I launched my first AI SaaS product eight months ago. It’s a tool that reads SEC filings and generates plain-English summaries for retail investors. Revenue: $4,200/month. Users: about 340 paying subscribers. Time to build the MVP: 12 days.
I’m not telling you this to brag — $4,200/month is not quit-your-job money. I’m telling you because eight months ago I’d never built a SaaS product, I’m not a particularly gifted developer, and the entire technical stack is essentially a well-designed wrapper around Claude’s API. If I can do it, the barrier to entry is genuinely low.
Here’s everything I learned, including the mistakes.
Finding the Idea (Stop Overthinking This)
I wasted two months brainstorming “the perfect AI SaaS idea.” I read articles about market size and TAM and competitive moats. None of that mattered.
What mattered: my father-in-law called me one Saturday asking what a 10-K filing meant. I spent 30 minutes explaining it to him. Then I thought: how many retail investors have the same question but no one to call?
That’s it. That’s the whole ideation process. Someone had a problem. The problem was solvable with an LLM. I built the solution.
The best AI SaaS ideas aren’t clever. They’re obvious in retrospect. Find a workflow where someone spends hours doing cognitive work that an LLM can do in seconds. Build a product around that workflow. The end.
The trap to avoid: “AI-powered [generic thing]” is not a product. “AI writing tool” is a feature. “AI tool that generates real estate listing descriptions from property photos and specifications, formatted for Zillow and Realtor.com” is a product. The specificity is the product.
Technical Architecture (Keep It Boring)
My tech stack: Next.js frontend, Node.js backend, PostgreSQL database, Claude API for the AI, Stripe for payments, Vercel for hosting. Total monthly infrastructure cost: $87.
That’s it. No microservices. No Kubernetes. No vector database (I added one later when I needed RAG for historical filing comparison, but the MVP didn’t have it). No fancy orchestration framework.
I see founders building complex AI architectures before they have their first user. They spend months on LangChain workflows, agent orchestration, and fine-tuning pipelines. Then they launch and discover that their users just want a text box and a “Go” button.
Build the simplest thing that solves the problem. You can add complexity later when you have users telling you what they actually need.
The AI layer was literally: take the SEC filing text, send it to Claude with a carefully crafted prompt, stream the response back to the user. The prompt engineering took two days. The rest of the 12-day MVP was auth, payments, and making it look not-terrible.
The Prompt Is Your Product
Here’s something that took me too long to realize: for most AI SaaS products, the prompt IS the product. Not the infrastructure. Not the framework. Not the database schema. The prompt.
I spent two days crafting and testing my summarization prompt. I went through about 40 iterations. The final prompt includes specific instructions about what to emphasize (material risks, revenue changes, forward-looking statements), what format to use (bullet points for key takeaways, narrative for overview), and what language level to target (avoid jargon, explain technical terms).
That prompt is what makes my product different from “paste a 10-K into ChatGPT.” The UX is a nice wrapper, but the prompt is where the actual value lives.
Version your prompts. A/B test them. Track which prompts produce outputs that users rate highest. Treat prompt engineering like product development, not a one-time setup.
Pricing (Charge More Than You Think)
I launched at $9/month. Users signed up. I raised it to $19/month. Users still signed up, at almost the same rate. I should’ve started at $19.
My cost per user per month is about $2.50 in API calls (the average user summarizes about 8 filings per month). At $9/month, my margin was 72%. At $19/month, it’s 87%. Same effort, same product, nearly double the revenue.
The lesson: AI products deliver enormous value relative to their cost. A retail investor who can understand SEC filings in 5 minutes instead of 2 hours would happily pay $19/month. Many would pay $49/month. I was pricing based on my costs instead of my users’ value. Classic mistake.
The Mistakes I Made
Building features nobody asked for. I spent a week building a “filing comparison” feature before launch. Zero users have used it in eight months. I should’ve launched a week earlier instead.
Not talking to users early enough. I built for two weeks before showing it to anyone. When I finally did, the first three people said “this is great, but can it highlight the risks specifically?” That became the most popular feature — and I could’ve known about it on Day 1 if I’d asked.
Ignoring latency. My first version sent the entire filing to Claude and waited for the complete response before displaying anything. Users stared at a loading spinner for 30-45 seconds. Switching to streaming responses (showing the summary as it’s generated) dramatically improved the perceived experience. Latency matters more than you think.
Underinvesting in onboarding. My first users landed on the dashboard and didn’t know what to do. Adding a simple “paste a ticker symbol to get started” prompt with a sample filing increased activation from 40% to 78%.
What I’d Do Differently
Start with 10 paying beta users before building anything. Charge them $29/month. Use the conversations to shape the product. Build only what those 10 people need. Launch publicly when 8 out of 10 say they’d be upset if you took the product away.
This approach would’ve saved me two weeks of building features nobody wanted and given me a product that was more precisely targeted from day one.
AI SaaS is the most accessible business opportunity in tech right now. The tools are mature, the costs are low, and the demand is real. The hard part isn’t the technology — it’s finding a specific problem and having the discipline to solve it simply.
🕒 Last updated: · Originally published: March 15, 2026