AI in Action: Building RetireCheck with Cursor

RetireCheck started as a practical question: can AI-assisted development ship a production financial tool — with real domain rules, tests, and a deployable architecture — without becoming a demo that falls apart under edge cases?

The answer, for this project, was yes — but only when AI accelerated scaffolding and exploration while architecture, domain modeling, and verification stayed engineer-owned.

What we were building

RetireCheck is a US retirement planning calculator: a four-step wizard covering income and expenses, Social Security claiming context, tax/RMD projections, and Monte Carlo portfolio outcomes. It returns estimates with clear disclaimers — useful decision support, not advice.

That product shape forced real engineering constraints: calculation correctness, honest UX copy, and a stack that could evolve without burying financial math inside React components.

Architecture before prompts

The first durable decision was not a model choice — it was boundary design. Financial math lives in a pure C# domain layer. An ASP.NET Core API exposes that domain. Next.js owns the wizard UX, charts, and presentation. Tests sit against the domain and API, not only the UI happy path.

That separation made AI assistance safer. Cursor could help generate UI flows, API wiring, and boilerplate while the domain remained a place where correctness could be reviewed and tested.

How AI actually helped

• Faster iteration on wizard steps, empty states, and chart presentation without losing product focus.
• Quicker exploration of API contracts and TypeScript/C# handoffs once the domain model was clear.
• Helpful first drafts for docs, disclaimers, and case-study structure — then edited for accuracy and tone.

What AI did not replace: deciding what belonged in the domain layer, which projections were honest enough to ship, and when a calculation needed another test instead of another prompt.

What “production-ready” meant here

For RetireCheck, production-ready meant more than a working demo: a monorepo with clear ownership, deployable frontend and API, automated tests around the math, and UI that states its limits. Those choices are what make an AI-assisted build feel like engineering instead of generated theater.

Takeaways for the series

1. Define domain boundaries before asking AI to fill the gaps.
2. Keep calculation-critical code reviewable and testable — not trapped in the UI.
3. Use AI for speed; keep architecture, judgment, and verification human-owned.
4. Ship with disclaimers, CI, and docs when the product touches real-world decisions.

RetireCheck is the first public product in the AI in Action series. The next apps will follow the same rule: practical engineering first, AI as acceleration — not as a substitute for ownership.