How I'd build Student Loan Advisor AI
I'd start with Next.js for the frontend and API routes, Postgres for the core schema, Stripe for subscription management, and Claude API for the advisor engine. Rough estimate is 120-150 hours to get from zero to production with real user subscriptions flowing and a working AI recommendation engine. That's 3-4 weeks at 40 hours per week, or tighter if we parallelize and I'm the only builder.
Day-by-day plan
- Day 1-2: Scaffold Next.js app, set up Postgres schema (users, loan_profiles, subscriptions, audit_logs), Clerk for auth, and bare Stripe integration for the three pricing tiers.
- Day 3-4: Build the onboarding flow (loan intake form, federal vs. private loan detection, income verification shadow) and wire it into the database.
- Day 5-6: Integrate Claude API for repayment recommendation generation, add prompt caching for the student aid rules knowledge base so we're not recomputing context every request.
- Day 7-8: Ship the loan calculator UI (IRR, monthly payment, 10-year vs. 25-year net cost), IDR comparison tool showing PAYE/SAVE/IBR trade-offs, and the calculator SEO landing page.
- Day 9-10: Implement Stripe webhook handlers for subscription upgrades, downgrades, and usage tracking. Add email receipts via Resend.
- Day 11-12: Write regulatory audit trail logging (who queried what, what recommendations were served, timestamps). Build admin dashboard to spot edge cases.
- Day 13: Load Stripe test mode, run through full user journey end-to-end. Deploy to staging on Vercel, run smoke tests.
- Day 14: Production cutover, point DNS, monitor error rates and token usage for the Claude API calls.
What's hard about this build
The regulatory exposure is real. We're threading a needle between "education tool" (legal) and "financial advice" (requires an RIA or nonprofit safe harbor). I'd bake in conflict-of-interest checking from day one: if a user has federal loan servicer conflict with a recommendation, we flag it and suggest they speak to a nonprofit counselor instead. We also need to version the advice based on when the rules snapshot was taken (today's SAVE rules, not 2023's), and we audit every recommendation served. The second hard part is data staleness. SAVE eligibility, income limits, forgiveness timelines shift on political cycles. I'd plan for a quarterly rules audit where we run a sample of existing recommendations against the new ruleset and flag users who need re-evaluation. The third is free tool substitution: studentaid.gov's Loan Simulator is authoritative and free. We win by being faster, more conversational, and catching edge cases (parent PLUS consolidation, PSLF under-reporting, income dips). Without those deltas, we don't survive the $10/month paywall.
What's fast because of AI
Claude handles the lion's share of what would normally take a week. Scaffolding the data model and form validation happens in an afternoon because I'm prompt-caching the DB schema and having Claude generate the Zod validators. Edge-case enumeration for the conflict checker (married filing separately, disability discharge, public service employer verification) compresses into a few hours of prompt iteration instead of days of domain research. Copy for the pricing page and onboarding microcopy gets generated, reviewed, and tweaked in parallel with development instead of blocking on a copywriter. Testing the recommendation engine is accelerated by Claude writing property-based tests and flagging logical inconsistencies in the ruleset. And when we hit production issues, Claude searches the codebase and audit logs in seconds and narrows down the root cause. What used to be 4 hours of grep and code review is 30 minutes.
How I'd hand it off
I ship a Loom walkthrough of the admin dashboard, user onboarding, and the recommendation audit trail. You get a runbook (how to adjust the Claude prompt if rules change, how to add a new pricing tier, how to rotate Stripe keys). I hand off the Vercel project, Postgres connection, and Stripe API keys. You're on a 30-day pager rotation: any subscription failures or recommendation errors go to your Slack. After day 30, you're owning the quarterly rules audit and the monthly cost optimization on Claude token usage. I'm available for 10 hours of slack support in the first month if you hit unexpected gotchas.