How I'd build Insurance AI
I'd reach for Next.js with TypeScript on the frontend, a Postgres database with field-level encryption for PII, Stripe for billing, and Twilio for SMS follow-ups. The backend would be FastAPI to handle intake processing, validation, and carrier API ingestion. I'm estimating 240 hours to a working production build, plus another 40 for security hardening before launch.
Day-by-day plan
- Day 1-2: Set up Postgres with encrypted SSN and health history fields, implement multi-tenant isolation at the row level, bootstrap Next.js with Clerk for authentication and role-based access
- Day 3: Wire Stripe billing across the three pricing tiers, configure webhook handlers for subscription and usage events
- Day 4-5: Build the intake form builder UI with drag-and-drop and conditional logic, back it with a validation service that sanitizes inputs before database writes
- Day 6: Integrate Twilio for SMS follow-up sequences, implement TCPA-compliant unsubscribe handling
- Day 7-8: Build the policy summary pipeline using Claude API for document parsing and summarization, implement caching to reduce API calls
- Day 9: Add audit logging for all data access events to a separate append-only table, tie this to E&O insurance requirements
- Day 10: Deploy to a hardened AWS VPC with TLS termination via Caddy, configure network ACLs and request signing for carrier API calls
- Day 11: Run a third-party security audit focused on PII handling before marking the product ready for customer data
What's hard about this build
The hardest part is the data liability surface. Storing SSNs and health history invokes state insurance regulations and triggers personal E&O exposure if there's a breach. I'd need a security lawyer involved before launch to vet the threat model and policy terms. Second, each carrier has a different API shape and authentication model. Without native integrations, agents double-enter data into their AMS and into Insurance AI, which kills adoption immediately. I'd ship integrations with the three highest-volume carriers before GA and map a six-month roadmap for the rest. Finally, the existing AMS vendors already do intake and follow-up well. To win, Insurance AI needs to be at least 40% faster to set up, or agents won't bother switching.
What's fast because of AI
Claude accelerates the structural work. I'd use prompt caching to build a form-builder code generator that turns a JSON spec into validated React components with conditional logic already baked in. That's a week of component work compressed into a day. For policy summaries, Claude's document understanding cuts prompt engineering from weeks to hours. I'd also use Claude to enumerate data validation edge cases (SSN formats across territories, health history classification), then scaffold the test suite. For carrier integrations, Claude helps reverse-engineer API docs and generates client code matching their auth patterns, saving a week per integration. The net is that what used to take a month of scaffolding, testing, and debugging takes a week.
How I'd hand it off
I'd leave a Loom walkthrough of the deployment pipeline and the pattern for adding carrier integrations. You get a runbook for common support escalations: password resets, failed SMS delivery, carrier timeouts. I'm on-call for 30 days, handling incidents and transferring knowledge to your team by day 20. All credentials go into a shared 1Password vault with audit logging. Code lives in a private GitHub repo with branch protection and required review. Linear tracks post-launch bugs. After 30 days, you're independent.