AI for Restaurants: Orders, Reservations, Reviews
Restaurants lose bookings every Friday night because nobody can pick up the phone mid-service. AI does not cook, but it can answer that phone, and a few other things that quietly cost money.
- Published
Four uses that pay back
- Reservations by phone and WhatsApp: the agent checks table availability and books, even during the rush.
- Order taking for delivery and pickup: structured orders straight into the POS, with upsells the staff forget.
- Review replies: drafts responses to every review for a manager to approve in seconds.
- Prep forecasting: predicts covers and item demand from history, weather and events to cut waste.
The phone problem
Between 7pm and 10pm, most restaurants miss a meaningful share of calls. Each missed call is a table or an order that went elsewhere. A voice agent answers every call, books into your reservation system, and passes anything unusual — large parties, complaints, allergies it cannot confirm — to a manager.
Forecasting to cut waste
With a year or more of POS data, a demand model can forecast item-level sales per day part. Kitchens use it to set prep quantities, and the waste reduction is usually visible within a month. This is a small custom ML project, not an LLM feature.
Where to start
Start with reservations if you take bookings, or order taking if you run delivery. Both have clear success metrics — calls answered, bookings made, average order value — and a voice booking line sits in our $8k–$18k tier.
Frequently asked questions
Does it integrate with our POS?
Most modern POS and reservation systems have APIs. We check yours in discovery.
What about allergies?
The agent never confirms allergen safety itself. It records the requirement and routes the question to staff.
Is it worth it for one outlet?
For a busy single outlet, reservations alone can justify it. For small, quiet outlets, a simpler WhatsApp booking flow is cheaper.
