How I'd build Upsell AI
Next.js for the admin dashboard and API, Postgres for multi-tenant data, Stripe for recurring billing tied to store revenue tiers, and Claude API for recommendation intelligence. I'd estimate 480-520 billable hours across 12 weeks - front half is infrastructure and integrations, back half is feature refinement and launch support. Early weeks are heavy on plumbing; final stretch is merchant onboarding and App Store optimization.
Day-by-day plan
- Week 1: Shopify OAuth app scaffold, Next.js project setup, Postgres multi-tenant schema with tenant isolation
- Week 2: Stripe billing backend, tier-based pricing logic tied to store revenue detection
- Weeks 3-4: Shopify Admin API product and order sync, validation testing with stores under 6 months
- Weeks 4-5: AI recommendation engine using Claude API, human review workflow for data-sparse merchants
- Weeks 5-6: Merchant dashboard UI, analytics dashboard with recommendation accuracy and revenue lift metrics
- Week 6: Shopify webhook handlers for order events, real-time recommendation injection
- Weeks 7-8: Test coverage, load testing against Shopify rate limits, security audit for data handling
- Weeks 8-9: Shopify App Store listing optimization, demo video strategy and content review
- Weeks 9-12: 30-day launch support, merchant onboarding, churn cohort analysis, App Store feedback handling
What's hard about this build
The core issue is that AI recommendations are useless for stores under 6 months old - there's no historical purchase data for the model to learn from. I'd build a detection layer that identifies early stores and disables AI recommendations entirely, instead surfacing a "human curator" flow. This prevents the reputation damage of bad early reviews. Separately, Shopify's 40 requests/second API limit means I'll queue catalog syncs asynchronously and lean hard on caching. Finally, ROI attribution at this price point is genuinely unsolvable - merchants see 2% upsell lift but can't isolate whether the AI caused it or they were already buying. I'd build analytics that acknowledge this uncertainty rather than claim false precision, keeping renewal conversations grounded.
What's fast because of AI
Claude compresses the scaffolding phase by 3-4 days: multi-tenant schema, Stripe webhook handlers, Shopify API client boilerplate. Edge-case enumeration - SKU variants, subscription products, deleted items, inventory sync conflicts - normally takes 2-3 days; Claude handles it in hours. Product copywriting (dashboard labels, error messages, onboarding tooltips, in-app education) goes from 4-6 hours to 30 minutes. Debugging Shopify integration edges like product ID reconciliation across draft and published states is 2x faster. Without AI assistance, I'd estimate 16-18 weeks instead of 12.
How I'd hand it off
Loom walkthrough covering the multi-tenant architecture, Stripe billing logic, Shopify data sync, and how to debug recommendation quality. Runbook in the repo with deployment procedures, monitoring setup (Sentry for errors, custom dashboards for recommendation accuracy), and the manual review queue for early stores. You take pager rotation starting day 31. All API credentials transfer to your account. Code lives in GitHub with Vercel CI/CD pre-configured; you own deployments from there.