How I'd build Engagement AI
I'd reach for Next.js with TypeScript for the dashboard, Postgres for multi-tenant data, Stripe for billing, and FastAPI in Python on the backend for the heavy LLM orchestration. The email sending goes through Resend, with a Bull queue to handle async jobs. I'd estimate 350-420 hours for a clean MVP: auth, integrations, metering, and a proof system that actually ties engagement back to churn reduction so we can sell into risk-averse ops teams. That's roughly 10-12 weeks solo, or 8-9 weeks with a good frontend contractor.
Day-by-day plan
- Day 1: Provision Clerk for auth, spin up Postgres schema with org/workspace isolation, wire Stripe webhooks.
- Days 2-3: Ship three pricing tiers through Stripe. Lock features behind usage limits. Build metering into the API layer so we're billing accurately from day one.
- Days 4-5: Next.js landing page with email capture. Onboarding flow that lets users connect their first email account and pick a cold outreach template.
- Days 6-7: Resend integration for outbound sends. Bull queue with retry logic. Dashboard showing sent, opened, clicked. Basic cohort segmentation for ecommerce product IDs.
- Days 8-9: LinkedIn profile scraper with rate-limit resilience, or Clearbit API for prospect enrichment. This is where we start building differentiation: better data about who's in the buyer's audience.
- Days 10-11: Churn-prediction model stub. Nothing fancy at launch, but the schema supports attaching engagement metrics back to customer lifetime value. This lets future customers prove ROI.
- Days 12-13: Monitoring, logging, error alerts. Stripe event handling for dunning. Rate-limit enforcement per tier.
- Day 14: Documentation, Loom walkthrough, deploy to Vercel and Railway.
What's hard about this build
The core risk is the integration layer. LinkedIn scraping breaks every quarter when they update their frontend, and their API access is gated behind sales relationships. We're either playing cat-and-mouse with selectors or cold-calling LinkedIn's sales team, neither of which scales cleanly. Second: cost metering. If a single customer triggers 50,000 LLM tokens per send trying to personalize at scale, our margin on their $99/month tier evaporates. I'd implement usage alerts and hard caps early, but that means supporting angry customers hitting limits during their busiest campaigns. Third: the proof problem. Our earliest customers won't see churn reduction for 60-90 days. Without case studies from paid pilots, the GTM team will struggle to close. This means the product ships with a basic attribution system, but sales will need to negotiate outcome-based pilots with early logos.
What's fast because of AI
Schema design and CRUD scaffolding used to be three days of boilerplate. Claude generates the Postgres schema, the API routes, and the form components in hours. Prompt engineering for the engagement copy suggestions inside the dashboard gets done by having Claude generate 50 variations of cold-outreach hooks, then we A/B test them. Error handling and edge cases: instead of discovering that we crash when a Stripe webhook arrives for a deleted customer, I prompt Claude with the Stripe event spec and get a matrix of cases back in one session. Documentation and runbooks that would take a junior 40 hours get drafted in a few prompts. Testing is faster too. Claude generates pytest cases for the metering logic, which is where bugs hurt most. I'm confident we catch 80% of payment-logic regressions before they hit production.
How I'd hand it off
I'd record a Loom walkthrough of the admin panel, the customer dashboard, and the deploy process. I'd leave a runbook in the repo: how to scale the queue, how to refresh LinkedIn scrapers if they break, where to adjust LLM cost thresholds. We'd do a 30-day pager rotation where I'm on call for critical bugs and the new operator shadows me. Stripe secrets, Resend API keys, and the Postgres replica connection go into the shared vault. The codebase is Next.js and FastAPI, both widely understood, so the handoff is clean.