How I'd build Mortgage AI
I'd reach for Next.js on the front end with a FastAPI backend and Postgres for the database, using Stripe for billing, Twilio for notifications, and AWS S3 for document storage. The architecture is multi-tenant from day one: each broker organization gets isolated data, its own Stripe subscription, and three pricing tiers tied to loan originator seat count. I estimate 160-180 hours from kickoff to production, assuming clear requirements and one iteration cycle on the onboarding flow.
Day-by-day plan
- Day 1: Provision auth using Clerk, multi-tenant tenant model at the database level with row-level security, Postgres schema for organizations, loan originators, borrowers, and loans.
- Day 2: Add document schema and compliance_log table for audit trail. Configure AWS S3 bucket with encryption at rest.
- Day 3: Wire Stripe integration for subscription billing, webhook handlers for subscription events, tier-based pricing logic.
- Day 4-5: Build broker onboarding flow: sign up, create org, add loan originators as team members, configure Stripe subscription, send welcome email via Resend.
- Day 6: Loan originator dashboard showing active loans, pipeline status, next actions, and quick stats (volume, close rate).
- Day 7: Loan creation and editing forms, validation for required fields (borrower name, loan amount, property address), form state management.
- Day 8: Document upload UI, S3 storage with encryption, file validation (PDF only), virus scanning via ClamAV, compliance log entry for every upload.
- Day 9: PDF rendering in the browser, basic redaction for sensitive fields, document download for borrowers.
- Day 10: Admin panel for organization management, audit log export (CSV), user role management, billing history view.
What's hard about this build
Mortgage origination data is heavily regulated. RESPA forbids improper steering of borrowers toward certain loan products, ECOA forbids fair-lending discrimination based on protected characteristics, and state privacy laws govern how PII is stored and accessed. A single compliance incident - data leaked, a discrimination complaint, unauthorized document access - ends the product permanently. I'd need comprehensive audit logging on every document access and loan decision, encrypted fields for PII (SSN, DOB, income), and mandatory legal review of the LO workflow UI to catch steering or discrimination risks in the UX copy and workflow design. The second hard part is data integration: borrowers send documentation as PDFs, emails, spreadsheets, and photos. Reliably extracting and standardizing that data requires OCR or manual LO work, and errors propagate through underwriting. I'd scope document parsing conservatively, not trying to automate it on day one. The third risk is market reality: LOs at UWM, Rocket, and loanDepot are locked into employer-mandated LOS platforms and cannot adopt third-party tools regardless of interest. The real addressable market is independent brokers and small mortgage shops, many of which are capital-constrained in a high-rate environment.
What's fast because of AI
Scaffolding CRUD endpoints and React forms collapses from a full week to a day with Claude. I describe the loan object schema and ask Claude to generate the FastAPI routes and Next.js form components. Test generation is nearly instant: I sketch the happy path and Claude enumerates edge cases in borrower validation, document upload, and billing scenarios. UI copy - onboarding wizards, error messages, empty states, email templates - takes minutes instead of hours. Debugging is faster: I paste error logs and Claude often spots the root cause immediately. For the compliance side, Claude helps me enumerate fair-lending risks and build checklists, though the actual legal review still requires a lawyer. I also use Claude to draft the audit log schema and access-control rules, compressing what would be a day of careful design into a few hours of iteration.
How I'd hand it off
I'd record a Loom walkthrough of the broker sign-up, LO dashboard, loan creation, and document workflow, plus the admin panel for compliance exports and billing. I'd write a runbook covering deployment, Stripe webhook recovery, database backup procedures, and how to respond to a document storage failure. You'd own the 30-day pager rotation with me available for critical bugs. I'd transfer all credentials: AWS IAM keys, Stripe API keys, Auth0 config, database backups, S3 encryption keys. The staging environment is a full production replica so you can test changes safely. I'd also introduce you to the lawyer who reviewed the LO workflow for fair-lending compliance, so you know exactly what workflows need legal sign-off in future iterations.