Import and Distribution

Manufacturer and Distributor of White-Label Beverages for Business Clients (B2B)

Food and Beverage — Manufacturing and Marketing of White-Label Smoothies for Restaurants and Cafés (B2B)

From a "hi" on WhatsApp to a delivery note in Rivhit, automatically — with no triple data entry and no picking errors.

WhatsApp Business API

AI API (AI Agent)

SQL Database

Admin Dashboard

n8n

Rivhit

Challenge

The Challenge

The client manufactures and markets 16 smoothie flavors under a white label for restaurants and cafés, with a turnover of about ₪33,000 a week — run by two people. Every order went through triple manual data entry: from WhatsApp into Excel, and from Excel into Rivhit. This consumed hours a day on repeated entries, produced recurring errors in delivery notes and picking, and delivery scheduling was done by hand according to distribution zones. There was no CRM to hold each customer's fixed product profile, and the products being white-label added confusion in naming — a customer asks for a "pink smoothie" when in the system it's a "halva smoothie." The result: a glass ceiling. It was impossible to grow order volume without adding headcount, and the whole business depended on a single person doing data entry.

Solution

The Solution

Automaziot built a single end-to-end flow that takes an order from the moment of the "hi" on WhatsApp through to a delivery note and invoice in Rivhit — with no double data entry. An AI agent on WhatsApp identifies the customer by phone number, pulls up their fixed product set, and immediately presents the flavors for quantity selection, while translating between the internal names and the names the customers know. The order flows into a management dashboard that replaces the Excel, waits for manual approval with a single click, and is then automatically scheduled into a weekly delivery table by the customer's distribution zone and delivery days — with cutoff logic (an order approved by 12:00 the day before enters that delivery day, otherwise the next day) and a manual override when a gap opens up in a delivery. The entire infrastructure was set up on a cloud server fully owned by the client, with an official WhatsApp Business API connection through Meta — with no dependence on a single vendor, and all assets (server, code, data) remaining theirs.

Our Approach

An AI agent on WhatsApp for taking orders from regular customers — identification by phone number, pulling the fixed product set, managing a short order conversation (flavors ← quantities ← summary), a full menu for ordering an additional product, and translation between internal names and the customer's names

A customer and product database in SQL with a management interface — initial import from Excel, a fixed flavor profile per customer, full editing (add/replace/remove) and assignment to distribution zones and delivery days

A weekly orders and deliveries dashboard that replaces the Excel — automatic intake from WhatsApp, a manual approval screen, a weekly delivery table, scheduling automation with cutoff and manual override

Weekly and monthly business tracking — totals of cartons/units/money and customer-behavior monitoring (who hasn't ordered, where to push)

Automatic connection to Rivhit — creating a delivery note and invoice after order approval, with no double data entry

Setting up a cloud server fully owned by the client + an official WhatsApp Business API connection through Meta + an orderly handover that allows continuing with another vendor in the future

Technologies Used

WhatsApp Business APIAI API (AI Agent)SQL DatabaseAdmin Dashboardn8nRivhitCloud ServerMeta Business Manager

The service behind this project

Automation for Businesses

Results

The Results

Weekly business turnoverapprox. ₪33,000

White-label products/flavors16

Team size before automation2 people

Manual data-entry steps eliminated3 (WhatsApp ← Excel ← Rivhit)

ROI target set in the conversation2–3 months

We replaced a triple manual data-entry process (WhatsApp ← Excel ← Rivhit) with a single end-to-end automated flow. The team is freed from repeated entries, the points of error in delivery notes and picking are addressed at the root, and the business can absorb a larger order volume without adding headcount — breaking the very glass ceiling that tied them to a single person doing data entry. All of this runs on infrastructure fully owned by the client (server, code and data), with no vendor lock-in. The ROI target defined in the conversation was a return within 2–3 months from saved working time; precise monthly savings figures were not recorded in the system, so the result here is qualitative and not numerical.

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