MealSmash
MealSmash: The AI Chef Revolutionizing How Restaurants Create, Price & Perfect Every Dish
Where Dynamic Menu Engineering & Sentiment Analysis Turn Dining Data into Culinary Gold
🍽️ The Restaurant’s Hidden Crisis
- 📉 Menus waste 32% of ingredients from poor dish forecasting
- 💸 Chefs guess pricing while food costs fluctuate wildly
- 🔇 “5-star” reviews drown meaningful feedback (“Raita too watery”)
- 📉 Trending dishes like “Chicken Tandoori” get buried
✅ MealSmash fixes this with culinary AI — no more guesswork in your kitchen.
⚙️ Food Intelligence Engine: How It Works
1. Dynamic Menu Optimization
(Powered by your menu UI — images 4/5)
- Real-Time Dish Darwinism:
- Promotes top dishes (“Biryani: $30”) → demotes poor performers (“Syrup Filled Ball”)
- Sends alerts like: “Sweet Pudding” → Drop to $25 → 70% sales lift
- Ingredient-Driven Pricing:
- Mutton futures → adjusts “Biryani” price (image 6)
- Dairy shortages → margins hit on “Carved Sweetmeat” (image 4)
2. Sentiment-Driven Recipe Refinement
(Analyzes reviews like “5.0 ★ – 5 Reviews” — image 5)
- Deep Feedback Dissection:
- Spots comments like “Portions too small” even in 4.8-star reviews
- Filters vague feedback (“Good!”) vs. real notes (“Raita lacked mint”)
- Kitchen Alerts:
“12 customers mentioned ‘tough mutton’ in Biryani — adjust cooking time”
3. Hyperlocal Demand Forecasting
(Uses your Madrid location data — images 1/3)
- Neighborhood Flavor Profiling:
- “C. de la Aduana” prefers spicy → boosts “Chicken Tandoori”
- “Las Vistillas Garden” orders desserts → promotes “Sweet Dishes”
- Waste Reduction:
- Forecasts “37% less Biryani orders on rainy days” → cuts excess ingredients
📸 Project Gallery
