lead that handles tickets, escalates the hard ones, learns from every conversation

Eight executives. All AI. All coordinated. All working 24/7.

That's Brainz Lab.

Why this might actually work now

I've tried versions of this before. They didn't work. The AI wasn't good enough. It would hallucinate. It would miss context. It would do something confidently wrong.

But Claude is different. Not perfect—nothing is. But good enough that I trust it with real work. I use Claude Code every day. It's not a novelty anymore. It's how I build.

The missing piece was always infrastructure. Claude can debug an error, but only if it can see the error. It can fix a bug, but only if it can access the code. It can respond to a customer, but only if it knows what they're asking about.

So that's what we're building first. The tools. The eyes and ears and hands that AI needs to actually do the work.

31 products

Yeah, I know. That sounds insane.

But here's the thing—they're not 31 separate products. They're one system. Error tracking that feeds into logging that feeds into metrics that feeds into alerts. All connected. All designed for AI consumption.

When FORGE (our AI CTO) sees an error, she doesn't just see a stack trace. She sees the logs leading up to it, the metrics around it, the last deploy that might have caused it, the customers affected by it. Full context. Everything she needs to actually fix it.

That's why we're building all of this. Not because we want to compete with Sentry or Datadog or Stripe. Because we need infrastructure that AI can actually use.

Building in public

I'm going to document everything here. The decisions, the failures, the breakthroughs. Why we chose Rails. Why we structured the products this way. What worked. What didn't.

Partly because I think others are trying similar things, and we should share notes.

Partly because I want accountability. If I'm going to claim AI can run a company, I should prove it.

And partly because this is genuinely new territory. I don't know if it'll work. But I think it's worth finding out.

What's next

Right now, I'm building the foundation. The landing page you're reading this on. The first few products—Reflex for errors, Recall for logs, Pulse for metrics. The platform that ties them together.

No AI executives yet. They come later, once they have something to work with.

But the vision is clear: a software company that runs itself, with a human at the helm making the calls that matter.

Let's see if we can build it.

— Andres