How I'd build Restaurant AI
I'd reach for Next.js on the frontend, FastAPI on the backend with Python, Postgres for data, Clerk for multi-tenant auth, Stripe for billing, and Twilio for SMS notifications to owners. On the integration side, I'm starting with Square and Toast APIs - Clover comes later if we validate PMF. Rough estimate: 400-450 hours to production, so we're looking at five and a half to six weeks at full capacity. That matches the 33k rough budget. Not a weekend project, not a six-month slog.
Day-by-day plan
Day 1-2: Scaffold the monorepo. Set up Next.js with TypeScript, FastAPI skeleton, Postgres schema for tenants, users, menus, and analytics events. Clerk integration for multi-tenant routing. Deploy boilerplate to Vercel and Railway.
Day 3-4: Auth and tenant isolation. Complete Clerk setup, tenant middleware, RBAC for owner vs. distributor roles. Verify we can spin up new tenant datastores on signup without cross-contamination.
Day 5-6: Stripe integration. Build the pricing tiers (basic, pro, enterprise), subscription webhooks, seat-based metering, and dunning flows. Wire the billing portal into the dashboard. Invoice schema and email triggers via Resend.
Day 7-8: POS connector scaffold. Square SDK integration first - OAuth flow, menu fetch, transaction streaming. Menu schema to normalize Square's oddly-structured item and modifier data. Unit tests for the connector abstraction so Toast can slot in without touching the core.
Day 9-10: Onboarding flow. Owner signup, restaurant details form, POS selection and connection, initial menu import. Show the owner a live dashboard of their last 7 days of sales by item. This is the aha moment.
Day 11-12: AI backbone. Prompt chain for menu optimization: pull sales data, compare item margins and velocity, surface the top 5 underperformers with suggested price increases or deletions. Use GPT-4 for the explanations. Wire cost-per-item from the POS if available, fallback to industry averages.
Day 13-14: Scheduling assistant. Staff scheduling template, shift request board, Twilio SMS for shift confirmations and last-minute coverage. Basic constraint solving: no double-shifts, respect posted availability.
Day 15-16: Notifications and observability. Real-time alerts to owners for anomalies - a menu item suddenly trending, low-inventory warnings, labor cost spikes. Datadog or Axiom for logs and traces. Error budget dashboarding.
Day 17-18: Distributor marketplace (optional MVP). Simple two-sided marketplace so owners can see recommended vendors based on their spending patterns. Doesn't need transactional payment upfront - just referral links to negotiate offline.
Day 19-20: Security and compliance audit. PCI-DSS scope review with counsel (POS data handling), SOC 2 checklist, password policies, rate limiting, API keys. Fixes based on findings.
Day 21-22: Load testing and polish. Spike 1000 concurrent owners on the dashboard, verify Postgres and FastAPI hold. UI/UX tweaks based on internal testing. Prepare demo video.
What's hard about this build
POS integrations are the moat and the landmine. Square, Toast, and Clover all have different rate limits, auth expiration patterns, and data schemas. Toast's webhooks drop events if you're slow to ack them. Square's menu API doesn't always reflect inventory in real time, so owners see stale data. You need per-platform retry logic, idempotency keys, and a solid connector abstraction or you're rewriting the same logic three times. Regulatory risk is real too: if you're storing transaction data, PCI compliance gets messy. You need a compliance lawyer review before launch, not after. The other trap is use-case clarity. "AI for scheduling and menu optimization" is clear. "AI co-pilot for your restaurant" is vapor. I'd scope the MVP to menu pricing and staff scheduling, launch there, expand once we have retention data.
What's fast because of AI
Claude accelerates the test suite - I'd generate unit and integration tests for each connector, then spot-check them. Saves a week of manual test writing. Copywriting for the dashboard UI and email templates compresses a day of writing into an hour. When I hit a bug in the POS data normalization, Claude quickly enumerates edge cases I'd miss: null vs. empty string modifiers, decimal precision, timezone offsets in timestamps. Scaffolding the FastAPI routes and Postgres migrations is 3x faster with code generation. The biggest win is the onboarding flow - building a delightful wizard that walks an owner from signup to first actionable insight, Claude helps me enumerate branching logic and error states that would take two days to think through manually.
How I'd hand it off
I'd record a Loom walkthrough of the entire product - how to onboard a restaurant, what each dashboard widget does, how to configure pricing tiers. Leave a runbook in Markdown covering deployment, emergency Postgres recovery, Stripe webhook debugging, and how to add a new POS connector. You or whoever ops this next gets 30 days of paid pager rotation from me: I'm on-call for critical bugs, integration failures, or billing issues. I'll transfer all API keys, Stripe account access, and GitHub organization ownership. Slack channel for async escalations.