AI web MVP · Gemini + Supabase
AI Readiness Assessment
A public pilot of an assessment that turns structured answers into an AI-readiness result — scoped to stay small enough to test quickly.
Current status: Public repository and demo landing are live. The AI evaluation flow was not re-tested for this case.
Problem
A useful AI-readiness assessment has to do more than display a questionnaire: it must turn structured input into a clear result while keeping the MVP small.
What was built
- Defined which inputs were actually necessary and how to structure the assessment flow.
- Decided where AI should interpret the answers and what the result should communicate.
- Repository-described journey: questionnaire → structured data → Gemini analysis → readiness result.
- Postponed everything that was not needed for the first testable version.
What it demonstrates
- Ruthless scope control for a first version
- A public, inspectable repository and demo
- Evidence-safe presentation of AI-assisted work
My role
- Product framing
- Scope prioritization
- Delivery coordination
Stack / methods
Links
Evidence boundary
Implementation is AI-assisted. The stack is as described by the repository. No build-time, user or business-outcome claims are made.