I built Materia AI to solve a real problem I saw firsthand — messy menu and inventory management from my years working as a waiter. It's still growing. But somewhere along the way, I realized the backend pattern I kept reusing — the FastAPI structure, the AI service layer — was useful on its own, separate from the specific product.
So I pulled it out and packaged it. Here's what that process actually taught me.
Cutting scope until it's honest
My first instinct was to make the package look impressive — throw in auth, a database layer, the works. Then I stopped and asked: does the code actually have that? It didn't. So I cut the description down to exactly what exists, nothing more. It felt like a downgrade in the moment. It's actually the only version I'd feel fine standing behind if someone asked for a refund.
Writing the README for a stranger, not for future-me
My own project notes assume I remember context. A README for someone who's never seen the code needs to explain why, not just what — why routers stay thin, why the AI call lives in its own service, why the response gets parsed defensively instead of trusted blindly. Writing that down clarified my own thinking more than I expected.
The code isn't the product — the packaging is
Anyone could write similar FastAPI code in an afternoon. What they're actually paying for is not having to make the fifty small decisions I already made and documented. That reframing changed how I wrote everything, from the file structure down to the comments.
Starting cheap on purpose
I priced it low to start. Not because the pattern isn't worth more, but because the real goal right now is learning what resonates before optimizing for revenue. Feedback first, pricing later.
None of this is groundbreaking business advice. It's just what actually happened, in order, the first time I tried it.
If you want to see what came out of it, it's here.
I'm a backend developer working in Python, FastAPI, and AI integrations, currently open to freelance work — find me on GitHub.
