Predict tomorrow's rush before the kitchen starts cooking.
A premium AI demo for restaurants and cafes that forecasts demand, recommends ingredient preparation, prevents food waste, and gives owners confident decisions before peak hours.
Increase hot beverages, fried starters, and quick-serve combos. Reduce cold dessert prep after 9 PM to avoid inventory loss.
The expensive problem restaurants cannot see.
Most restaurants prepare food based on guesswork. This demo shows how Trishastik can turn raw sales, weather, events, reservations, and customer behaviour into operational decisions.
Over-preparation
Food is prepared before demand is known, causing wastage and margin loss.
Peak-hour delays
Kitchen teams react late because they do not know which dishes will spike.
Wrong inventory
Owners buy too much of slow-moving items and miss fast-moving stock.
No decision system
Sales reports show what happened yesterday, but not what should be done tomorrow.
Live Demand Forecast Lab
Change the day, weather, reservation load, and event signal. The demo calculates demand, preparation quantity, risk level, and chef-ready stock suggestions.
Total order expectation for the next dinner window.
Preparation is balanced with demand signal.
Confidence based on available operational signals.
Suggested kitchen + floor team count for the rush.
Push hot coffee + crispy starter combo from 6 PM to 9 PM. Keep cold dessert stock limited after peak.
What Trishastik can provide inside this system.
This is not a simple dashboard. It becomes a restaurant research and intelligence layer connected with POS, QR ordering, CRM, inventory, weather, and marketing campaigns.
Demand Prediction AI
Forecast dish-wise sales using historical orders, day patterns, weather, events, and booking load.
Ingredient Prep Planner
Convert predicted dishes into ingredient quantities so kitchen teams know what to prepare.
Rush-Hour Simulation
Identify expected order spikes and recommend staff distribution before the restaurant gets crowded.
Waste Intelligence
Track unsold, expired, and returned items to reduce avoidable food waste and margin leakage.
Profit-Based Menu Signals
Suggest which items to promote based on demand, preparation cost, speed, and profit margin.
Campaign Recommendations
Recommend WhatsApp, Instagram, and in-store offers for slow hours and predicted demand gaps.
How it works in a real restaurant.
A complete flow from research to production-ready restaurant intelligence.
Collect Signals
POS, QR ordering, reservations, inventory, weather, and campaign data.
Analyse Patterns
Find weekly, seasonal, customer, and item-wise demand behaviour.
Forecast Demand
Predict item-wise order quantity for upcoming shifts and branches.
Prepare Smartly
Give chef-ready preparation, purchasing, and staffing recommendations.
Improve Daily
Compare forecast vs actual and make the model more accurate over time.
Turn restaurant guessing into restaurant intelligence.
Use this demo to show cafe and restaurant owners how Trishastik can reduce waste, increase preparation accuracy, and make daily operations smarter.