AI for Restaurants

Cover count and revenue prediction by shift using historical POS data, reservation data, day of week, weather forecasts, local events, and seasonal patterns -- the combination of signals that a good head chef uses intuitively but a trained model applies consistently. Prep quantity recommendations for each day by menu section based on predicted demand, so the kitchen starts service having made the right amount rather than adjusting mid-service. Over-ordering and under-ordering flagged against the forecast at end of service so the variance is visible and the pattern can be addressed. Forecast accuracy tracked over time with the model recalibrated automatically as more data is collected, so accuracy improves the longer the system runs.

Waste pattern analysis by ingredient, menu item, and shift -- identifying which items generate consistent waste, at what point in service, and whether the root cause is overproduction, spoilage, or menu unpopularity. Items with high or increasing waste flagged with the likely root cause so the kitchen team knows whether the response is a prep quantity adjustment, a storage process change, or a menu decision. Prep quantity suggestion by shift based on forecast demand and the current waste pattern for each ingredient, closing the loop between demand forecasting and waste reduction. Food cost impact of waste quantified in the reporting view -- the actual cost in pounds of what was thrown away each week, tracked over time.

Slow-moving item identification by shift and day of week, with a promotion or pricing recommendation where the data supports it -- reducing the item's price on a Monday lunch when demand is consistently low rather than guessing. High-margin, high-popularity item identification for menu positioning decisions -- items that should be more prominent on the menu or on digital ordering channels because they drive both volume and margin. Off-peak period pricing opportunity flagged based on historical demand patterns, so a lower-price offering at a quiet time is proposed when the data shows it would fill covers that would otherwise be empty. Menu mix analysis showing contribution margin against volume for every item on the menu so the picture of which items are earning their place is clear.

AI chatbot on the venue website and Google Business profile that handles reservation availability queries, dietary requirement questions, set menu and events enquiries, and deposit and cancellation policy queries without staff involvement. The chatbot is trained on your menu, your event calendar, your policy documents, and your FAQ content so answers are specific to your venue rather than generic. Booking confirmation and pre-visit reminder automation sent to the customer after a reservation is made. Escalation to the reservations team for requests the chatbot cannot handle -- complex group bookings, specific accessibility requirements -- with the full conversation context passed to the staff member. Unanswered query logging so gaps in the FAQ or menu information that customers are asking about are visible and can be addressed.

Automated sentiment extraction from Google, TripAdvisor, and Yelp reviews, categorised by topic -- food quality, service speed, ambience, value, specific dishes or drinks mentioned -- so the manager sees structured insight rather than a list of reviews to read individually. Sentiment trend by topic over time: service speed ratings declining over three months is a pattern worth acting on; one bad service review is noise. Negative review alert with same-day or next-day notification to the manager so the response window is not missed. Staff and menu insights extracted from recurring sentiment themes across hundreds of reviews -- the kind of pattern that is invisible when reading reviews one at a time but clear when the data is processed at scale.

Item affinity analysis from POS data identifying which dishes and drinks are consistently ordered together across your customer base -- not industry assumptions, but the actual patterns from your transactions. Upsell recommendation at the point of order on kiosk and online ordering: customers who have selected an item see a recommendation based on what guests who ordered that item also ordered, presented at the moment when the decision to add something is easiest. Cross-sell prompts for add-ons with high attachment rates -- sides, sauces, desserts, or drinks that are frequently added to specific mains. Revenue impact of upsell recommendations tracked per channel so the contribution of AI recommendations to average order value is measured against the baseline.