How I'd build Almsbury
I'd start with Next.js for the frontend and API routes, Postgres for the database, Stripe for billing, and Auth0 for multi-tenant auth. The core feature - a grant application form builder with Claude-powered writing assistance - maps cleanly to a React component library plus a backend service that hits the Anthropic API. I'm estimating 400-450 billable hours, which lands us at the $32k budget with some breathing room for iteration and unforeseen edge cases.
Day-by-day plan
- Day 1-2: Scaffold the Next.js app with Auth0, Postgres schema for users, organizations, and grant applications. Multi-tenant isolation at the org level.
- Day 3: Build the basic dashboard and navigation, define the grant application data model (fields, required vs optional, section grouping).
- Day 4-5: Wire Stripe billing API, implement subscription tiers ($0 free trial, $89/month, $119/month with org-seat scaling), sync subscription status to the database.
- Day 6-7: Build the grant application form builder UI with drag-and-drop fields, WYSIWYG text editor for instructions and prompts.
- Day 8-9: Integrate Claude API to power the "write this section" button - send application context and user drafts, return improved text with revision history.
- Day 10: Build the trial signup flow (7-day free tier auto-downgrade or payment required).
- Day 11: Set up email notifications via Resend for trial expiry, billing failures, new team member invites.
- Day 12: Build the team management UI (add members, set roles like viewer/editor/admin).
- Day 13-14: Audit and test multi-tenant isolation, payment flows, and trial-to-paid conversion paths. Deploy to Vercel.
What's hard about this build
The biggest technical risk is the grant-data integration layer. Grant funders like MacArthur and Mellon are actively writing AI-prohibition clauses into application guidelines, which means any output from Claude needs to be clearly labeled as AI-assisted and we need audit trails showing what was AI-generated vs human-written. That requires versioning every section and flagging AI contributions explicitly. We also need to survive the long sales cycle: nonprofits buy on annual budget cycles, so the trial-to-paid funnel needs to stay robust through 3-6 months of inactivity without degrading. Finally, nonprofit data is sensitive, so SOC2 compliance and careful handling of donor and grant information require thoughtful schema design and access controls.
What's fast because of AI
Claude accelerates the scaffolding phase - generating the form builder component library, CRUD endpoints for applications, and test suites for multi-tenant isolation takes a few hours instead of a week. The Claude API integration itself is straightforward, so the writing-assistance feature goes from concept to working in a day. I'd use Claude to enumerate edge cases in the billing flow (failed charges, refunds, seat-count changes mid-cycle, trial expirations) and generate test cases for each, which compresses painful QA into a few hours. Copy for the onboarding UI, pricing page, and help documentation gets generated and refined with Claude. Debugging production issues also accelerates when Claude can ingest logs and suggest root causes with context.
How I'd hand it off
I'd create a 30-minute Loom walkthrough covering user signup, trial activation, grant application creation, and the Claude writing-assist feature. A runbook covering database backups, Stripe webhook monitoring, and Claude API rate limits. I'd transfer Stripe and Auth0 admin credentials to your team, commit all code to your Git repo with a clean main branch, and schedule a 30-day pager rotation where I'm on-call for critical production bugs. Linear tickets for any post-launch follow-up work stay organized and scoped.