Build an AI SaaS

What you'll learn

1. 1

   Pick an AI Feature People Pay For

   Find a problem worth solving with AI, and check the numbers before you build.

   • Start with the pain, not the model
   • Strong or weak idea?
   • Do the unit economics
   • Your moat
   • The $10 plan that cost $20

   Take-home template: AI SaaS Idea Scorecard & Unit Economics

2. 2

   Call AI From Your App, Safely

   The backend pattern every AI product uses: keys on the server, validation, clean errors.

   • The browser never talks to the model
   • Find the leak
   • Inside your endpoint
   • Write the endpoint

   Take-home template: Secure LLM Endpoint (FastAPI)

3. 3

   Protect User Data

   What you can send to a model, what to redact, and how to earn trust with real customers.

   • Every prompt is data leaving your app
   • Send, redact or never?
   • Build a PII redactor
   • A customer asks
   • Source code in a chatbot

   Take-home template: PII Redaction Module

4. 4

   Usage Limits & Pricing That Cover AI Costs

   Quotas, rate limits and plans that keep heavy users from eating your margin.

   • Every click costs you money
   • Set a fair quota
   • Write a daily quota check
   • The heavy user

   Take-home template: Quotas & Rate Limits (Postgres/Supabase + Python)

5. 5

   AI UX People Trust

   Design patterns that make AI features feel fast, honest and in the user's control.

   • Great AI products feel in control
   • Spot the risky copy
   • Good or bad AI UX?
   • Turn 👍/👎 into a decision

   Take-home template: AI UX Checklist

6. 6

   Launch, Measure, Improve

   Ship a small first version, track the numbers that matter, and improve every week.

   • Ship small, then learn fast
   • From idea to launch
   • Build your launch dashboard
   • You can build this now

   Take-home template: AI SaaS Launch Checklist & Weekly Metrics