How I'd build Job Costing AI
I'd reach for Next.js on the frontend with Postgres in the back, Stripe for billing, and a FastAPI worker for the heavy lifting around QuickBooks syncing and cost reconciliation. Clerk for auth, Resend for transactional email, S3 for job attachments. Rough estimate: 110-130 hours, so around eight to ten grand before contingency. I'm building something that survives a contractor's real workflow, not a demo.
Day-by-day plan
Day 1: Provision Clerk auth and multi-tenant schema in Postgres. One org can have multiple users, users can belong to multiple orgs. Nail the isolation model first.
Day 2-3: QB OAuth flow and initial read-only sync. Pull customers, jobs, line items, and general ledger entries into our schema. Test against three different QB file structures to understand variance.
Day 4: Build the core costing engine. Match QB line items to job phases, calculate gross margin by phase, flag jobs that are underwater. Async task, runs nightly initially.
Day 5: Job dashboard - overview table, drill-down to phase-level P&L, estimated vs actual labor hours, material waste flags. Designed for a 1024-wide iPad in landscape.
Day 6: Stripe billing. Three tiers: $99, $299, $799 per month. Tie quota and feature gating to subscription status. Usage-based billing flag for future add-ons.
Day 7: QB sync reliability layer. Handle stale tokens, rate limits, malformed data. Webhook ingestion when QB updates a job. Manual re-sync button with dry-run preview.
Day 8: Automated testing for costing logic, QB integration smoke tests, auth and multi-tenant isolation tests. At least 60% coverage on critical paths.
Day 9: Sentry setup, Datadog for API latency, alerts for sync failures or data inconsistencies. A broken QB sync is a support incident, not a silent bug.
Day 10: Loom walkthrough, deployment runbook, 30-day on-call rotation briefing.
What's hard about this build
QuickBooks integration is the load-bearing wall. Their API has rate limits, OAuth tokens expire silently, and job numbering schemes vary wildly across contractors. A sync that corrupts cost data doesn't just fail - it erodes trust and triggers chargebacks. I'd build a state machine around sync status and make every QB write idempotent with deduplication keys. Multi-tenancy is also unforgiving: one org's bad data cannot leak into another's reports. I'd test isolation at the SQL level, not just the application level. Finally, contractors move slowly; the onboarding flow needs to prove value in the first session or they'll bounce back to spreadsheets. That means a smooth QB connect and a precomputed dashboard within minutes, not weeks.
What's fast because of AI
Claude cuts scaffolding and edge-case thinking time by a week. I'd use it to generate the Postgres schema, API endpoint stubs, and test fixtures for QuickBooks' three most common file structures. For the costing engine, Claude helps me enumerate edge cases - partial invoices, retained retainage, phase splits, equipment rental allocations - and write exhaustive tests. Copywriting for the UI takes hours normally; Claude drafts error messages, empty states, and onboarding microcopy that I refine. Debugging async QB sync failures is verbose; Claude reviews logs and suggests hypotheses faster than I'd reason through them. The codemod to add feature flags to every UI surface takes a day manually; Claude does it in minutes. Those five to seven days of compression directly reduce the total bill.
How I'd hand it off
I'd record a 15-minute Loom of the full onboarding flow and QB setup. Leave a runbook in Linear: how to debug a stuck sync, how to escalate a data inconsistency, how to add a new job costing rule. I'd run 30 days of on-call rotation with the first contractor - available for Slack questions and sync failures. All credentials, API keys, and deployment configs go into a shared 1Password vault. Code is in GitHub with main branch protection and a deploy to staging workflow. You're on-call after day 30; I've documented what breaks and how to fix it.