How I'd build Seller AI
I'd reach for Next.js on the frontend with a Node.js backend, Postgres for the database, and Stripe for billing. The Amazon SP-API integration is where most of the complexity lives. For a full-featured MVP with multi-tenant auth, Stripe integration, Claude-powered listing optimization, and seller onboarding, I'm looking at roughly 220-240 hours of engineering work.
Day-by-day plan
- Day 1-2: Set up Next.js project, Postgres schema for users/teams/subscriptions, Clerk for multi-tenant auth. Provision development and production environments on Vercel and AWS RDS.
- Day 3: Implement Stripe integration across the three pricing tiers, including webhook handling for subscription events, dunning workflows, and seat-based usage tracking.
- Day 4-5: Build the seller onboarding flow: email verification via Resend, phone verification, and Amazon seller identity validation (checking seller ID against public registries and SP-API live credentials).
- Day 6-7: Integrate Amazon SP-API: fetch catalog listings, sync product details, build rate-limiting and token refresh handlers. Implement a job queue (Bull) for async syncs so catalog pulls don't timeout the UI.
- Day 8: Wire Claude API for listing optimization. Build a form where sellers input target keywords, then generate optimized titles, bullet points, and descriptions using cached prompt templates.
- Day 9: Implement analytics: track API call volume, keyword research data, estimated listing performance. Build dashboard charts pulling from stored Amazon reports and our own usage metrics.
- Day 10-11: Set up Sentry error logging, build a runbook, deploy to production with health checks, and shadow the system for 48 hours to catch integration issues.
What's hard about this build
The Amazon SP-API integration carries real operational risk. Seller API credentials expire without warning, and silent failures cascade into broken dashboards that damage trust immediately. Token refresh is stateful and race conditions are easy to miss in production. Keyword data sources require contract negotiation with third parties (most charge 50-100 per seller per month), adding compliance and support complexity. There's also latency: keyword research can take 30-60 seconds, and if the frontend times out, sellers churn. I'd implement circuit breakers and graceful degradation so a 429 throttle doesn't cascade. Amazon also changes SP-API scopes and request limits without notice. I've seen integrations break mid-quarter because of policy shifts, so I build with feature flags and fallbacks so you can disable features that lose API access without taking the product offline.
What's fast because of AI
Claude accelerates the core value prop significantly. For listing copy generation, I use prompt caching to avoid recomputing the same product context across similar SKUs. That alone saves 30-40 percent of tokens for a seller with 50 plus products. Test generation is faster too: I feed Claude the SP-API schema and ask it to enumerate edge cases (malformed tokens, rate limits, partial syncs, regional differences) that I'd normally write by hand. Debugging is quicker. When a seller reports bad listings, Claude helps me trace through the product data, keyword mismatches, and API errors to spot systematic issues. Copywriting for product UI - error messages, onboarding flows, pricing page - compresses a week of iteration into 2-3 days because I work with Claude on tone and clarity before a designer touches it.
How I'd hand it off
I'd record a 30-minute Loom walkthrough of the admin panel, seller dashboard, and Stripe integration. The runbook covers Postgres backup procedures, Stripe webhook troubleshooting, Amazon API credential rotation, and playbooks for common support issues. I'd do a 30-day pager rotation where I'm on call to diagnose production issues and transfer knowledge to your ops team. All credentials (Stripe, Amazon, Claude API keys) go into a secure vault with documented rotation schedules. CI/CD lives in GitHub Actions with deployment logs to Slack. Your team owns deployments from day one.