How I'd build Tax Planning AI
I'd reach for Next.js on the frontend, FastAPI on the backend, and Postgres for the database. Stripe handles billing across the three tiers, Resend manages transactional email and PDF generation, and everything runs on Vercel and Railway. Rough estimate: 420-450 hours to hit production-ready with the human-in-the-loop guardrails baked in from day one.
Day-by-day plan
- Days 1-2: Auth schema and multi-tenant data model in Postgres. User and organization tables, role-based access control for CPAs versus business owners.
- Day 3: Stripe product setup and webhook ingestion. Wire the three pricing tiers and trial period into the customer model.
- Days 4-5: Customer onboarding flow. Email verification, tax return upload handler, CPA invitation logic (this is the human-in-the-loop entry point).
- Days 6-7: Claude API integration for tax strategy generation. Prompt engineering for tax scenarios, streaming responses to the frontend, flagging system for legal review.
- Days 8-9: Tax strategy comparison dashboard. Show current approach versus AI recommendation with a "request CPA review" button.
- Days 10-11: Reporting and audit trail. Every recommendation logged with timestamp and reasoning, searchable for compliance.
- Days 12-13: CPA dashboard. View pending reviews, approve or reject recommendations, add manual notes that feed back to the owner.
- Day 14: Analytics and usage tracking for GTM. Identify which tax scenarios drive conversions, which referral partners send the most leads.
- Days 15-16: Runbook and deployment automation. Docker containers, CI/CD pipeline, error alerting to Slack.
What's hard about this build
The hardest part is the liability model. Every tax recommendation leaving the platform needs a clear audit trail and a human CPA sign-off before it reaches the business owner. If we skip this, we're one audit penalty away from litigation. That means designing the workflow so the AI generates the strategy, the platform flags it for review, and the CPA explicitly approves it before the owner sees it as actionable. The second challenge is tax scenario enumeration. We need to model edge cases (S-corp versus LLC, state-specific credits, recent law changes) without the prompt becoming a hallucination factory. I'd start with high-ROI scenarios (R&D credits, Section 179, quarterly estimated tax adjustments) and expand only after validating against a CPA panel.
What's fast because of AI
AI compresses this timeline in three ways. First, scaffolding: Claude generates the Next.js page structure and FastAPI route templates in minutes, not hours. Second, test coverage: I use Claude to enumerate edge cases and generate test suites for the payment flow and tax scenario logic, cutting what was a week of manual testing to a day. Third, prompt engineering: Instead of guessing how to frame a tax strategy for the UI, Claude helps me iterate the prompt to produce clear, CPA-friendly copy, then I validate it against a small batch of real tax returns. That feedback loop normally takes days happens in hours.
How I'd hand it off
I'd record a Loom walkthrough of the full stack: adding a new tax scenario, the CPA approval flow, reading audit logs for compliance questions. You get a 30-day pager rotation where you can reach me on Slack for production issues. I transfer all credentials (Stripe, Claude API, Postgres, Vercel, Linear) to your team account and give you a deployment runbook. The codebase lives on GitHub with a README covering local setup, environment variables, and the CPA onboarding process.