How I'd build Tour AI
I'd reach for a Next.js frontend with Postgres on the backend, Stripe for billing, Resend for transactional email, and Twilio for SMS confirmations to tour operators. I'd target 150 hours: roughly 4 weeks at full-time engagement, though the core product loop ships in 2.5 and the last week is monitoring, testing, and handoff.
Day-by-day plan
Day 1: Provision auth schema and multi-tenant architecture. Tour operators need isolated data, so I'd build row-level security in Postgres and a tenant context layer in Next.js that threads through every API route.
Day 2: Wire Stripe billing across the three pricing tiers. I'd implement subscription creation, webhook handling for upgrades and downgrades, and invoice PDF generation.
Days 3-4: Build the customer onboarding flow. TourAI's value prop only lands if operators understand what it solves, so I'd invest here: sign-up form, Stripe payment confirmation, initial tour data import wizard, and a guided walkthrough of the AI scheduling interface.
Days 5-6: Model the core tour domain. Tour, Booking, Operator, Guide, Vehicle. Build the API layer for CRUD and add the constraint-solving logic that assigns tours to available guides and vehicles while respecting operator preferences and regulatory limits.
Week 2: FareHarbor API integration. Their API is stable but conservative; I'd pull available tours, sync bookings, and post confirmations. Rezdy integration follows the same pattern.
Week 2.5: Email sequencing for the GTM motion. I'd template cold email sequences targeting specific operator types and seed the cold email list from ATTA directories.
Week 3: Testing, Sentry monitoring, deployment to Vercel and Railway, 24-hour observability setup.
What's hard about this build
The real complexity lives in multi-tenant constraint solving. Tour operators have wildly different rules: some enforce guide-vehicle pairings, others allow freelance guides, some have per-guide license restrictions. Building an optimizer that ships with sensible defaults but adapts to each customer's constraints without touching code will cost days. FareHarbor and Rezdy's API reliability is also a concern; they don't guarantee uptime, so I'd build retry logic and a fallback UI that caches tour data locally. Finally, seasonality affects product design: you can't charge annually if October through February is a ghost month. Stripe should be configured to allow easy pause and resume.
What's fast because of AI
AI compresses scaffolding and testing significantly. Claude generates the Postgres schema migrations, the CRUD API routes, and the React form components in an hour that used to take half a day. Writing tests for the constraint solver especially benefits from AI: naming edge cases like guide unavailable, vehicle double-booked, and seasonal license expiry is prompt work; Claude spins out parameterized test suites that catch gaps I'd normally find in production. Copywriting for the onboarding UI goes fast too: the value prop for scheduling optimization is abstract, but AI riffs on operator pain points and turns it into concrete UI copy. Debugging customer integration issues is faster because AI can ingest API logs, trace data flow, and suggest the fix before I've fully understood the problem myself.
How I'd hand it off
I'd record a 20-minute Loom walkthrough of both the operator dashboard and the internal admin panel. I'd leave a runbook covering deployment, environment variable setup, Stripe webhook configuration, and known monitoring thresholds for operator churn. I'd brief the on-call rotation and provide a 30-day pager window where I'm available for critical bugs. All API credentials go into a shared 1Password vault with rotation policies. The Linear board inherits all known issues, feature requests, and the roadmap for native competitor features.