How I'd build Lockstep
I'd reach for Next.js on the frontend, FastAPI on the backend with Postgres, Stripe for billing, and Twilio for driver alerts and job updates. The scheduling engine would use a cron-based task queue (Celery or Bull) to optimize routes and flag maintenance windows without manual intervention. Rough estimate: 350-400 hours, so about 5-6 weeks at full capacity. The math works: you're looking at roughly $26k-30k in labor to get this to a revenue-ready MVP.
Day-by-day plan
- Day 1-2: Postgres schema for fleets, drivers, trucks, maintenance schedules, jobs. Multi-tenant isolation at the row level. Auth flow with Supabase or JWT.
- Day 3: Stripe integration. Provision three pricing tiers: basic ($79/month for up to 5 drivers), pro ($129/month up to 15 drivers), enterprise (custom). Webhook handling for billing events.
- Day 4-5: Fleet onboarding flow. Bulk CSV import for existing trucks and drivers. Manual data entry fallback. Validation and sanitization.
- Day 6-7: Dashboard MVP. Live fleet status (trucks on road, idle, needs maintenance). Maintenance calendar. Next 7 days of flagged service windows.
- Day 8: Twilio integration. Driver SMS notifications for job assignments, route changes, maintenance reminders. Webhook for SMS responses.
- Day 9-10: Route optimization proof-of-concept. Greedy bin-packing algorithm to minimize deadheading. Assumes manual maintenance and job data for now (ELD integration is phase two).
- Day 11-12: Trial signup flow. 14-day free tier, auto-downgrade to paused account on day 15 unless card on file.
- Day 13: Admin dashboard. Churn tracking, trial-to-paid conversion metrics, manual override levers for customer support.
- Day 14: Loom walkthrough, deployment runbook, on-call handoff.
What's hard about this build
The real complexity is conflict detection. You need to prevent double-booking of drivers, catch maintenance windows that collide with already-assigned jobs, and handle the race condition when a driver cancels mid-route. You also need to decide how aggressively to optimize. Too aggressive and the algorithm pulls drivers off lucrative short hauls to hit utilization targets. Without native ELD integrations (Samsara, Motive, KeepTruckin), you're stuck with manual data entry, which kills the product's AI value prop. Owner-operators won't keystroke maintenance records; they'll spreadsheet it or ignore it entirely. The legal question of liability if your route optimization creates unsafe conditions (fatigue, speed, compliance) requires either strict disclaimers or legal review. Finally, freight market downturns are product killers. A recession hits and your customer's gross margin collapses, they cancel, and you have zero addressable market to sell into.
What's fast because of AI
Claude accelerates scaffolding and edge-case thinking dramatically. I'd use Claude to generate the Postgres schema with all constraints and indexes, then review and fix it. Test case enumeration for the scheduling conflict detector would normally take two days; Claude generates the matrix (double-bookings, overlapping maintenance windows, driver shift violations, ELD compliance checks) in an hour. Copywriting for onboarding prompts and email templates is a Claude pass instead of a three-day slog. Debugging is where AI shines. When the route optimizer produces nonsensical output, Claude quickly enumerates hypotheses (off-by-one indexing, missing timezone handling, weight calculations inverted) and generates targeted logging. That's a full day of hunting compressed into two hours.
How I'd hand it off
I'd ship a recorded Loom walkthrough covering the admin panel, trial signup flow, and Stripe webhook logs. A runbook in Linear documenting the Postgres backup schedule, environment variables, and how to manually override a driver's assignment if Twilio fails. I'd do a 30-day pager rotation where I'm on-call for bugs and customer-blocking issues, with Slack escalation for anything that needs code changes. Database credentials and Stripe API keys transferred to your vault. Deploy process documented: git push main triggers GitHub Actions to run migrations and restart the FastAPI container on Railway.