Your Restaurant. Inside WhatsApp.

An AI agent takes the full order in a WhatsApp chat, so the customer never opens an app or a website. Riders are dispatched automatically, customers track their food live, and managers watch every order from one dashboard.

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Restaurant WhatsApp Ordering Agent dashboard

Connected to the tools a delivery kitchen runs on.

WhatsApp Cloud API

Meta Commerce

POS Systems

Google Maps

Rider App

How it works

From first step to result

Step 01
Connect WhatsApp
One step with Meta signup
Step 02
Menu digitised
From a PDF, a photo or your POS
Step 03
Customer orders in chat
Natural language, typos welcome
Step 04
Address and ETA confirmed
Location pin, fee and total
Step 05
Kitchen marks ready
On the dashboard or the POS
Step 06
Rider assigned
Live tracking for the customer

Result
Delivered
Cash collected, customer invited back

Engineered for Delivery Restaurants.

USE CASE A

Ordering in Plain Chat

The problem

Orders arrive as messy WhatsApp messages that staff retype by hand, with typos, changes and missing details. During the rush, messages pile up, replies slow down and customers order elsewhere.

The solution

The AI understands natural chat, typos and edits like 'make it 5', builds the order, takes the location pin and confirms the total and ETA. It handles many chats at once, so no customer waits for a free member of staff.

The outcome

Complete orders taken without anyone typing them. Busy hours without lost orders.

USE CASE B

A Menu Live in a Day

The problem

Menus live in PDFs and photos, and every price change means retyping the whole thing. Out of date menus mean customers order dishes that are gone or pay old prices.

The solution

Upload a PDF or a photo and AI extracts every dish for a manager to approve, then syncs it to the WhatsApp catalog or from your POS. Price changes and sold out dishes are updated once and show wherever the menu appears.

The outcome

A digitised menu in a day, kept in sync. Customers always order from the current menu.

USE CASE C

Delivery on the Clock

The problem

Riders are assigned by hand, and nobody notices a late order until the customer complains. Customers call the restaurant to ask where their food is, tying up staff in the middle of service.

The solution

The nearest rider is assigned automatically with smart batching, customers get a live tracking link, and an SLA monitor raises alerts at 30 and 35 minutes. Managers see every order and rider on one dashboard, so problems are spotted while there is still time to fix them.

The outcome

A late order past 40 minutes gets an automatic apology and coupon. Fewer calls to the restaurant and happier customers.