How I'd build Gym AI
I'd reach for Next.js on the frontend, Postgres for the database, and Stripe for billing. The backend would be Node.js with Prisma as the ORM, Twilio for SMS retention campaigns, and custom webhooks for Mindbody integration. I'd use PostHog for product analytics and Resend for email. Rough estimate: 200-250 hours to production. That's four to five weeks of full-time build.
Day-by-day plan
- Day 1-2: Provision Postgres schema with multi-tenant support, auth flow with NextAuth, and role-based access control for SaaS founder, gym manager, and member views.
- Day 3: Wire Stripe billing for the three pricing tiers, including webhook handlers for subscription lifecycle events and failed payments.
- Day 4-5: Build the onboarding flow that asks for Mindbody API credentials, validates the connection, and syncs gym member data in daily batches.
- Day 6-7: Scaffold the core dashboard showing member churn risk scores, calculated via cohort analysis of last visit date and visit frequency trends.
- Day 8: Implement the retention campaign UI: draft SMS templates, schedule sends, track delivery and click-through rates via Twilio webhooks.
- Day 9: Build admin settings for gym managers to customize alert thresholds, campaign timing, and member segments.
- Day 10-11: Add billing portal, subscription management, and the 30-day trial onboarding experience. User acceptance testing with a real gym.
- Day 12: Deploy to Vercel, configure domain, scale Postgres read replicas, ensure automated backups run daily.
What's hard about this build
The Mindbody API is rate-limited and breaks occasionally. If a customer's member list syncs once at signup but never refreshes, you're billing them for stale data. I'd implement exponential backoff and daily refresh jobs with error alerts, but you're still dependent on Mindbody's API health. Gym owners using ClubReady or Zen Planner expect feature parity, so you're locked into building similar integrations or shrinking your addressable market. The second hard piece is churn risk scoring. Trainers and owners will scrutinize the math. A false positive damages trust fast. I'd version the scoring model and add explainability so owners understand why the system flagged someone as at-risk. The third risk is your customer base itself. Gym operators churn at 30-40% annually, which compresses your CAC payback window to six to nine months. You need upsells built in from day one, not bolted on later.
What's fast because of AI
Claude accelerates the entire scaffolding phase. I'd use it to generate the Postgres schema, NextAuth config, and Stripe webhook handlers, cutting what's normally two days of boilerplate into four hours. For the churn scoring logic, Claude helps me enumerate edge cases: vacation stretches, seasonal gyms, price hikes that shock the cohort. That enumeration is normally a week of back-and-forth with product. Here it's a 20-minute conversation. Test writing is the same. Claude generates unit tests for billing and data sync edge cases that I refine. On copy, Claude drafts SMS templates, dashboard microcopy, and onboarding flows. Normally I'd hire a copywriter for a week; instead I iterate with Claude in an afternoon. Finally, debugging: when the Mindbody integration breaks, Claude helps me parse the API error, trace the pipeline, and write a fix. That's probably a day of investigation saved per incident.
How I'd hand it off
I'd record a Loom walkthrough covering onboarding, the churn dashboard, campaign scheduling, and subscription management. I'd hand over a runbook documenting the Postgres schema, environment variables, API keys, and deployment steps. You'd get 30 days of pager rotation for any production issues. After 30 days, ops load should be near zero. I'd transfer all credentials (Stripe, Twilio, Mindbody OAuth apps) to your AWS account and ensure CI/CD runs via GitHub Actions. You own the codebase completely.