How I'd build Salon AI
I'd reach for Next.js on the frontend, FastAPI for the backend, Postgres for multi-tenant relational storage, Twilio for SMS delivery, Claude API for personalized message generation, and Stripe for subscription billing. From scaffold to production-ready code, I'm looking at 140-160 hours at $75/hr, plus another 40 for infrastructure setup and TCPA compliance documentation.
Day-by-day plan
Day 1: Provision Postgres multi-tenant schema, auth tables, organizations and users. Day 2: Wire Stripe integration with three pricing tiers ($89, $99, $119), test webhook handlers for subscription events. Day 3-4: Build Next.js dashboard, session management, JWT auth, onboarding wizard for salon setup. Day 5: Integrate Vagaro, Mindbody, and Boulevard booking APIs using OAuth, build adapter pattern for API differences. Day 6-7: Build the rules engine for rebooking triggers and upsell detection (client ghosted, time-sensitive services). Day 8: Integrate Twilio and Claude API for personalized SMS generation, test message quality across salon types. Day 9: Build TCPA compliance layer: opt-in audit trail, timestamp logging, client consent verification. Day 10: End-to-end testing, Sentry error tracking, staging environment, deploy to production.
What's hard about this build
The booking system integrations are the biggest risk. Vagaro, Mindbody, and Boulevard all expose different APIs, have rate limits, and sync data on different cadences - some webhook-driven, some polling-required. I need adapter classes for each, plus circuit breakers and exponential backoff. TCPA compliance is non-negotiable: documented opt-in must be tied to each client phone number, stored in Postgres, with an audit trail. Missing this means your salon client gets fined and we're the vendor they name. Third risk is message tone. Salon owners want messages that sound like their salon, not generic bot copy. That means iterating Claude's system prompt, testing across ten salon types, and building a dashboard UI where salon owners can preview and tweak messages before I send them.
What's fast because of AI
Claude scaffolds the FastAPI models, Postgres migrations, and Stripe webhook handlers in 30 minutes where I'd normally spend two hours on boilerplate. For testing, Claude generates the test cases for Twilio SMS delivery, Stripe billing reconciliation, and edge cases I'd miss on first pass - timestamp timezone bugs, concurrent requests, race conditions during client deletion. The hardest part, Claude prompt engineering for message generation, moves from five iterations to one working prompt. During code review, Claude helps me enumerate gotchas: what if the salon deletes a client while a reminder is queued, what if two upsell rules fire for the same appointment, what if Mindbody's API is down during sync. Debugging gets faster too - a weird timezone issue that would take an hour to trace takes 15 minutes because Claude can enumerate the likely causes.
How I'd hand it off
I leave a Loom walkthrough of the admin dashboard, a Markdown runbook covering deployment, Twilio secret rotation, and how to add new booking systems. All Postgres migrations are version-controlled in the repo with rollback scripts. Secrets live in your 1Password or AWS Secrets Manager, never in code. CI/CD is GitHub Actions: tests run on every push, only merged code deploys to production. I do a 30-day pager rotation for production fires - outages, compliance issues, Twilio failures. After that, you own it.