From Idea to AI SaaS: What an MVP Really Needs
An MVP isn't a smaller product. It's the smallest test of your biggest assumption.
AirBrain Team 7 min read
Step one: the assumption
Every product rests on a bet. For example: "Property managers will trust an AI assistant to draft tenant replies." Your MVP exists to test that bet as cheaply as possible.
What belongs in the MVP
| Include | Why |
|---|---|
| One core workflow that delivers the value | It's the thing you're testing |
| Sign-up and sign-in | Customers need accounts |
| Multi-tenant basics (each customer's data kept separate) | Painful to retrofit later |
| Role-based access (simple roles) | Trust and security from day one |
| Basic analytics (what customers do) | Learning is the point |
| A way to charge, even if manual at first | Willingness to pay is a key signal |
| Human review points for AI outputs | Quality and safety |
What can wait
- Advanced settings and customisation
- Many integrations (pick the one customers need most)
- Fancy dashboards
- Complete automation of billing edge cases
- Every feature a competitor has
Foundations that are hard to add later
- 1Data separation between customers. Design for it now.
- 2Permissions and roles. Even simple ones.
- 3Usage tracking. You can't price what you can't measure.
- 4Logging and evaluation of AI outputs. You need to see quality to improve it.
Common traps
- Building for everyone. Pick a narrow first customer.
- Perfecting the AI before testing the problem. Test demand early.
- Ignoring running costs. AI usage has a cost per use. Understand it before you set prices.
- No feedback loop. Plan how users tell you what's wrong.
A sensible sequence
Ideate, validate with real customers, build the MVP, launch to a small group, scale what works. Timelines depend on scope and complexity, so be wary of anyone who promises one without a discovery session.
Have a workflow in mind?
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