How I'd build Compliance Radar
I'd reach for Next.js on the frontend, FastAPI with Python on the backend for the rules engine, Postgres for multi-tenant data, and Redis for caching regulatory conflicts. Stripe for billing, Twilio for SMS alerts, and Thomson Reuters plus free regulatory RSS feeds integrated day one. Rough estimate: 520 hours to MVP production, so about three and a half months.
Day-by-day plan
Day 1-2: Auth0 integration, multi-tenant Postgres schema (companies, users, regulations, compliance_events, detected_conflicts). Day 3: Stripe product tiers and subscription webhooks wired end-to-end. Day 4-5: Dashboard UI in Next.js: regulation feed, compliance timeline, alert configuration. Day 6-7: FastAPI backend for parsing Thomson Reuters Regulatory Intelligence API and SEC filings. Day 8: Conflict detection engine that maps overlapping regulations by industry. Day 9-10: Automated email digests via Resend, with industry-specific templating. Day 11: Twilio SMS alerts for high-severity regulatory changes. Day 12-13: Data pipeline QA against healthcare and financial services baselines. Day 14: Deploy to production with Sentry monitoring and Postgres replication. Day 15: Onboarding flow and first 30-day free trial workflows.
What's hard about this build
Data accuracy is existential. Missing one regulation in a prospect's industry kills trust instantly and creates liability exposure we'd own. Thomson Reuters and LexisNexis partnerships can't wait until month four; they need to be locked before launch. The conflict engine is the second complexity: when FDA rules overlap with state-level requirements or international standards apply, the system must surface that without false positives that wreck credibility. I'd build conservatively, test against five years of historical regulatory changes, and keep a compliance consultant on retainer for validation. The third gotcha is freshness: if regulatory agencies change rules mid-cycle and our pipeline doesn't catch it within 24 hours, customers lose trust. Monitoring SEC feeds, agency APIs, and RSS sources in parallel with manual spot-checks across 4-5 high-risk verticals is non-trivial.
What's fast because of AI
Claude compresses the data pipeline and conflict detection logic from a week to two days. Enumerating edge cases (phase-in periods, exemptions, state variations) normally takes a compliance lawyer; Claude scaffolds the logic and I validate against live data. Frontend scaffolding saves 60-80 hours: dashboard layout, forms, table components, all generated and tested in a day. Writing scannable, accurate regulatory digests is normally a copywriter's job; Claude templates the structure and I edit for accuracy. Debugging is faster too: when a customer reports a missed regulation, Claude traces the data pipeline and generates targeted tests that would have caught it. I estimate AI saves 100-120 hours on boilerplate, tests, and edge-case enumeration.
How I'd hand it off
I'll create a Loom walkthrough of the dashboard, billing workflows, and data pipeline operations. You get a runbook covering: adding new regulatory data sources, adjusting conflict rules, handling Stripe webhook failures, and monitoring SMS delivery. I'll run 30-day pager rotation for production issues. Credentials transferred: Auth0, Stripe, Thomson Reuters, Sentry, and Twilio accounts. Slack integration for monitoring alerts stays active the first month.