How I'd build Cleaner AI
I'd build this with Next.js for the customer-facing dashboard and admin panel, PostgreSQL for multi-tenant data isolation, Stripe for subscription billing, and Twilio for job dispatch SMS to crew members. The AI component is a scheduling optimization service written in Python that runs on a cron to reassign jobs based on geography and availability. I'd estimate 90-110 hours total, delivered in 4-5 weeks at 20-25 hours per week.
Day-by-day plan
Day 1-2: Provision PostgreSQL schema with tenant isolation at the database level, set up Next.js with NextAuth for Google OAuth, multi-tenant middleware. Build the basic signup flow.
Day 3-4: Wire Stripe integration across the three pricing tiers. Implement subscription webhooks, handle downgrade/churn properly, build basic billing dashboard for customers.
Day 5-7: Build the core job management feature. Schema for jobs, customers, crews, service types. API endpoints for job creation, assignment, and status updates. Front-end form for entering jobs.
Day 8-9: Integrate Twilio for SMS dispatch. When a job is assigned to a crew member, send an SMS with job details, address, and a link to accept/decline.
Day 10-11: Build the scheduling optimization engine. This is the AI piece. Uses Claude API to analyze open jobs against crew availability and geography, then surfaces recommended assignments to the manager for one-click approval.
Day 12-13: Customer onboarding flow. Email signup confirmation, welcome email with Loom walkthrough, direct them to the crew invite link.
Day 14: Testing, security audit, deployment to Vercel. Document all credentials and environment variables.
What's hard about this build
The real friction is multi-tenant data isolation. If a crew member from Tenant A can query Tenant B's data because we forgot a WHERE clause, we've got a legal problem fast. Every query needs tenant context baked in, which means security reviews and multiple test passes before production.
Second: the scheduling optimization engine is deceptively complex. Edge cases abound. What if a crew member is already assigned three jobs that day but the AI recommends a fourth overlapping job? What if a job is cancelled after dispatch? Building a robust conflict checker with proper rollback behavior takes time. I'd use Claude to enumerate these scenarios and build test cases, but I'm still validating each one manually.
Third: Jobber and HouseCall Pro integration. Cleaning owners have years of data there. If we don't make import smooth, onboarding fails. That's a separate integration phase, but scope-creep risk is high. I'd scope it out initially and handle it in month 2.
What's fast because of AI
Claude compresses the scaffolding and test phase significantly. Database migration files, Stripe webhook handlers, form validation schemas, SMS message templates, and error handling for edge cases all emerge from prompting. What was a 2-3 day manual build becomes 4-5 hours of prompt-refine-commit cycles.
Copy is another accelerant. Onboarding emails, in-app UI microcopy, crew dispatch SMS templates. These feel small but take time to get right. Claude drafts them, I refine them once for brand voice, and they're done. UI edge-case enumeration too: "What breaks in the scheduling optimizer if a crew member has a custom availability window?" Claude generates a list, I validate it's exhaustive, then I write tests for each case. Debugging is faster too. When a data model issue surfaces, Claude can trace through the query logic and spot tenant isolation bugs I'd miss.
How I'd hand it off
I'd record a Loom walkthrough of the entire customer flow, from signup to first job assignment. Document the runbook: how to deploy, how to rotate Stripe and Twilio keys, what to monitor in Vercel logs. Leave a detailed API documentation file. I'd stay on for 30 days of pager rotation at reduced hours (5 hrs/week) to handle critical bugs. All credentials go into a shared password manager, GitHub repo access is transferred, and deployment privileges are handed to your operations person.