Gaming & Support

High-Volume WhatsApp Support Center (Gaming & Player-to-Player Payment Brokerage)

High-volume customer service — a WhatsApp support center for online games and player-to-player payment brokerage

The client described a 30-person center handling thousands of WhatsApp inquiries a day on an unofficial solution with repeated blocks and severe lag. The delivered project moved the operation to official WhatsApp Business API infrastructure with returning-customer recognition, structured matching and inquiry classification; the starting-volume and latency figures are client-reported, not post-launch measurements.

WhatsApp Business API
Meta Business Manager
n8n
SQL Database
Custom agent interface
Slack
Challenge

The Challenge

According to the client's discovery account, the center handled thousands of WhatsApp inquiries a day with 30 employees across three shifts and manually matched deposit and withdrawal requests in a spreadsheet. The client also reported repeated blocks on an unofficial WhatsApp solution, about five seconds of lag per character and non-clickable phone numbers. These figures describe the client-reported starting point and were not remeasured in the project system. The verifiable implementation need was a move to official WhatsApp Business API infrastructure and a documented operating flow.

Solution

The Solution

An entire support center was built on official WhatsApp Business API infrastructure — two separate systems, an instance per project, connected to Meta Business Manager with separate permissions and load tests that verify it handles thousands of inquiries a day. On top of the infrastructure, five components were built, each tailored to an explicit requirement from the conversation: (1) a short 2-3 step opening menu that summarizes the request and immediately routes it to an agent by inquiry type, with an absolute focus on response speed to get close to a regular WhatsApp experience; (2) returning-customer recognition — on the first inquiry the details are saved, and from the second inquiry the customer skips the bot and goes straight to an agent with all the saved context; (3) a dedicated SQL database into which the data from Excel and the previous system was imported and cleaned, used to match withdrawers to depositors by amount and for fast search under high volume; (4) an agent interface for 10 agents across 3 shifts with label-based filtering, canned responses and clickable phone numbers; (5) an AI layer that identifies in real time whether the inquiry is a withdrawal, a deposit or a technical complaint — tags it accordingly, and sends technical complaints as an automatic alert to Slack. All of this was delivered on a fast-delivery track with close coordination through handover.

Our Approach

Setting up two official WhatsApp Business API systems (a separate instance per project) against Meta Business Manager with separate permissions, including load tests for thousands of inquiries a day
A fast 2-3 step opening menu with automatic request summarization and immediate routing to an agent by inquiry type, optimized for response speed
Returning-customer recognition: saving details on the first inquiry and automatically skipping the bot from the second inquiry, passing the saved context to the agent
Setting up a dedicated SQL database, importing and cleaning the data from Excel and the previous system, and infrastructure to match depositors with withdrawers by amount
An agent interface for 10 agents × 3 shifts with label-based filtering, canned responses and clickable phone numbers
An AI layer for automatic identification of inquiry type (withdrawal / deposit / technical complaint) with automatic tagging and Slack alerts on technical issues
A fast-delivery track with handling priority and close coordination through handover

Technologies Used

WhatsApp Business APIMeta Business Managern8nSQL DatabaseCustom agent interfaceSlackDedicated serverAI API

The service behind this project

AI Agents for Business
Results

The Results

The project was delivered and included migration to official WhatsApp Business API infrastructure, returning-customer recognition, a database for amount-based matching, inquiry classification and technical-team alerts. The blocks, lag and spreadsheet work that motivated the project were reported by the client during discovery. The project data does not contain verified post-launch measurements of response time, volume, block rate, cost savings or conversation length, so none are presented as outcomes.
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