How I'd build Pricing Intelligence
I'd reach for Next.js on the frontend, Postgres for the data layer, FastAPI for the scraping workers, Stripe for billing, and Claude API for pricing analysis and recommendations. I'd seed historical data from public sources and competitor snapshots before touching real-time scraping. Rough estimate: 300-350 hours to production-ready.
Day-by-day plan
Day 1: Stripe account setup, multi-tenant billing schema, define the three pricing tiers ($99/mo base structure).
Day 2: Next.js app scaffold with Clerk auth, database schema for customers and pricing history.
Day 3: Shopify and WooCommerce OAuth integration layer; test with test stores.
Day 4: Ingest historical pricing data from public APIs and partner datasets to build initial credibility and avoid day-one scraping complexity.
Day 5-6: Build the real-time scraper in FastAPI using headless Chromium and IP rotation. Include circuit breakers for rate limits and honeypot detection.
Day 7: Integrate Claude API to generate pricing analysis summaries and competitive recommendations from scraped data.
Day 8: Customer onboarding flow, dashboard UI for price comparisons and alerts, email notifications via Resend.
Day 9: Shopify App Store listing preparation, documentation, and edge-case testing on real customer stores.
Day 10: Final QA, monitoring dashboards, known-issues board in Linear.
What's hard about this build
Real-time price scraping is the core tension. Target sites deploy honeypots, IP rotation detection, and JavaScript rendering requirements that break frequently, forcing constant maintenance. I'd need legal review early because automated price monitoring sits in gray area territory in some jurisdictions. LLM inference costs for analysis will compress margins hard at small scale - you're burning 25-35% of revenue per customer at $99 MRR unless you batch analysis or implement aggressive caching. Data freshness tradeoffs matter too; hourly updates cost more than daily but are table-stakes for premium positioning. Competitors already own years of historical data and established integrations, so differentiation on scraping alone is weak. I'd focus the first 90 days on reliably covering the top 20 product categories rather than breadth.
What's fast because of AI
Claude handles the work that would normally eat 2-3 weeks. I'd use it to scaffold the Next.js component library and API route structure in days instead of weeks, generate test cases for scraper edge cases, write the legal compliance documentation outline for review, and handle dynamic pricing recommendation copy. When a scraper breaks, Claude's good at enumerating why - markup changes, new anti-bot patterns, redirect chains - faster than I'd enumerate manually. Copywriting for the dashboard UI, onboarding emails, and Shopify listing descriptions compresses with Claude to a few hours instead of a few days. The real acceleration is in reducing yak-shaving on boilerplate.
How I'd hand it off
I'd record a 30-minute Loom walkthrough covering the dashboard, customer setup, and scraper configuration. Leave a runbook with the critical maintenance tasks: adding new sites to the scraper, responding to rate-limit incidents, updating pricing tier limits in Stripe. Set up a 30-day pager rotation for scraper monitoring and alert handling - real-time systems need coverage. Transfer all credentials: Stripe account, API keys, Shopify app credentials, GitHub access. Create a Linear board with known issues, maintenance debt, and the data-quality gaps that surface first. We'd do a weekly sync call for the first month, then move to on-demand.