How I'd build Pet-ai
I'd reach for Next.js as the frontend and API layer, Postgres for the database, Stripe for billing, Twilio for SMS reminders, and Resend for transactional email. The real complexity is multi-tenant data isolation and PMS integrations, not the core booking engine. Rough estimate: 150-180 hours of focused development to ship something defensible that handles the core motion - the 20 demo requests per month, onboarding flow, booking widget, basic SMS/email, and integration with at least Gingr and PetExec. That's 4-5 weeks if we're disciplined about scope.
Day-by-day plan
Day 1: Provision auth schema using Supabase or custom JWT, multi-tenant account model, row-level security policies so shop A can't see shop B's data.
Day 2: Stripe integration for the three pricing tiers, webhook handlers for subscription lifecycle, invoice generation.
Day 3-4: Customer onboarding flow, tenant configuration screen, initial admin dashboard shell.
Day 5: Build the booking widget (iframe-embeddable) that captures customer name, pet info, preferred time, and routes to shop calendar.
Day 6: Wire the after-hours buffer logic: flag bookings that arrive between 6 PM and 8 AM as "after-hours" and trigger a priority email to the shop owner.
Day 7-8: Start Gingr integration using their API to pull existing appointments and availability windows; sync bookings back into their system.
Day 9-10: PetExec integration, repeat the pattern. Different schema, different rate limits, same outcome.
Week 3: SMS notifications via Twilio for booking confirmations and 24-hour reminders. Email via Resend for the shop owner alerts and customer confirmations.
Week 4: Analytics dashboard showing demo-to-close metrics, booking volume by hour, churn cohorts. Add Sentry for error tracking, write tests for the PMS sync logic, polish the onboarding copy, deploy to production on Railway or Fly.
What's hard about this build
Multi-tenant data isolation in Postgres is straightforward in theory but easy to botch in practice, especially under deadline pressure. The PMS integration tax is real: Gingr's API returns availability differently than PetExec, so every integration requires custom logic to normalize bookings and availability into our canonical model. Conflict detection is non-trivial - we need to prevent double-booking even when the shop is managing bookings in two places simultaneously (our widget and their PMS directly). Payment reconciliation when a customer cancels and Stripe refunds is a state-machine problem we can't skip. The legal side is smaller than I'd expect but still present: shop liability for booking errors, GDPR compliance on pet owner data if they expand to EU, and ensuring we're not liable for a booking we couldn't fulfill.
What's fast because of AI
Claude accelerates the scaffolding: I feed it a schema, ask for the Next.js API routes, and get 80% of the CRUD done in minutes. Writing tests for the PMS sync logic is much faster when I ask Claude to enumerate edge cases: what happens if the shop has two bookings at the same time in Gingr but we only see one. Onboarding copy, button text, and email templates come faster from Claude than from me trying to sound folksy and non-corporate. Database schema exploration is a conversation rather than a whiteboard - I ask "what if we model cancellations as a separate table," Claude spots the referential integrity issue, and we iterate. Debugging integration failures is faster when Claude helps trace a Gingr API response through our normalization logic and spots the null check we missed.
How I'd hand it off
I'd record a 15-minute Loom for each major feature: how the booking widget is embedded, how to configure a new PMS integration, how Stripe billing flows. I'd write a runbook in a shared Notion: setup instructions, environment variables, how to deploy to production, how to handle a Stripe webhook failure. I'd schedule 30 days of on-call time post-launch; you own the first 14 days, I own days 15-30. I'd document every credential in a password manager, transfer all Linear issues to you, and leave an audit trail in the commit messages so the next engineer can understand the "why" behind the data model.