RestaurantSIM — The first synthetic restaurant universe.
Powered by CulinaryOS.
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The Structural Barrier Blocking Restaurant Innovation
The Catch-22 Problem
Restaurant tech companies face an impossible paradox. You cannot train forecasting, inventory, or scheduling models without meaningful historical data. But restaurants won't share sensitive sales, guest, and labor data with unproven products. This creates a deadlock that stifles innovation across the entire hospitality technology sector.
RestaurantSIM breaks this cycle by generating complete synthetic operational datasets that mirror real restaurant behavior—eliminating the largest barrier to building restaurant technology products.
No Data Access
Restaurants protect operational information
Cannot Build Product
Models require training data
Cannot Prove Value
Unproven products get no access
“Can’t ChatGPT just make this dataset for me?”
No — it can’t.
LLMs generate fiction. Restaurants run on physics.
If your forecasting model trains on AI hallucinations, it will fail the moment it touches a real kitchen.
RestaurantSIM generates the only synthetic restaurant universe that follows real operational math: covers → demand → usage → orders → labor → seasonality → chaos.
If you want pretty numbers, ask a chatbot.
If you want models that survive real service on a Saturday night, you need RestaurantSIM.
What RestaurantSIM Generates
Synthetic data that behaves like real restaurants. Focused first on the data teams actually need to train models and ship product.
Front-of-House Operations
  • Daily covers by service
  • Reservations, walk-ins, waitlists, no-shows
  • Party sizes and simple pacing curves
  • Basic check averages by service and location
  • Realistic weekend, weekday and seasonal patterns
Back-of-House Systems
  • Ingredient usage for high-waste and high-volume items
  • Simple inventory on-hand and daily depletion
  • Par levels for key products
  • Vendor orders with realistic case sizes and costs
  • Invoice lines that look like real Sysco, US Foods or Shamrock invoices
Financial & Trend Analytics
  • Daily and weekly sales by restaurant and service
  • Demand curves by day of week and season
  • Weather and event adjustments
  • Basic price and check average scenarios
  • Enough history to train forecasting and timeseries models
How RestaurantSIM Generates Synthetic Reality
01
Learn from Real Operations
Trains on real multi-unit restaurant history. Covers, sales, reservations, weather and high-waste ingredient behavior.
02
Build Unique Restaurants
Creates unique restaurant profiles across major US cities. Each gets its own volume tier, price index, service mix and weather sensitivity.
03
Simulate Daily Operations
Simulates day-to-day operations. Covers, reservations, walk-ins, no-shows, checks, key ingredient usage, inventory movement and vendor orders.
04
Export Structured Data
Outputs clean, structured datasets. CSV or JSON ready for model training, backtesting and product prototyping.
A Synthetic Universe Grounded in Reality
RestaurantSIM can simulate hundreds of restaurants across major US markets. Each one has its own service mix, volume tier, price level, reservation culture and weekend behavior. New York feels different from Denver. Miami feels different from Chicago. All of them still follow the same operational laws learned from real data.
Each synthetic restaurant includes location-specific weather models, event frequency patterns, market-based pricing structures, and distinct service emphasis—whether lunch-focused, brunch-heavy, dinner-centric, or late-night operations. Walk-in versus reservation culture varies by market authenticity.
500+
Virtual Restaurants
Across major U.S. markets
All
Major US Cities
Regional operational diversity
100%
Accurate
Real behavioral patterns
Traditional Approach vs RestaurantSIM
Built on Real Restaurant Patterns
RestaurantSIM learns behavioral patterns from authentic multi-unit restaurant data.
See RestaurantSIM in Action
RestaurantSIM generates high-fidelity synthetic data, enabling robust model training and product development. Below is a preview of the structured data output alongside key insights derived from the simulated restaurant universe.
Sample Synthetic Data Output
[
  {
    "date": "2023-10-26",
    "location_id": "NYC001",
    "service": "dinner",
    "covers": 125,
    "reservations": 78,
    "walk_ins": 47,
    "sales": 5250.75,
    "weather": "rainy",
    "ingredient_usage": {
      "salmon_fillet": 18,
      "avocado": 30,
      "filet_mignon": 22
    }
  },
  {
    "date": "2023-10-27",
    "location_id": "NYC001",
    "service": "lunch",
    "covers": 80,
    "reservations": 35,
    "walk_ins": 45,
    "sales": 2800.50,
    "weather": "clear",
    "ingredient_usage": {
      "chicken_breast": 15,
      "lettuce": 25
    }
  }
]
This structured JSON/CSV data captures the daily pulse of a simulated restaurant, ready for direct ingestion into your models.
Key Generated Insights
  • 2 years of operational history per restaurant
  • Daily cover predictions for optimized staffing
  • Invoice tracking for consumption patterns
  • Weather-adjusted demand curves for accurate forecasting
Gain actionable intelligence from the detailed and realistic data generated for every synthetic restaurant.
Built for Data Scientists, Product Teams & Engineers
Data Scientists & ML Teams
Train forecasting and demand models. Build synthetic pre-training pipelines. Stress test timeseries algorithms. Model inventory waste prediction and vendor optimization without real client data exposure.
Product Managers
Prototype features rapidly. Test UX with realistic scenarios. Simulate "what-if" questions like menu additions or price changes. Create compelling demos before securing real customers.
Restaurant Groups
Model expansion feasibility. Experiment with menu and pricing strategies. Simulate labor and prep requirements. Understand location-specific volatility before committing capital.
Why RestaurantSIM Is Different
Real Behavioral Intelligence
Based on authentic operational patterns, not random number generation or simplified assumptions
Complete Lifecycle Coverage
Models the full FOH lifecycle and the most important parts of BOH to start, with deeper coverage expanding over time
Context-Aware Simulation
Incorporates weather, events, holidays, and regional characteristics into demand modeling
Ingredient-Level Precision
Tracks individual ingredient depletion, spoilage, and ordering with realistic vendor dynamics
Infinite Scalability
Generate hundreds or thousands of virtual restaurants with unique operational personalities
AI-Ready Architecture
Purpose-built to power forecasting systems, analytics platforms, and machine learning pipelines
Powered by CulinaryOS Intelligence
A Complete Operational Ecosystem
RestaurantSIM is built on the same AI intelligence framework behind CulinaryOS—a platform designed to predict covers, ingredient usage, prep needs, and order quantities for real restaurant operations.
Together, they form a closed-loop system: RestaurantSIM generates realistic synthetic history, while CulinaryOS learns from it, forecasts on it, and operationalizes it. This enables teams to simulate future scenarios and optimize real-world restaurant performance.
RestaurantSIM
Generates synthetic operational data universe
CulinaryOS
Learns, forecasts, and operationalizes intelligence
Real Operations
Optimized performance in production
Pricing
Perfect for solo developers, AI hobbyists, and small teams validating ideas.
Includes:
✔ 10 synthetic restaurants
✔ Covers (2 years)
✔ Invoices (2 years)
✔ Inventory snapshots
✔ Basic metadata
✔ Download in CSV/Parquet
Get Starter →
Standard — $149/month
Best for early-stage startups building forecasting, ordering, or menu tools.
Includes:
✔ 50 synthetic restaurants
✔ Multi-region behavior
✔ Holiday + seasonality modifiers
✔ Ingredient usage patterns
✔ Chef/GM behavioral randomness
✔ CSV + Parquet exports
Get Standard →
Pro — $299/month
For teams building production-grade ML, simulations, or forecasting engines.
Includes:
✔ 200 synthetic restaurants
✔ Multi-unit chains w/ parent-child behavior
✔ Weather alignment
✔ Demand shocks + stress-testing
✔ Unlimited re-downloads
✔ CSV + Parquet exports
Get Pro →
What is CulinaryOS?
NOTE: CulinaryOS is still in live testing phase.
CulinaryOS is the brain for restaurant operations — a system that understands how a kitchen behaves in real life: the flow of covers, the rhythm of prep, the scaling of ingredients, the chaos of daily demand.
Every night it generates the four predictions that drive tomorrow’s service:
  • guest volume
  • ingredient usage
  • prep requirements
  • vendor orders
But CulinaryOS isn’t just learning from live restaurants.
How it fuels RestaurantSIM
CulinaryOS also acts as the synthetic engine inside RestaurantSIM — the system that gives each simulated restaurant its operational logic.
In the synthetic universe, RestaurantSIM generates hundreds of virtual restaurants; CulinaryOS predicts how those restaurants will behave tomorrow.
Then RestaurantSIM feeds back the outcome — demand changes, usage spikes, weather shifts — and CulinaryOS learns faster than any real kitchen could ever allow.
This creates a feedback loop where:
  • RestaurantSIM teaches CulinaryOS how restaurants behave at scale
  • CulinaryOS drives RestaurantSIM with realistic forecasts and decisions
It’s the first self-improving ecosystem for restaurant AI.