AI-Powered SaaS MVP
Part of the AI Engineer Roadmap
In short
A clever AI script is a demo. Add sign-up, saved history, usage limits and a deployed frontend, and it becomes a product people can use. It proves you can ship the boring parts too.
A small but complete product with AI at its core, real authentication, a database, and a deployed frontend — proves you can ship an AI feature inside a real product, not just a script.
Think of it like this
Starting a tiffin service
Great dal isn't enough to run a tiffin service. You need a list of customers, a way to take orders, delivery, payment and a limit on how many tiffins you can cook a day. Your AI model is the recipe. The SaaS around it is everything else that makes it a business.
Watch it happen
One request in the AI notes summariser
POST /api/summarise
1/7A signed-in student pastes lecture notes. The session cookie goes along with the request.
The big ideas
What you need to know
Real auth from day one
Use an auth library or service rather than rolling your own. Every row in the database belongs to a user.
Usage limits protect your wallet
Each AI call costs money. Daily limits per user and capped input size stop one user draining your budget.
Keys stay on the server
Call the LLM from your backend only. A key in frontend code can be copied by anyone.
Real users, real feedback
Get five friends to use it for a week. Their confusion is the most useful data you'll collect.
See it in code
Walk through the code
1export async function POST(req: Request) {2 const user = await getUser(req); // from your auth library3 if (!user) return Response.json({ error: "Sign in first" }, { status: 401 });4 5 const used = await db.usage.count({ where: { userId: user.id, day: today() } });6 if (used >= user.dailyLimit) {7 return Response.json({ error: "Daily limit reached" }, { status: 429 });8 }9 10 const { text } = await req.json();11 const summary = await summarise(text.slice(0, 20_000)); // cap input size and cost12 await db.usage.create({ data: { userId: user.id, day: today() } });13 await db.summary.create({ data: { userId: user.id, text, summary } });14 return Response.json({ summary });15}1/4No signed-in user, no AI call. Return 401.
A Next.js route with three checks every AI product needs: who are you, are you within your limit, is the input a sane size. getUser, db and summarise are your helpers.
Save yourself the pain
Common mistakes
Oops
Calling the LLM API straight from the browser with the key in your frontend code.
Do this instead
Route every AI call through your backend. Keep the key in a server-side environment variable.
Oops
Spending three weeks on a landing page before the core feature works.
Do this instead
Build the one AI feature, auth and history first. Polish comes after someone has used it.
Your mission
Milestone one: signed-in AI feature
Quick check
Where should the call to the LLM API happen in your SaaS?
Go deeper
- 1Scope it tightly: one AI feature done well beats five features half-built.
- 2Include real auth, persistence and a usable UI, not just an API endpoint.
- 3Track and display usage/cost so the AI feature feels production-aware, not a toy.
More projects at this stop
End-to-End ML Pipeline with Deployment
RAG Chatbot with a Custom Knowledge Base
Fine-Tuned LLM for a Niche Domain
AI Agent that Automates a Real Workflow
Computer Vision App (e.g. Defect Detection)
Multi-Modal AI Application
Kaggle Competition Submission
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