Building a SaaS App with Claude Code

At Jetpack Labs, we help startup founders turn their ideas into actual working products. Fast. And lately, we’ve been adding AI into our dev process to speed things up even more.

We wanted to see what it would take to build a simple SaaS app using Claude Code, Anthropic’s coding-focused AI, and put it through a real-world test. Not a toy demo. Something practical.

So we built a minimal note-taking app called Meemo. Think: markdown support, hashtags, pinning, and a simple UI. Just the essentials.

Here’s what we learned, the good, the buggy, and the parts where AI still needs a little human help.

Why AI Has Changed the SaaS Game (But Doesn’t Replace Devs)

A few years ago, building an app like this would take a full sprint or more. Now, with tools like Claude Code or ChatGPT, we can:

• Write boilerplate code in seconds
• Debug faster by pasting error messages
• Automate simple UI elements
• Document features on the fly

But here’s the catch: AI doesn’t think like a dev. It doesn’t see how parts connect unless you guide it. It doesn’t always write secure or scalable code. And if you ask it to fix a bug? It might break something else in the process.

That’s where real engineering skill still matters.

What Founders Are Struggling With Right Now

We’ve worked with a lot of early-stage founders. Here’s what we’re seeing over and over:

The Rise of Vibe Coding

There’s a growing trend of developers building by “vibe”, skipping specs, planning, and architecture in favor of intuitive, just-go-with-it development. It feels fun and fast… until it collapses under pressure.

Vibe coding is a style where developers rely on intuition, real-time feedback, and fluid, often undocumented changes. It’s common in early-stage indie hacking and fast prototyping. And it can work, in the short term. But the tradeoffs often show up later:

We’re seeing more and more founders who built something quickly, often in a weekend, only to find it can’t be extended, isn’t secure, or behaves unpredictably.

The vibe felt good. The reality didn’t scale.

We’ve cleaned up dozens of apps built too fast with AI or vibe coding. And we’ve learned a lot from those post-mortems.

Our Process: Building a SaaS App with AI

Step 1: Start with a Clear Product Requirements Doc

AI works better when you’re specific. So instead of jumping into code, we started by creating a product requirements document (PRD). We asked ChatGPT to help us define the features for what would become Meemo. Then we had it write a PRD based on our requirements.

It included:

• Core features (notes, markdown, pinning)
• User flow
• Sitemap
• Technical overview
• UI/UX suggestions

That gave us a shared vision. Then we got to building.

Step 2: Feed Features Into Claude Code (One at a Time)

We asked Claude to generate the core features. But we learned quickly: don’t ask for too much in one go.

”The goal is to guide AI with precise, focused tasks, not expect it to understand the full architecture on its own.”

Claude works best when:

• You give it focused tasks
• You explain the context clearly
• You paste in errors or logs when things break

For example, we asked Claude to add a modal for editing a note, with a cancel button and escape-key support. It got halfway there, but made the cancel button stop working. Turns out, it treated the escape key and cancel button as separate flows.

Once we explained they should trigger the same function, it worked. But we had to connect the dots.

Step 3: Use AI to Debug, but Watch for Side Effects

One of the best parts of using Claude Code was fixing bugs fast. When something broke, we pasted in the error message and got a fix back quickly.

But there’s a catch.

Sometimes, the fix created a new bug.

This is a common issue with AI coding. It doesn’t always understand the full context of your app. So when you ask it to fix a small piece, it might touch a bigger area.

Tip: Always test everything after a bug fix, not just the thing you fixed.

Step 4: Performance Problems at Scale

After adding some basic features, we created a seeder to simulate 200 notes for one user. We wanted to see how it held up with real data.

That’s when we noticed something: “One page was making 50 redundant SQL queries.”

That’s a red flag.

Claude hadn’t optimized for eager loading. Each note was triggering its own query. That’s fine with 5 notes, but at 200, things slow down fast.

This is one of the biggest risks of using AI to build apps: it doesn’t think about performance unless you ask it to.

Step 5: Create a Rules File to Train Your AI Dev

One idea we’re exploring now is creating a “rules file” for Claude.

Stuff like:

• Always eager load relationships
• Use caching for expensive queries
• Consistent naming conventions
• Shared styles for UI elements

We think if we feed this into Claude at the start of each project, we’ll get better results. Kind of like training a junior dev on day one.

What We Learned: AI Strengths and Limitations

What AI Did Really Well

✅ Generating boilerplate code fast

✅ Helping us fix bugs quickly

✅ Creating UI components like modals and forms

✅ Explaining errors in plain language

Where AI Fell Short

⚠️ Didn’t optimize database queries

⚠️ Created side effects when fixing bugs

⚠️ Struggled with context across multiple features

⚠️ Didn’t consider scalability or security by default

Final Thoughts: Use AI to Go Fast, But Don’t Skip the Fundamentals

We got a working MVP of our note-taking app, Meemo, done in a weekend. That’s huge. A few years ago, that would’ve taken a sprint or two.

But it wasn’t perfect. We had to:

• Manually debug some features
• Refactor queries for scale
• Watch out for regression bugs

If you’re a founder building with AI, remember:

• Start with a clear spec
• Break down features into small parts
• Test everything, even unrelated features
• Review the code for performance and security

AI makes it faster, but you still need product thinking and engineering skills to make it solid. You can check out the result of our work at meemo.app, where we focused on building a clean, intuitive note-taking experience with enterprise-grade encryption and hashtag organization.

Are you ready to build a SaaS app with AI that won’t fall apart at scale? At Jetpack Labs, we can help you create a product that not only works but is also secure and maintainable. Contact us today to get started on your journey to building better software with AI assistance.