How I'd build Doorstep
I'd reach for Next.js on the frontend with Postgres on the backend, Stripe for billing, Resend for email delivery, and Twilio for SMS. The architecture is straightforward: multi-tenant SaaS where agents spin up a workspace, upload property data, and the system generates a landing page template plus a queued 5-touch sequence. I'm estimating 140 hours to get this into production, which puts us around $10,500 in labor before any infrastructure costs.
Day-by-day plan
- Day 1-2: Auth schema, workspace model, and Clerk integration for agent sign-up and RBAC
- Day 3: Stripe integration with three pricing tiers; wiring subscription status to feature gates
- Day 4-5: Property ingestion flow (CSV upload or MLS API polling); schema for property metadata
- Day 6: Landing page template builder; dynamic route generation for each property
- Day 7: Email template system and Resend integration for the 5-touch sequence
- Day 8: SMS trigger system via Twilio; scheduling engine for sequence cadence
- Day 9: Analytics dashboard showing page views and sequence engagement
- Day 10-11: Testing, documentation, and deployment to Vercel with Postgres on Supabase or Railway
What's hard about this build
The main risk is the data layer. Real estate data is messy - MLS feeds vary by region, sometimes there's no machine-readable property list at all, and agents will upload CSVs with missing fields. I'd build validation and fallback templates early, but expect rework here. Sequence timing is also tricky: you want the 5-touch to feel natural and spaced intelligently, not like spam. That requires a queueing system with time-zone awareness and delivery guarantees. Twilio's reliability is good but you need retry logic and bounce handling. Finally, there's the regulatory piece: Fair Housing Act compliance when messaging potential buyers. I'd document the guardrails carefully and have legal review the templates before launch.
What's fast because of AI
Claude cuts my scaffolding time by half. I prompt it with the schema and feature list, and it generates the initial Postgres migrations, Next.js page structure, and Stripe integration skeleton. From there I iterate and test, but the boilerplate is done in an hour instead of a day. AI also accelerates edge-case thinking: prompt it with "what breaks in a 5-touch sequence" and you get a list of failure modes to handle. For copywriting the in-product templates, Claude generates five variations per template, which I refine. Testing edge cases like timezone-aware scheduling or partial CSV imports gets easier when Claude can generate 20 test cases and mock data. Debugging network errors or Stripe webhook failures is also faster with Claude walking through the logs.
How I'd hand it off
I'd record a Loom walkthrough of the admin dashboard and key workflows (onboarding an agent, setting up a property, reviewing sequence performance). You get a runbook in Notion covering deployment, environment variables, and common issues. For the first month, I rotate on pager duty Slack-style - I'm the escalation path for agent issues or bugs that bubble up. You get all the credentials: Stripe dashboard access, Twilio account, Vercel deployment, Postgres connection strings. I'd recommend a second engineer review my code while I'm still here, both to catch gaps and to hand off knowledge. By week four, you should be able to deploy patches without me.