How I'd build Plumb
I'd reach for Next.js with PostgreSQL, Stripe for billing, and Twilio for contractor notifications. The core estimate engine would live in a Python service consuming historical pricing data and regional labor multipliers. Frontend-to-production, I'm estimating 180-200 billable hours split across 3-4 weeks. That assumes clean requirements and no architectural surprises, which is rare.
Day-by-day plan
- Day 1-2: Provision auth schema (NextAuth.js), multi-tenant isolation in Postgres, and basic RBAC for contractor vs. admin roles. I'd lean on Row-Level Security policies to enforce tenancy at the database layer.
- Day 3: Stripe integration for three pricing tiers. Webhook handling for subscription state changes, trial logic, and seat-based scaling.
- Day 4: Customer onboarding flow. Contractor sign-up, email confirmation via Resend, and a guided walkthrough that collects their service area, typical job types, and historical project costs if available.
- Day 5-6: Core estimate form and calculation engine. Plumbing-specific line items (rough-in, fixtures, labor, materials), regional labor-cost multipliers keyed to ZIP code, and a real-time estimate output that refreshes as inputs change.
- Day 7: Integration skeleton for Procore API. OAuth handshake, job data sync, and a button to push estimates back into their existing workflow.
- Day 8-9: Admin dashboard for data management. Upload historical pricing, adjust regional multipliers, view estimate accuracy metrics against actual project outcomes.
- Day 10: Deployments, error handling, and a runbook. I'd host on Vercel for the frontend, a small EC2 instance for the Python estimate service, and RDS for Postgres.
What's hard about this build
The estimate accuracy risk is real and non-negotiable. An 8-10% miss on a $180k commercial project costs the contractor $14k+ and tanks their reputation in a tight-knit community. One visible failure triggers chargebacks and word-of-mouth damage that kills your GTM. To stay accurate you need continuous training data, not a static dataset. Regional variance is extreme: lumber prices in Idaho vs. Miami, union vs. non-union labor scaling, local code requirements. Gathering ZIP-level historical data from contractors themselves is slow and noisy. They won't volunteer their project data freely, so either you integrate with Procore and CoConstruct to pull it directly, or you build a data-import flow that requires operator intervention. The Procore integration is non-trivial: their API is documented but rate-limited, and contractors manage permissions carefully. Finally, you're competing against $300-500/mo platforms that already bundle estimates. Adoption requires the estimate tool to be so good or so easy that contractors add a second subscription. That's a high bar.
What's fast because of AI
AI scaffolds the form builder logic and validation rules in a day instead of a week. I'd have Claude generate the Stripe webhook handlers, TypeScript types for the contractor schema, and the multi-tenant query layer. Edge-case enumeration is where AI shines: What happens if a contractor downgrades mid-month? What if they upload a CSV with malformed ZIP codes? Claude spins out the state machine and error paths faster than I'd write them by hand. UI copy for the onboarding flow is better when AI writes the first draft. Debugging is faster: "Here's the estimate diff and a Postgres slow-query log - why?" Claude narrows the cause in seconds. Tests for the Stripe webhook handler and regional multiplier logic come out in one pass instead of three drafts. The time compression isn't magic, but it's real: scaffolding, boilerplate, and exhaustive error handling typically shrink by 40-50% when I use Claude alongside the IDE.
How I'd hand it off
I'd record a 20-minute Loom walkthrough covering the onboarding flow, estimate generation, and admin dashboard. A detailed runbook lives in Markdown: how to deploy, how to adjust regional multipliers, how to monitor estimate accuracy, and what to do if Stripe webhooks start failing. API docs for the estimate service cover the regional pricing schema and the Procore sync endpoint. I'd run a 30-day pager rotation so you have a contact for production issues, then hand off SSH keys, Stripe account access, and database backups. Deployment is automated via GitHub Actions, but you'll own the infrastructure from day one.