How I'd build Prospect Enrichment AI
I'd reach for Next.js on the frontend, FastAPI on the backend, Postgres for the core relational data, and Stripe for billing. The architecture is multi-tenant from day one, with proper row-level security and tenant isolation baked into the ORM layer. Rather than build proprietary LinkedIn scraping, which keeps the legal risk low and maintenance costs predictable, I'd integrate Apollo or RocketReach APIs for the actual enrichment data. This trades margin for reliability and gives customers instant credibility on data quality. The full build to production, including customer onboarding, payment processing, and campaign management, is roughly 380 hours at my rate. That's about five weeks at full capacity.
Day-by-day plan
- Day 1-2: Postgres schema for tenants, users, campaigns, and enrichment requests. FastAPI scaffolding with SQLAlchemy ORM and multi-tenant isolation at the query level. Authentication via Clerk or Auth0.
- Day 3: Stripe integration for three pricing tiers. Webhook handler to track subscription state changes and enforce plan limits.
- Day 4-5: Next.js UI for onboarding flow, team setup, API key management. Connect Stripe products to feature gates, where each tier has different capabilities and limits.
- Day 6-7: Apollo API integration. Build the enrichment request queue worker using Celery or Bull to handle batch and real-time lookups with exponential backoff and rate limiting.
- Day 8: Campaign builder interface. Let customers define LinkedIn search criteria, email templates, and which enrichment fields matter most to them.
- Day 9-10: Integrate Resend for cold email delivery and open/click tracking. Build webhook listeners to feed engagement metrics back into the campaign dashboard.
- Day 11-12: Data quality dashboard. Show bounce rates, duplicate detection rates, coverage gaps by industry or company size. Set alert thresholds for risky data drift.
- Day 13-14: Documentation, Loom walkthroughs, and runbook. Deploy frontend to Vercel and backend to Railway or Fly.io.
What's hard about this build
The gravity well is data quality and unit economics. Apollo, Clay, and ZoomInfo have 100M+ records backed by millions in maintenance costs. We'll hit coverage gaps in the first three customer demos. A prospect checks a contact they know is real and finds stale or incomplete enrichment. The retention killer is bounce rates drifting above 15 percent. That forces a choice between more expensive data sources or expensive deduplication logic. There's also the legal surface: even partnered with Apollo, LinkedIn can challenge our outbound practices. Sales teams churn enrichment tools within 3-6 months if quality drifts, which means the real CAC isn't ad spend. It's refund and support burden in month one and two.
What's fast because of AI
AI compresses scaffolding dramatically. Claude generates the FastAPI boilerplate with proper validation schemas, error handling, and multi-tenant middleware in hours instead of days. The Next.js component library, forms, tables, modals, and auth flows move 40 percent faster when Claude writes the initial code. Test writing runs at 4x speed. Edge-case enumeration is where AI saves the most time: duplicate detection strategies, handling vanished records, rate-limit recovery, timezone handling in campaign scheduling. Copywriting for the UI, onboarding tooltips, error messages, and pricing page copy gets drafted in seconds and polished in minutes. Debugging is faster too. Cross-tenant data leaks, ORM isolation bugs, and API integration issues get diagnosed through Claude's eyes on the codebase in a fraction of the time.
How I'd hand it off
I'd record a 20-minute Loom walking through the admin panel, customer onboarding, campaign builder, and the data quality dashboard. You get a runbook in Notion covering the Stripe webhook handlers, Apollo rate limits, Celery worker restarts, and Postgres backup procedures. I'd stay available for 30 days at my hourly rate for critical issues and handoff questions. All credentials including Stripe, Apollo, Clerk, Vercel, and Railway go into a shared 1Password vault. The GitHub repo stays on your account with me as a read-only collaborator. You're ready to operate this independently by day 31.