How I'd build Campground AI
I'd reach for Next.js for the front end, FastAPI for the backend, Postgres for the core database, and Stripe for billing infrastructure. Twilio handles SMS notifications, Resend for email delivery, and Redis for task scheduling around reservation reminders and monthly reports. I'm estimating 420-480 hours to production MVP, roughly 11-12 weeks at standard pace.
Day-by-day plan
- Day 1-2: Set up multi-tenant Postgres schema, user authentication with JWT and refresh tokens, basic RBAC for operators versus customers
- Day 3: Build the reservation creation and cancellation APIs with optimistic locking for conflict detection
- Day 4: Integrate Stripe, implement the three pricing tiers (monthly per-site fee, per-reservation transaction fee, seasonal overage billing)
- Day 5-6: Ship the customer-facing booking UI and operator admin dashboard with a real-time availability calendar
- Day 7: Integrate Twilio for SMS confirmations, cancellation notifications, and check-in reminders
- Day 8: Build the revenue dashboard with per-campground analytics: occupancy rate, seasonal trend visualization, refund tracking
- Day 9: Set up Resend for automated monthly billing statements and churn-prevention reminder emails
- Day 10: Deploy to Vercel for the frontend, containerize the FastAPI backend for AWS ECS, set up Sentry for error tracking
- Day 11-12: Run integration tests across the Stripe, Twilio, and Resend workflows, load test the booking endpoint under concurrent spikes, finalize documentation
What's hard about this build
Concurrent reservation requests are the real risk. Two bookings arriving within milliseconds of each other for the same dates on the same site will cause a double-booking if you're not careful with database locking. I'd use Postgres row-level SELECT FOR UPDATE to prevent it, but it's the kind of production-only failure that no amount of unit testing catches. Second: data migration from Campspot or ResNexus is messy. They don't publish their schemas, so you're either asking customers for CSV exports or reverse-engineering from sample data. Third: refund logic compounds when you layer in Stripe's API. A reservation cancelled 24 hours before check-in has different refund terms than one cancelled a month out, and you have to reconcile that against what Stripe actually processed. I'd enforce a clear policy and code it strictly to avoid operator support overhead on gray areas. Fourth: seasonal churn is structural. Half the country closes from October through March, so MRR is inherently lumpy and new customer acquisition flatlines for five months.
What's fast because of AI
Claude accelerates the parts that are routine and error-prone. I use it to scaffold the 12-15 CRUD endpoints for reservations, sites, and billing records, then audit for security and consistency. Generating test suites for edge cases, concurrency scenarios, and refund boundaries cuts my testing time by half. UI copywriting is another force multiplier. Instead of me drafting a billing explanation three times before it's clear, Claude drafts it in minutes and I edit it. Debugging is faster too. When something breaks in production, Claude helps me trace request flows through logs and suggest fixes I might not reach for immediately. Integration work with Twilio, Stripe, and Resend moves faster because Claude reads their docs and scaffolds the client code. That compression across scaffolding, testing, copywriting, and debugging probably saves me 50-60 hours.
How I'd hand it off
I'd record a Loom walkthrough of the full operator workflow: creating a new campground, setting availability windows, reviewing bookings, processing a cancellation, checking the revenue dashboard. I'd write a runbook in Linear documenting common operations: adding a new site, debugging a failed Stripe charge, bulk-importing historical reservations. The codebase lives in GitHub with a thorough README covering the data model, environment variables, and deployment steps. I'd transfer database credentials and API keys via 1Password shared vault. If you want 30 days of on-call support, I can stay in Slack handling production alerts and incidents. After the handoff period, I'm available for 10 hours per month of advisory work at the same rate.