Food demand prediction. Optimize production. Waste reduction.

Predict meal demand

Use AI with historical patterns to forecast portions by day and meal period.

Context-aware accuracy

Integrate weather, holidays, and local events for sharper predictions.

Cut overproduction

Plan right-sized batches to reduce food waste and lower CO₂ emissions.

Track savings

Monitor cost and sustainability savings in real time as menus are produced.

Intuitive dashboards

Chef-friendly views for today’s prep and manager views for trends & goals.

Business & financial purposes

• Revenue tracking: Monitor daily income and compare against targets.
• Profitability analysis: See which menu items bring the highest margins.
• Cash flow planning: Predict when you’ll need more working capital.
• Investment decisions: Use data to justify expansion or marketing spend.

🍽 Menu & Customer Experience

• Menu optimization: Identify best-selling dishes vs. items that rarely sell.
• Dynamic pricing: Adjust prices based on demand (e.g., happy hour discounts).
• Personalized offers: Recommend dishes to repeat customers based on past orders.
• Customer satisfaction: Link sales with feedback to improve service quality.

🛠 Operational Efficiency

• Staff scheduling: Match staffing levels to busy vs. quiet hours.
• Inventory management: Forecast ingredient usage to reduce waste.
• Supply chain planning: Order stock based on predicted demand.
• Queue management: Track wait times to improve service speed.

📈 Strategic Growth

• Market insights: Compare performance across locations or regions.
• Seasonality detection: Spot patterns (weekends, holidays, weather effects).
• Expansion planning: Use data to decide where to open the next branch.
• Benchmarking: Compare against industry averages or competitors.

🤖 Machine Learning & Advanced Analytics

• Sales forecasting: Predict future demand using time series models.
• Recommendation systems: Suggest dishes or combos to customers.
• Churn prediction: Identify customers who stop visiting and re-engage them.
• Anomaly detection: Spot unusual sales drops or inventory issues quickly.

💳 Sales Transactions

• Date & time: Timestamp of purchase.
• Items ordered: Menu item IDs, categories.
• Quantity & price: Volume and revenue per item.
• Payment method: Cash, card, online.

👥 Customer Information

• Group size: Number of customers per table/order.
• Contact info: Email/phone (optional for loyalty).
• Demographics: Age group, gender (only if ethically collected).

🛠 Operational Data

• Staffing: Shifts and hours worked.
• Inventory: Ingredients consumed, stock levels.
• Wait times: Order placed → food served.

🌦 Environmental Data

• Weather: Temperature, rain (affects sales).
• Events: Holidays, sports games, local festivals.

🖥 Point of Sale (POS)

Most restaurants use POS software that automatically logs transactions.

Examples: Square, Toast, Lightspeed.

📝 Manual Logs

Use spreadsheets to record daily sales, customer counts, and inventory.

Simple but effective for small setups.

📱 Customer Feedback

Paper forms, QR code surveys, or apps to collect satisfaction ratings.

📡 IoT / Sensors (Advanced)

Smart counters for foot traffic or kitchen sensors for inventory tracking.

🧠 Human Expertise

Solutions built by professional Data Scientists & ML Engineers — not just black-box tools.

✅ Practical AI & Applied ML

We focus on measurable business outcomes and clear ROI — not hype.

🤝 Collaborative Partnership

We co-create with your team and transfer knowledge for long-term success.

⚖️ Ethical & GDPR-Aligned

Privacy by design, transparency, and robust governance from day one.