How I'd build Farm AI
I'd reach for Next.js on the frontend with a FastAPI backend in Python, Postgres for the database, Stripe for billing, and Twilio for crew notifications. The architecture would use Next.js server components for the compliance forms (they're complex and stateful), with a REST API layer that handles multi-tenant isolation at the database and application levels. I'm estimating 240-280 hours of focused development time to ship a defensible MVP, which puts us at the upper end of the $30k budget. I'd plan for 5-6 weeks of heads-down work.
Day-by-day plan
Day 1-2: Provision Postgres schema with row-level security policies for multi-tenancy, set up NextAuth with farmer account types and org ownership. Build the tenant context middleware so every query is scoped to the authenticated farm.
Day 3-4: Integrate Stripe billing. Wire the three subscription tiers to tenant provisioning, test downgrade/upgrade flows, handle grace periods for lapses. This is non-negotiable for recurring revenue.
Day 5-6: Build the farmer onboarding flow. Email verification via Resend, farm details intake, crew roster CSV import, equipment inventory seeding. Make this smooth because farmers won't tolerate friction.
Day 7-9: Crew scheduling module. Calendar UI, shift assignments, Twilio integration so crew gets SMS reminders 24h and 2h before shifts. Conflict detection for double-booked people.
Day 10-11: Compliance logs schema and entry forms. Structure state-specific rules in a config layer: FSMA fields for produce farms, H-2A tracking for labor, organic certifications if needed. Don't hardcode; make it extensible.
Day 12-13: Equipment records: serial numbers, maintenance history, purchase date, depreciation tracking. Link to financial summaries.
Day 14: Season-end financial summary generation. Cost-of-goods-sold by crop, labor hours aggregated by crew member, equipment depreciation, gross margin. Export to CSV and PDF.
Day 15-16: Testing, load testing with realistic data volumes, and Loom walkthrough for handoff.
What's hard about this build
Compliance rules are the nuclear mine. A generic "compliance log" means nothing to a farmer if it doesn't match their state's actual FSMA docket requirements or if the organic certification fields don't align with USDA reality. You'll face pushback and liability exposure fast. I'd need to validate against real FSMA guidance, talk to one or two actual farmers in a target state early, and build the compliance config layer defensively so we can iterate per-state without redeploying. Multi-tenant data isolation is the second gotcha. A mislabeled row-level security policy and you're exposing Farmer A's crew schedule to Farmer B. That's death for the product. I'd spend extra time on test coverage around isolation boundaries. Third is timezone handling. Crew schedules and compliance logs need to track both local farm time and UTC; get this wrong and you lose trust on day one.
What's fast because of AI
I'd use Claude to scaffold the Postgres schema and migrations from a product spec, which saves a day. Generating comprehensive test coverage around the compliance rule engine is slow to write by hand; Claude enumerates edge cases I'd miss and writes the test bodies. The farmer onboarding flow has a lot of tedious form validation and error messaging. Claude writes that copy and handles the repetitive validation patterns. Debugging state bugs in multi-tenant isolation is where AI really pays. When a row-level security policy isn't working, I'd use Claude to trace through the Postgres role hierarchy and suggest fixes instead of reading docs for an hour. That compresses debugging cycles from days to hours.
How I'd hand it off
I'd leave a 20-minute Loom walking through the admin dashboard, the database schema, the compliance config layer, and the Stripe webhook handlers. A runbook in Linear covering deployment steps, environment variables, and how to add a new state's compliance rules. I'd do a 30-day pager rotation overlap: if something breaks in week 3 or 4, I'm on call. All credentials transfer to your 1Password vault. The codebase stays in your GitHub with a clear README on local setup and the tech stack.