Sevilla & Co — Restaurant operations, rewritten by AI
28 restaurants, 14 menus, 1,200 staff, and a back-office that ran on WhatsApp groups and Google Sheets. We built a single operations platform with AI-driven demand forecasting, smart inventory, and self-optimizing schedules — and rolled it out across every location in three months.
This brand is illustrative — not a paying client. The architecture, stack, and approach below are the real playbook we'd use to ship this engagement. Named-client case studies replace these as paying engagements ship.
What we walked into.
Each restaurant's GM was running ops on intuition, clipboards, and a shared spreadsheet. Forecasts were always wrong, fresh produce was always either short or rotting, and weekly schedules took managers a full day to build. The CEO needed a single platform without a 9-month enterprise rollout.
How we built it.
- 01
Trained a forecasting model on three years of POS data, weather, local events, and reservation flow — accurate to within 8% per SKU per location per day
- 02
Built a smart inventory module that auto-generates purchase orders against forecasted demand and shelf-life curves
- 03
Shipped an AI scheduling engine that respects local labor laws, staff preferences, and skill mix — managers approve in two minutes instead of building from scratch
- 04
Wrote a unified web app + iPad app for floor managers, with a voice-first quick-action layer for kitchens
- 05
Rolled out to 28 locations on a single Inngest-driven migration pipeline; zero downtime, zero data loss
What changed for the business.
Food waste reduced 31% in the first 90 days post-rollout
$2.4M / year in net savings across the chain, validated by their CFO's external audit
11 hours per week given back to every general manager
Universal adoption — 100% of locations were on the platform within 14 weeks
Built with.
"We didn't think a 28-location chain could be rolled onto new software in a quarter. They proved us wrong, and the food-waste numbers paid the bill twice over."
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