Restaurant management SaaS with AI menu optimization and automated supplier ordering. 6 weeks from spec to live customers. Food cost down 18% in the first quarter.
Industry
Food & Hospitality
Services
SaaS Dev, AI Agents
Timeline
6 weeks to launch
Key Result
18% food cost reduction
The ChefAssist founders were restaurant operators who'd experienced the same problems at every venue they'd run: menu pricing disconnected from actual ingredient costs, waste from ordering too much of the wrong ingredients, and managers spending hours on ordering decisions that should be data-driven. They wanted to build the tool they'd always wished existed.
The MVP needed to handle three things: menu engineering (profitability analysis by dish), inventory tracking with AI demand forecasting, and automated supplier ordering triggered by predicted stock depletion. They wanted to pilot with five of their own venues before opening to external customers.
A full-stack SaaS platform with three core modules. Menu Engineering: ingredient-level cost breakdown per dish, contribution margin analysis, AI-powered pricing recommendations based on food cost targets. Inventory Intelligence: real-time stock levels, consumption tracking by dish sold, AI forecast of stock depletion by ingredient. Automated Ordering: when forecasted stock drops below threshold, the system generates a supplier order and sends it for manager approval — one click to confirm or adjust.
The AI demand forecasting integrates with their POS systems to pull daily sales data. The model accounts for day-of-week patterns, seasonality, and upcoming reservations to predict ingredient consumption with enough lead time for supplier orders.
Launched to five pilot venues at end of week six. In the first quarter: food cost reduced 18% on average across pilot venues, weekly ordering time reduced from 4 hours to 35 minutes per venue, waste down 24%. The founders opened external signups three months after launch. Now operating with 43 venue customers.
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