How I'd build Immigration Law AI
I'd reach for Next.js and FastAPI as the core stack, with Postgres for the relational data model (cases, forms, attorneys, billing). I'd wire Stripe for the three-tier billing, Resend for attorney onboarding emails, and Claude API for form generation and case analysis. S3 for document storage. This build is roughly 160 hours across four weeks of full-time work if I'm moving fast and the scope stays tight. It's a legal product, so I'm honest about the risk surface.
Day-by-day plan
Day 1: Provision NextAuth.js with multi-tenant org isolation, Postgres schemas for users, organizations, and cases. Day 2: Stripe checkout flows wired for solo ($250), firm ($500), and enterprise tiers, plus webhook handlers for subscription lifecycle. Day 3: Case/matter creation UI, file upload flows, S3 integration for document storage. Day 4: Form library CRUD with version control and rollback (critical for USCIS updates). Day 5-6: Claude API integration for form field generation, conflict-checking detection, and malpractice risk flagging. Day 7: Testing and staging deployment. Week 2: Attorney onboarding flows, dashboard analytics, Sentry monitoring wired. Week 3: Custom branding, email sequences, competitive positioning in UI. Week 4: Comprehensive runbooks, deployment docs, 30-day handoff period.
What's hard about this build
USCIS forms and fee schedules change frequently, especially under new administrations, and breaking the form library silently while attorneys rely on it destroys trust instantly. The competitive landscape is already dense: Docketwise, Imagility, and INSZoom own the workflow layer, and switching costs are brutal for attorneys who've embedded these tools into their daily operations. Attorney malpractice exposure makes lawyers paranoid about AI-generated content, so they'll review everything manually anyway, gutting the automation value prop. This manual review cycle kills retention and makes churn high after trial. Conflict checking introduces complex edge cases across immigration case types. Data sourcing from USCIS has to be reliable. And every form output touches legal liability, so the product carries real downside risk if something goes wrong.
What's fast because of AI
Claude accelerates the scaffolding: I generate Next.js component structure, FastAPI route boilerplate, and Postgres migrations in minutes instead of hours. Test writing compresses weeks into days; Claude enumerates edge cases for conflict checking and form validation that I'd normally discover in production. Copywriting for the product UI, onboarding emails, and case audit messaging is fast because Claude drafts it contextually and I refine. Form library bootstrap from raw USCIS specs takes a day instead of a week because Claude parses PDFs and generates JSON schema for each form. Debugging edge cases is faster because I can describe a scenario and Claude brainstorms failure modes I haven't considered. Documentation becomes a by-product instead of an afterthought.
How I'd hand it off
I'd record a Loom walkthrough of the full product and then a separate Loom on the form update procedure (critical for staying current). Runbook in Linear covers staging deploys, production rollbacks, and manual form ingestion. Sentry dashboard and monitoring alerts are already hooked into Slack. I'd do a 30-day pager rotation where I'm on call for critical issues (malpractice exposure means this is not optional). Stripe, S3, and Postgres credentials are transferred securely via your credential management. Linear project board is up-to-date with known issues and technical debt.