Case study

DPDP Readiness Tool

A short self-assessment for India's Digital Personal Data Protection Act, built in two days as an exercise in shipping a small, complete product quickly with AI.

Built for: My own learning: shipping a small, complete product end to end with AI
Time: 19 – 20 Aug 2026 (two days, 3 commits)
AI tools: Claude Code with Claude Fable 5
Stack: Next.js 16 (App Router), TypeScript, Tailwind CSS 4, deployed on Vercel

User stories

As a founder of a small business in India, I want to know in a few minutes how ready I am for the DPDP Act, so that I know where to start.
As that founder, I want my top three gaps and a first action for each, so that the result tells me what to do rather than just giving me a score.

Situation & constraint

A learning project. I work in security and compliance (my profile lists ISO 27001 and DPDP), and I wanted to see how quickly I could take a small idea from nothing to a deployed, working product with AI.

Architecture

A single-page app: a 15-question quiz, then a 0-100 readiness score, a readiness band and the three areas that cost the most points.
All quiz content (questions, weights, answer options, gap explanations, score bands) lives in one file, separate from the scoring logic, so the content can change without touching code.
Scoring is a pure function: a weighted sum, with partial answers earning half the weight and the top gaps picked by points lost.
A server route can send submissions to a Google Sheet through a webhook, and the share image is generated by the app itself.