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.

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⚙️ 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

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