How I'd build Auto-AI
I'd reach for Next.js on the frontend with a Postgres backend, Stripe for billing, and Claude's API to generate repair estimates. Clerk for multi-tenant auth, Resend for transactional email, Twilio for SMS notifications to shop owners during outreach. The rough estimate is 250-300 hours to MVP, another 100-150 to get integrations with Tekmetric and Mitchell1 solid enough that shops actually use it without friction. That lands us around the $30k investment if I'm working focused weeks.
Day-by-day plan
- Day 1: Provision Clerk auth, multi-tenant tenant model, and Postgres schema for shops, estimates, users, and audit logs (liability trail).
- Day 2: Wire Stripe customer objects, create three pricing tier products, and build the subscription checkout flow with proration logic.
- Day 3-4: Build the shop onboarding wizard. Shop name, location, estimated monthly vehicle count, integration preference (manual upload or API). Store their configuration.
- Day 4-5: Integrate Claude's API to generate estimate recommendations given a vehicle description, service type, and shop location. Add fallback logic if the API times out.
- Day 5-6: Build the shop dashboard with estimate history, basic analytics (repair type distribution, average estimate time), and a form to rate estimate quality.
- Day 6-7: Create the Tekmetric and Mitchell1 API skeleton. HTTP webhooks to pull work orders, map them to estimate requests, push results back into their systems. Partial; we iterate in production.
- Day 7-8: Security audit for liability exposure. All estimates timestamped, versioned, flagged with confidence scores. Shops understand these are suggestions, not guarantees. Add legal language.
- Week 2: Set up cold outreach infrastructure. Google Maps scraping for repair shops, create Pipedrive CRM integration for demo scheduling, wire Twilio SMS for appointment reminders.
- Week 2-3: Deploy to Vercel, Postgres on Railway, set up Sentry for error tracking and PagerDuty for critical alerts. Staging mirrors production.
What's hard about this build
The liability surface is enormous. If our AI recommends a $400 transmission rebuild and the shop installs it, then the vehicle fails and the owner sues, we're exposed. Mitigating this means building a robust confidence-scoring system, audit trails for every recommendation, and forcing shops to acknowledge these are tools, not replacements for human judgment. The second hard problem is integration. Tekmetric and Mitchell1 have zero incentive to help a competitor. Their APIs are underdocumented and deliberately restrictive. We need to reverse-engineer webhook formats and handle rate limits gracefully. Finally, auto repair shops are notoriously tech-resistant. The UI has to be stupidly simple. If shops see a loading spinner or get an error, they'll abandon it for paper ROs.
What's fast because of AI
Claude scaffolds the entire data model and API routes in one prompt. Manually that's a day; now it's 90 minutes. For testing, Claude generates unit tests for edge cases I'd normally miss: shop with zero prior estimates, API timeouts mid-request, duplicate submission retries. Copywriting for the onboarding flow and landing page comes from Claude in draft form within an hour; manually that's two days of back-and-forth. Debugging is faster because Claude can enumerate what went wrong in a trace log and propose fixes before I've finished reading the error. And for the liability disclaimers and T&Cs, Claude gives me a first draft that legal can refine, instead of starting from a blank page.
How I'd hand it off
I'd record a Loom walkthrough of the admin dashboard, shop onboarding, and estimate workflow. I'd provide a runbook covering deployment, Stripe account setup, Postgres backup procedures, and how to handle Tekmetric API outages. For the first 30 days, I'd be on pager rotation. Any critical production issue (failed estimates, billing errors, integration failures) and I'm back in. I'd transfer Stripe credentials, Clerk tenant config, Twilio API keys, and database access to your operations team. Code is on GitHub, CI/CD via GitHub Actions.