How I'd build Catering AI
I'd reach for Next.js with TypeScript on the frontend, FastAPI on the backend, PostgreSQL for the database, and Stripe for billing. Email goes through Resend, authentication through NextAuth.js, and I'd host the app on Vercel with a managed Postgres instance. Looking at the scope - auth, multi-tenant architecture, Stripe integration, customer onboarding, request management, and a functional dashboard - I'd estimate 350-400 hours, so roughly 9-10 weeks at a standard pace. That's the investment-to-production window.
Day-by-day plan
Day 1-2: Provision authentication schema, multi-tenant tenant model, and basic role-based access control. Set up Docker locally, a staging database, and Caddy reverse proxy for local HTTPS.
Day 3-4: Implement Stripe integration across the three pricing tiers. Handle webhook events for subscription updates, failed charges, and cancellations. Wire subscription state to the tenant model so feature access is gated correctly.
Day 5-6: Build the customer onboarding flow, including signup, email verification through Resend, initial profile setup, and the first post-login UX. This is the first impression.
Day 7-8: Scaffold the core feature: catering request intake and management. Forms for request capture, a dashboard to view pending requests, status tracking, and the ability to assign to team members.
Day 9: Implement transactional email notifications for request confirmations, status updates, and payment receipts using Resend templates.
Day 10-11: Add availability and scheduling. Calendar view, conflict detection when a single caterer is booked twice, and capacity planning across different types of services.
Day 12-13: Build the admin dashboard with analytics - request volume, conversion rates from inquiry to booking, customer LTV - and admin controls for managing accounts and support.
Day 14: End-to-end testing, stress-testing the payment flow, edge cases like timezone handling and concurrent request submissions. Performance profiling and optimization.
What's hard about this build
Multi-tenant data isolation is non-negotiable. A bug that leaks one catering business's customer data to another is a dealbreaker for legal and trust reasons. The scheduling system has to handle overlapping requests, team capacity constraints, dietary restrictions, and cancellations, all of which introduce hidden state-machine complexity. Seasonal churn is structural: catering goes quiet January-February and post-summer. I'd need to build retention hooks into the product itself. Catering owners often operate on Excel spreadsheets or legacy software, so migration path and integration with their existing workflow are the actual sales conversation, not the demo. Payment reconciliation for failed charges and refunds requires careful handling. None of this is technically novel, but corners cut here become customer problems later.
What's fast because of AI
Claude scaffolds CRUD endpoints and React components in minutes instead of hours - the patterns are predictable, just tedious. Test generation is where I see the biggest time savings: AI writes unit tests for data models, integration tests for payment flows, and edge-case tests I'd otherwise discover through customer bug reports. Database schema design benefits too; I describe the problem and get a normalized, indexed schema in one iteration instead of three rounds of refinement. UI copy and email templates are generated and edited down, faster than starting blank. Debugging becomes faster when Claude enumerates edge cases I haven't considered, like what happens if a Stripe webhook fires twice or a user submits a form twice rapidly. I'd estimate AI collapses a 14-15 day timeline into 9-10 days of actual work.
How I'd hand it off
I'd record a Loom walkthrough of the entire app - signing up, creating a catering request, admin analytics, team management - and document the runbook: how to run locally, deploy to production, handle common operational issues, and read the monitoring dashboards. You get 30 days of pager rotation where I'm on call for critical bugs or performance issues. I'd transfer all credentials for Stripe, Resend, Sentry, and the Postgres database, plus a Linear board with outstanding technical debt and feature requests ranked by customer impact. The codebase is yours to maintain or iterate on.