How I'd build Moving AI
I'd reach for FastAPI on the backend with Postgres for the database, Next.js for the frontend, and Stripe for billing. Twilio handles SMS dispatch notifications, Resend for email, and Claude's API for quote generation and follow-up sequencing. I'm estimating 350-400 hours at current pace with AI assistance - roughly 6-8 weeks of full-time work if nothing breaks unexpectedly.
Day-by-day plan
Day 1-2: Provision Postgres multi-tenant schema, build basic auth with Supabase, set up the core SaaS tenant model so each moving company sees only their own data.
Day 3-4: Wire Stripe billing. Three tiers: Starter ($99/mo for quote only), Professional ($329/mo with follow-ups and basic dispatch), Enterprise ($799/mo with crew app integration). Test payment flows and invoice generation.
Day 5-6: Build the customer onboarding flow. Sales team uploads a CSV of leads, system validates phone/email, stages them for AI outreach.
Day 7-8: Integrate Claude API for smart quote generation. Feed the model job details (size, distance, item list, access difficulty), get structured quote with labor estimate and materials markup. Store quote history so we learn from accuracy over time.
Day 9-10: Build automated follow-up sequences. If no response in 24 hours, send SMS. If no SMS open in 48 hours, send email. Track engagement and let the operator see which leads are hot.
Day 11-12: Build the dispatch dashboard. Crews see assigned jobs in the Next.js app or via SMS, can mark jobs as accepted/completed. Estimator gets real-time visibility of who's booked and capacity left.
Day 13-14: Admin panel for the moving company owner. Revenue dashboard, crew performance metrics, quote-to-close conversion rate, integration with their existing phone system via Twilio.
Day 15: Deployment, env setup, monitoring with Sentry.
What's hard about this build
Quote liability is the real teeth here. If Claude overestimates labor and the company wins on price, they lose margin. If Claude underestimates, the crew shows up to a 4-bedroom house with a piano and the owner loses money or customer trust. We need to validate quotes against historical job data, but most moving companies don't have clean historical databases. Integrating with MovingPro or HouseCall Pro's APIs is messy - their quote modules already exist, and asking an owner to run two systems kills adoption. Crew adoption is a second hard problem. Blue-collar workers don't open apps. We need SMS-first dispatch and owner enforcement around crew accountability, or the system sits unused in the field.
What's fast because of AI
Claude generates the entire onboarding email sequence, copywriting included, in one prompt instead of me writing it manually. I scaffold the entire FastAPI boilerplate with test fixtures using code generation. Debugging complex Postgres queries and N+1 problems runs 5x faster when Claude can enumerate edge cases before I even hit them. Writing the follow-up logic - conditional SMS timing, email fallbacks, retry logic - takes a day instead of a week because I'm asking Claude to enumerate all the branching cases and code them at once. Test coverage for the quote pipeline goes from a chore to something I ship on day 8 instead of day 20.
How I'd hand it off
I'd leave a Loom walking through the full operator flow: creating a customer, uploading leads, monitoring dispatch in real-time, reviewing metrics. I'd document the runbook on GitHub including environment setup, how to rotate Stripe keys, how to restart Twilio webhooks after credential changes. I'd do 30 days on pager rotation so any production issues hit me first. All credentials transfer to a shared 1Password vault under your team's account. The system runs on a standard $50/month Render instance with Postgres; scaling to 50 moving companies probably sits on the same box.