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Food distribution · Malaysia

AI ordering for a multi-outlet Malaysian food group

A publicly listed Malaysian food and poultry group turns WhatsApp messages, voice notes, and product shorthand into clean orders—without an experienced employee decoding every single one by hand.

Where it stands

Five outlets first. Built for the whole group.

Product mix

Bow ChatBoni AI AgentsOrder Operations

Challenge

Orders don't arrive as orders. They arrive as "the usual, 2 boxes" at 11pm. As a voice note in mixed languages. As a product nickname exactly one person in the company understands. Someone experienced has to decode every one of them—and that person becomes the bottleneck for the whole outlet. The group needed a dependable order record without stopping to build an ordering API first.

Approach

Five outlets went first, each with its own supervisors, customers, catalog, product aliases, units of measure, languages, and exception rules.

Every customer's own order history does the matching, so "the usual" resolves to the right product and the right quantity for that customer—not a generic best guess.

Anything low-confidence, unusual, or expensive stops for a human before it becomes an order. The AI never quietly guesses with a customer's money.

Boni runs the order backend and hands over a daily export the group uses in its own reporting. Nothing new for their team to babysit.

Conversation, order state, review decisions, escalations, and follow-up all live in one place in Bow Chat.

Outcome metrics will be added only after the agreement is signed and the first implementation phase is paid.

What it does today

Order intake runs across the first five outlets—and the group never had to build an ordering API to get there.
Staff confirm drafted orders instead of reconstructing shorthand, voice notes, and repeat orders by hand.
The order model already fits the group's wider outlet network, so the next outlets switch on without a new implementation.

Next Step

Map this pattern to your business.

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