How I'd build Deal Tracker
I'd reach for Next.js on the frontend with Postgres on the backend, Stripe for billing, Resend for transactional emails, and OAuth flows to sync with Salesforce and HubSpot. This is a 350-400 hour build to reach launch: that's 4.5-5.5 weeks at full-time pace, getting you to a 14-day trial-ready product.
Day-by-day plan
- Day 1-2: Multi-tenant auth schema with NextAuth.js. Tenant isolation at the database and API layer. Create initial deal and contact models in Postgres.
- Day 3: Stripe integration across the three pricing tiers. Subscription lifecycle, seat scaling, payment method handling.
- Day 4-5: Build the Salesforce OAuth connector. Sync opportunities and contacts on interval. Handle rate-limiting and partial sync recovery.
- Day 6: Wire HubSpot integration using their API. Same sync pattern as Salesforce. Test conflict resolution when the same lead exists in both systems.
- Day 7-8: Build the deal pipeline UI. Kanban board with drag-drop, quick-edit on cards, bulk actions. Filter by rep, stage, and probability.
- Day 9: Add email notifications. Automated deal alerts for reps when deals update or get assigned. Digest emails for managers.
- Day 10: Onboarding flow. Walk new customers through data mapping, rep roster setup, and first sync validation.
- Day 11-12: Testing, bug fixes, documentation. Runbook for common support issues. Sales demo Loom video.
What's hard about this build
The integration dependency is the real knife's edge. Salesforce and HubSpot sync APIs have rate limits, inconsistent schemas across instances, and nested data structures that break easily. If a customer's Salesforce admin redefines a field in production after we've mapped it, we silently drop that data. I'd build conflict resolution and rollback: any sync failure gets logged and surfaced to the admin, never corrupting their CRM data. The second gotcha is non-adoption churn. If the UI feels like data entry instead of a benefit, reps ghost it at the first friction point. Onboarding has to prove value on day one by showing pipeline insights from imported data, not asking reps to manually rebuild a deal list. Third, handling multi-tenant data privacy at scale when syncing live CRM data requires strict isolation and compliance auditing from day one.
What's fast because of AI
I use Claude to scaffold the Postgres schema and API routes from a spec, saving days of schema design debate. Tests write themselves once APIs are modeled; Claude generates comprehensive test suites for happy paths and error cases that I refine. UI copy for empty states, onboarding tooltips, and error messages come from Claude in one pass, then I audit for brand voice. The sync logic has endless edge cases (deleted records, field mapping changes, rate-limit retries); Claude's edge-case enumeration catches scenarios I'd miss and generates the handling code. Debugging sync failures is where Claude shines: I paste error logs and code, and Claude narrows the cause in seconds instead of 30 minutes of log reading. That's easily a week compressed into two days.
How I'd hand it off
I'd record a 15-minute Loom walkthrough of the admin dashboard, rep view, and integration setup. I'd leave a Linear runbook with the top 20 support scenarios and solutions (data sync failures, rep onboarding, billing edge cases). I'd do a 30-day pager rotation where I'm on-call for critical production issues, response time under one hour. I'd transfer Stripe and CRM OAuth credentials to your AWS Secrets Manager, document the rotation schedule, and walk your ops team through the deployment pipeline once.